Application of theSCOR Model in

Supply ChainManagement

Rolf G. Poluha

Amherst, New York

Copyright 2007 Rolf G. Poluha

All rights reserved Printed in the United States of America No part of this publication may be reproduced, stored in orintroduced intoa retrieval system, or transmitted, in anyform, or by any means (electronic,mechanical,photocopying, recording, or otherwise), without thepriorpermission of the publisher. Requests for permissionshould be directed topermissions@cambriapress.com, ormailed to Permissions, Cambria Press, PO Box 350,Youngstown, New York 14174-0350.

This book has been registered with the Library of Congress.

Poluha, Rolf G. Application of the SCOR model in supply chainmanagement / Rolf G. Poluha. p. cm. Includes bibliographical references and index. ISBN 978-1-934043-23-3 (alk. paper) 1. Business logistics—Management.I. Title.

HD38.5.P65 2007 658.7—dc222007012289

For my wife, Sandra, mychildren, Kim, Dion and

Tia,and my parents,Edeltraud and Alfred

IV. Foreword Dr. Poluha presents himself with thecomplex task of examining the mostcommonly known reference model forthe Supply Chains of organizations,namely the SCOR model (Supply ChainsOperations Reference Model) by theSupply-Chain Council (SCC), within theframework of an empirical examinationof its value for Supply Chain analyses inand for the purpose of practicalapplications.

In recent years, the SCOR modelhas achieved ever-growing importance,most importantly in the North Americanfield of business, but also increasingly in

Asia and Europe. The origins and aimsof the model are just as comprehensivelydiscussed as its strengths andweaknesses. In addition to this,impressive examples of application frombusiness practices are also represented.It is surprising that hardly any scientificstudies are available with reference tothe model and its application. In actualfact, its reference to realization and itsefficiency are simply taken to be a givenquantity.

Dr. Poluha’s work wishes toaccomplish an exploratory contributionto the scientific examination of themodel. For this purpose, roughly 80empirically gained sets of data from

companies in Europe, North Americaand Asia are evaluated and interpretedby means of statistics. The analysis isperformed by means of specialperformance indicators, which form abasis for the structure of the model, andare discussed in detail.

During the statistical evaluation, themethod of procedure orientates itselfupon a sequence, which is logicallyproduced and incorporates statistically-descriptive descriptions, inferencial-statistical evaluations, interpretativeattempts at the explanation of non-confirmed results, as well ascomprehensive thoughts upon anaggregated level. In addition to this, an

attempt is made to submit the model toexamination by means of a procedure forstructural analysis.

A concrete example of a theory-based empirical research project issuggested as a topic of possible andsubsequent research. Hypothesesrepresent the basis for deliberations,which are founded upon model-specificperformance indicators and are deducedfrom a distinctly and clearly organizeddepiction of the SCOR model.

Conclusions to the model aredrawn and potentials for improvementare extricated by the comparison ofwork-theses and results of the empiricalexamination. Innovative initiatives for

the configuration and possibilities forutilization of the SCOR model arepresented and consequently discussed.The restrictions of the presentlyavailable SCOR model are elaborated,wherein a central role is played by themissing dimensions of the configurationof organization and human resources.

The work offers an exploratory andinterim result towards the scientificresearch into the SCOR model and itsapplication. The author has benefitedfrom his extensive experience in theconsultation practice, and his ability tocall upon the use of relevant data andmaterial. Continuous studies can expandupon the results and are urgently needed

in order to scrutinize and extend theaccumulated knowledge, as well as tothrow light upon any outcome of thefindings that may appear inconclusive. Inthis manner, success can be achieved ingenerating incentives for themaintenance and further development ofthe SCOR model.

Prof. Dr. Dietrich SeibtUniversity of Cologne, Cologne,

Germany, April 2007

V. Preface This book is designed to provide anoverview of the SCOR model in itspresent form, as well as the operationalpossibilities for analysis andmeasurement of the performancepotential of Supply Chains. Subsequentto this, the examination design and theresults of an empirical study areintroduced, which were designed tomake the structure of the SCOR modeloperational, and to subject it to a testwith regards to its solidity andproximity to truth.2

In roughly the last ten years, themeaning of logistic processes in

companies has strongly increased.Whilst before, logistics stillpredominantly represented a verticalcompany function, the functionalencroachment and integrated view of asupply chain have stepped into theforeground. This reflects itself, forexample, in the creation of a newpolitical economic discipline, SupplyChain Management, and the increasinganchoring of this discipline withincompanies.

The work at hand moves the SupplyChain into the focal point. For thepurpose of structuring the related SupplyChain Processes, the so-called SupplyChain Operations Reference Model

(SCOR) model is utilized and thoroughlyreflected upon with regards to thepossibilities pertaining to its explanationand description. The selected researchgoals can be summarized as follows:

According to investigationconducted by the author, noacademic studies have as yet beenperformed to specifically analyzethe SCOR model structure;The SCOR model structure ispresumed to be correct and themodel is being used for applicationin projects, or for subsequentstudies;The model is increasingly“popular” and used in practice,

primarily in North America andAsia, with Europe still laggingbehind;There is an assumption of“correctness” simply because it isapplied. However, there is noobjective external assessment forthe validity of the model or itsinternal measures;This leaves the model’s user atsome risk that despite wide use, themodel itself may be, at leastpartially, incorrect.

Due to the represented situation, the

book tries to find answers to thefollowing research questions (scientific

motivation):

How could the consistency, i.e.,assumed alignment of the model’sperformance measurements, betested?How could the SCOR model bemade operational for statisticalanalysis?What would be the implications of“inconsistency” (i.e., lack ofassumed alignment of the model’sperformance measurements)?Is “inconsistency” a SCORproblem, or one that it inherits andbrings into clearer view due to itsframework?

The author by no means claims to

perform a generally valid and finalexamination of the SCOR model. It isobvious that such a goal would gobeyond the scope of a single and initialresearch effort, if it would be possible atall. The intention is rather to perform anexploratory contribution to research, anexploratory attempt to gain initial andconsequently provisional resultsconcerning the questions and researchgoals listed above, and furthermore, toinitiate and support subsequent researchthat can build upon those results.

VI. Acknowledgements The work at hand has been created in thecontext of an in-service dissertationproject over a period of time, primarilybetween 2001 and 2005, at theDepartment of Information Systems andInformation Management at theUniversity of Cologne, Germany by Prof.Dr. Dietrich Seibt, who is presentlyleading the Research Department forInformation Systems and LearningProcesses. Since then, the content hasbeen continuously updated andcomprises the status quo of practicalknowledge and academic research as atthe beginning of 2007.

As the author, I have been workingas a consultant and project manager withBearing Point (formerly KPMGConsulting) and SAP AG on projects inEurope and the United States during thecreation of this work. In the course ofthese projects, I had ample opportunityto acquire intensive and extendedinsights into the topics of Supply ChainManagement and SCOR. The resultingexperiences are reflected in the work inmany ways.

Special thanks for the initiation,development and execution of the workare due to my doctoral advisor, Prof. Dr.Dietrich Seibt. He literally took andfulfilled the role and acted as a real

advisor. In this sense, he always gaveme the appropriate and necessaryimpulses at the right time with hissuggestions, encouragement andconstructive criticism.

The second opinion was kindlyadopted by Prof. Dr. Detlef Schoder,director of the Seminar for InformationSystems and Information Management atthe University of Cologne. The chair ofthe debate was Prof. Dr. UlrichThonemann, director of the Seminar forSupply Chain Management andManagement Science at the University ofCologne.

Further thanks go to Prof. RichardWelke, director of the Center for

Process Innovation at J. Mack RobinsonCollege of Business at the Georgia StateUniversity. Based on his experienceswith normative models in general, andthe SCOR model in particular, heprovided me with helpful references andsuggestions, which have beenincorporated into the work.

I would also like to thank thelibraries of the University of Stuttgart,Germany, North Carolina StateUniversity, Georgia Institute ofTechnology, and Georgia StateUniversity. They have granted megenerous access to their archives, whichallowed me to consider a broad range ofliterature from authors from both the

United States and Europe. Furthermore,Mr. Derek Hardy for his outstandingsupport in translating this work fromGerman into English language. Andfinally, Dr. Paul Richardson andMs.Toni Tan of Cambria Press and theirteam for the excellent support inpublishing this book.

As the motto for the book, I wouldlike to use the guiding theme of ascientist and researcher, Karl R. Popper,whose work and insights haveaccompanied and inspired me over theyears:

Not from the beginning did theGods reveal everything to theMortals. But over time they

will find, seeking, the Better.3

In this sense, I wish the seekingreader that this work will help to gainsome new and interesting insights. And Ihope that it can initiate and contribute toextend existing knowledge and expertise.

Dr. Rolf G. PoluhaAtlanta, Georgia, April 2007

VII. Abbreviations

3PL Third-Party Logistics ServiceProvider

α-error Alpha Error (Type I Error)β-error Beta-Error (Type II Error)abbrev abbreviatedABC Activity-based costing

AG Aktiengesellschaft (PublicCompany)

AGFI Adjusted-Goodness-of-Fit-Indexapprox. approximatelyASC Adaptive Supply ChainAMOS Analysis of Moment Structuresam. above-mentioned

AMR Advanced ManufacturingResearch

AP Asia PacificAPS Advanced Planning Systems

ARIS Architecture of integratedInformation Systems

bm. below-mentionedbn BillionB2B Business-to-BusinessB2C Business-to-ConsumerBC before Christ

BIAIT Business Information Analysisand Integration Technique

BICS Business Information ControlStudy

BKM Best Known MethodBP Business ProcessBPA Business Process Analysis

BPC Bravais-Pearson CorrelationCoefficient

BPM Business Process ManagementBPO Business Process OptimizationBPR Business Process ReengineeringBRE Business Rules Engine

BSCol Balanced ScorecardCollaborative

BSC Balanced Scorecardca. circaCCC Customer-Chain Council

Customer-Chain Operations

CCOR Reference ModelCEO Chief Executive OfficerCFO Chief Financial OfficerChap. ChapterCLM Council of Logistics ManagementCo. CompanyCOGS Cost of goods soldCol. ColumnCorp. Corporationcp. compareCPA Certified Public Accountant

CPFR Collaborative Planning,Forecasting and Replenishment

CPG Consumer Packaged Goods

CRM Customer Relationship

ManagementCSCO Chief Supply Chain OfficerCSF Critical Success Factor

DBW Die Betriebswirtschaft (TheBusiness Studies)

DCC Design-Chain Council

DCOR Design-Chain OperationsReference Model

Desc. Descriptiondf degree of freedomDiag. DiagramDiags. DiagramsDola Day of last accessDoD US Department of DefenseDOS Days on stock

DP Data processingDLA US Defense Logistics AgencyE-Business Electronic BusinessE-Commerce Electronic CommerceE-CRM

Electronic Customer RelationshipManagement

E-SCM

Electronic Supply ChainManagement

EBN Electronic Buyers Newsed. editorEDI Electronic Data InterchangeEMEA Europe, Middle East and AfricaERP Enterprise Resource Planninge.g. exempli gratia (for example)et al. et alii (and others)

etc. et ceteraEUR Eurof degree of freedomFG Finished goodsFig. Figure

firForschungsinstitut fuerRationalisierung (Research Institutefor Rationalization)

FTE Full-Time-EquivalentFTF Face-to-faceG2B Government-to-BusinessG2C Government-to-ConsumerG2G Government-to-GovernmentGFI Goodness-of-fit-Index

Gesellschaft mit beschränkter

GmbH Haftung (Limited LiabilityCompany)

GPF Geographic Product Flow

HGB Handelsgesetzbuch (GermanCommercial Code)

HP Hewlett-PackardHRM Human Resources ManagementHW Hardware

HWBHandwörterbuch derBetriebswirtschaft (Dictionary ofBusiness Studies)

i.e. id est (that is)ibid. ibidem (at the same place)

ICT Information and CommunicationTechnology

I-C Intra-Competency

I-CP

Inter-Competency/PerformanceAttribute

I-P Intra-Performance AttributeInc. Incorporated

ISCM Integrated Supply ChainManagement Program

iSNG Intel Supply Network GroupIS Information SystemIT Information Technology

KIM Kölner Integrationsmodell(Cologne Integration Model)

KPI Key Performance IndicatorKRP KostenrechnungspraxisLA Latin AmericaLISREL Linear Structural Relationships

LLC Limited Liability CompanyLtd. Limited CompanymMillionM SCOR Model groupMax MaximumMCC Micro Compact Car AGMES Manufacturing Execution SystemsMin MinimumMIS Management Information System

MIT Massachusetts Institute ofTechnology

MPS Master Production Schedule

MRO Maintenance, Repair andOperating Equipment

n number (as part)

N number (total)NA North America

NDST Network Decision SupportTechnology

No. NumberNos. Numbersnon-signif. non-significantNPV Net Present Valueny no year specifiedNYSE New York Stock ExchangeODM Original Design ManufacturerOEM Original Equipment Manufacturer

OSD US Office of the Secretary ofDefense

p. page

pp. pagesP ProbabilityPara. ParagraphParas. ParagraphsP(α) Probability of ErrorPC Personal ComputerPM-Correlation

Product-Moment-Correlation

PMG Performance Measurement Group

PRTM Pittiglio, Rabin, Todd &McGrath

q.v. quod vide (which see)

R Bravais Pearson CorrelationCoefficient

R&D Research and Development

re. regardingROA Return on AssetsROE Return on EquityROI Return on InvestmentROS Return on Saless Standard deviationSA Société AnonymSAS Société par actions simplifiéeSC Supply ChainSCC Supply-Chain Council

SCDM Supply Chain DesignManagement

SCM Supply Chain Management

SCOR Supply Chain OperationsReference Model

SCORcard SCOR-based Supply ChainScorecard

Sect. SectionSEM Structural Equation ModelSFA Sales Force Automation[sic!] just as that (“same in copy”)SIG Special Interest Groupsignif. significant

SIMTech Singapore Institute ofManufacturing Technology

SKU Stock Keeping UnitSN Supply NetworkSNG Supply Network Group

SPSS Statistical Package for SocialSciences ?(former desc.)

SPSS Statistical Product and ServiceSolutions (current desc.)

SRM Supplier RelationshipManagement

SV Shareholder ValueSW SoftwareTbl. Table

TCP/IP Transmission ControlProtocol/Internet Protocol

TQM Total Quality ManagementUK United KingdomUoM Unit of MeasureUS United StatesUSA United States of AmericaUSD US-Dollar

V Variation rangeVAR Value-added resellerVC Value ChainVCG Value Chain Group

VCOR Value Chain OperationsReference Model

Ver. VersionVMI Vendor Managed InventoryVol. Volumevs. versusWFM Workflow ManagementWIP Work in process

WiStWirtschaftswissenschaftlichesStudium (Studies of EconomicScience)

X Arithmetic mean

ZfB Zeitschrift für Betriebswirtschaft(Journal of Business Studies)

ZfbfZeitschrift fürbetriebswirtschaftliche Forschung(Journal of Business Research)

1. Chosen conventions with regards to the structure:–On the topmost level are the chapters (abbrev. as

chap.):Example: Chapter A (represented in bold and large

writing).–Beneath these follow the respective sections (abbrev.

as sect.):Example: 1.1 (represented in bold and normal-sized

writing).–Allocated to a section, there may be paragraphs

(abbrev. as para.):Example: 1.1.1 (represented in bold, normal-sized

writing and italics). If need be, there are at leasttwo paragraphs allocated to one section.

–Sub-paragraphs may follow beneath a paragraph:

Example: 1.1.1.1 (represented in bold, normal sizedwriting, italics and indented). This levelrepresents the lowest possible level of structure,referred to, analogue to the previous level andalso simplifi ed with the term para., (forexample: see for this purpose para. 1.1.1.1)

2. With regards to the proximity to truth , reference ismade to the criteria of truth in the sense ofconformity of theoretical statements and thepolitical economic or respective corporate reality(correspondence theory). With this, acontinued comparison of theoretical statementand observed reality is assumed in ahermeneutic sense. The SCOR model makesallowances for the fact that it is, as it were, anevolutionary model, which is adapted to the(changed) reality in regular cycles (for moreinformation concerning the correspondencetheory, cp. for example (Neale, 2002) FacingFacts).

3. Cp. (Popper, 1989) Logik der Forschung (TheLogic of Scientific Discovery), p. XXVI. Thequote is originally from Xenophanes (570 – 474

BC), founder of the so-called Eleatic School ofPhilosophy, cp. (Encyclopedia of Philosophy,2004) Xenophanes. For Karl Popper and hiswork cp. for instance (Magee, 1986, KarlPopper; Geier, 1994, Karl Popper; Popper,1 9 9 5 , Objektive Erkenntnis (ObjectiveKnowledge))

Chapter One

Objectives,methodology, approachand definition of terms

“Using SCOR has become away of life for the company,including getting the topexecutives together to makeacross-the-board decisions.(…) It costs nothing. AllSCOR is is [sic!] a tool thattells you what the possiblemetrics are you can use to

determine how your businessis doing. (…) SCOR isn’tmagic. It’s a good, simplemanagement tool, and I don’tknow why everyone doesn’tuse it.”1

1.1 Foundations andObjectives of the Work1.1.1 Arrival at andobjectives of the researchThe last ten or so years have seen apronounced change in the level ofimportance that companies attach tologistical processes.2 Whereas logisticshave, traditionally, been seen largely asa vertical function of organizations, the

more recent comprehensive functionaland integrated view has become morewidespread, particularly in the guise andthe framework of a Supply Chain.3 Thisprocess may be discerned, for example,in the creation of a new managementdiscipline, Supply Chain Management,and the increasing emphasis on thisdiscipline within companies.4

In conjunction with this change offocus, in recent times a growing numberof companies have introduced a newposition, namely that of Chief SupplyChain Officer (CSCO) or Supply ChainPresident, who often reports directly tothe Chief Executive Officer (CEO).5

This work moves the Supply Chaininto the center of the discussion. TheSupply Chain can, in the first instance,be represented by means of the physicalflow of material. It can also be used toillustrate the underlying organization ororganizational segment: procurement, forexample, or sales. Finally, therepresentation is possible by use ofSupply Chain processes: the purchasingprocess, for example, or the salesprocess. In the course of this study, theprimary focus will be on the range ofprocesses within an organization’sSupply Chain.

For the purpose of the structuring ofthese Supply Chain processes, the so-

c a l l e d Supply Chain OperationsReference Model (SCOR) is utilized andassessed with regard to the possibilitiespertaining to its clarification anddescription. The SCOR model6 wasdeveloped by an independent, non-profit-orientated association,7 namelythe Supply-Chain Council (SCC).

The Supply-Chain Council wasfounded in 1996 by the businessconsultancy Pittiglio, Rabin, Todd &McGrath (PRTM) and AdvancedManufacturing Research (AMR), andoriginally consisted of 69 voluntarymembers.8 Membership is open to allcompanies and organizations that areinterested in the application and further

development of modern and qualifiedsystems and practices for themanagement of the Supply Chain.9

This study addresses primarily thefollowing questions:

How could the SCOR model, on thebasis of model-immanentperformance indicators, betransposed upon a theses model andthereby operationalized?How could this theses-transposedillustration of the SCOR model besubmitted to an explorativeexamination based upon empiricaldata?

Since its inception, the SCOR

model has been the subject of academicresearch.10 However, these studies havetraditionally been based on a givenstructure of the model, and in turn beenconcerned with its applicatory uses.11 Atthe time of writing, however, no studyhas focused solely on the modelstructure itself. Thus, the author haschosen to address the SCOR model intheses format and test it by means of its“operational qualities.”

In this sense, the study tests thestructure of the model and asks whetherit can be considered to be “suitable andcorrect” (entirely or partly). This is

particularly pertinent as, in the yearssince the “breakthrough” of the model’sinception, it has been greatly diffusedthroughout North America and Asia,although in this respect Europe currentlylags slightly behind. This continuouslyexpanding field makes such a study morenecessary than ever.

Having said that, the work in noway wishes to lay claim to performing auniversally valid examination of theSCOR model. It is not, moreover, anattempt to examine the SCOR model assuch, but to strive for a respectivelycompiled illustration, or modeloperationalization, in order to make anexploratory contribution to research. It

is, therefore, not primarily about theexamination, verification, orfalsification of theses, but about anexploratory attempt at gaining initial –and consequently provisional – results.A step-by-step accumulation ofknowledge stands in the foreground thatmust, under all circumstances and evenafter conclusion of the work, becontinued through further focusedexaminations built upon its findings.12

Within these parameters, however,

and by use of the answers to the above-mentioned research questions, it isnonetheless possible to identify potentialareas for improvement, and also to makerecommendations accordingly. Such

recommendations are made on theunderstanding that the accumulatedknowledge is relative to the developedillustration of the SCOR model, wherebyfactors like a possible “mis-match”between theoretical model and empiricalreality or the quality of the applied datacan play a role.13 In addition to this, itmust be borne in mind duringtransposition of conclusions onto theactual SCOR model that such a practicecan only be carried out within certaingiven limitations, which will beintroduced below.14

1.1.2 Methodical approachto the workThe core of this study consists of an

examination15 of the SCOR model inrespect of the Supply Chains of selectedcompanies. For this purpose empiricalresults, accumulated via the use ofquantitative questionnaires, are used. To preserve the anonymity of the clients,the results of the empirical study arepresented on a neutral basis, withoutmentioning the names of the respectivecompanies. However, the characteristicsrelevant to these companies (indicationsof their industry affiliations, forexample) are represented, as these aregermane to the study.

The exploratory examination of theSCOR model is based upon the analysis

of the scientific and application-relatedresearch already conducted in the field.It is also based on the authors extensivepractical experience gained during atenure for years as a business consultant,particularly with respect to the field ofSupply Chain Management, andspecifically the SCOR model. Theexamination of the SCOR modelinvolves a comparison of the thesesdeveloped during the course of the workand their results, accumulated byempirical examination. An evaluation, toinclude recommendations for theimprovement of the model, appears atthe end of the work.

In the first stage, the study applies

scientific statistical methods in order totest the conformity of the developedmodel. The findings of the empiricalstudy then provide the basis forrecommendations that follow for theimprovement of the SCOR model, or forSupply Chain analysis. The conclusionprovides modern concepts and tools forSupply Chain formation, as well assuggestions for further research in thefields of Supply Chain Management andSCOR.

The theses that are outlined,although not yet verified, within thework refer to the connections betweenthe interval-scaled model parameters ofthe SCOR model.16 The verification of

the connections advocated in thehypotheses will succeed these by meansof additional descriptive-statisticalclarifications. Although the thesesthemselves are, at this stage of researchin the field, often not completelyverified, special attention is paid tothose companies that differ from a givenbasic tendency in order that suchdeviational cases can be clarified.17

The findings are thus articulated in

accordance with the submitted theses. Inthe findings, the material data or thecorrelation of the individual variables(according to the thesis in question) areprimarily verified on an empirical basis.

Diag. 1-1: Research-logical course of the work19

According to Friedrichs,18 the research-logical course of the empiricalexamination orientates itself uponconnections to discovery, reasoning andevaluation. Interpretations – unlessrelevant to the statistical durability of

the hypotheses – or appraisals withregards to concrete proceduralrecommendations play no role as far asthe findings are concerned. On thecontrary, such respective conclusionsare drawn later in the study.

The following diagram represents,in graphical form, the methodicalapproach that shapes the structure of thiswork. 1.2 Integration of the SubjectMatter into the Scientific andEmpirical DiscussionThe methods by which companies plan,purchase, produce, and sell their

products substantially influence theirposition within the market. In thepresent-day business environment,transparency, efficiency and speed arethe key factors in determining acompany’s success or failure. Efficientmonitoring of the procedures andprocesses is seen as vital if the companyis to derive advantages (which apply toall aspects of its business) from usingprofit-effective acquisition potentials,from the reduction and outsourcing ofstock, and from the improvement incustomer relations arising out of a betterdelivery service.

The continuous process ofglobalization in the procurement and

distribution markets, when coupled withthe modern trend towards a moreworldwide distribution of productionlocations, demands not only that abusiness plans and optimizes its value-generating processes20 and businesslogistic networks as a whole, but alsothat it develops greater levels ofeffective customer management. Thisrepresents a great challenge for thoseresponsible for such areas of acompany’s life because they must bothrealize operational improvements whilstsimultaneously minimizing costs, withoutletting customer service suffer in theprocess. As there are obviouslyconflicting objectives in this case, theimplementation methods must be

perfectly balanced and all relevantaspects included into any deliberationson these issues.21

As a result of increased

globalization, many companies areconfronted with the challenge of havingto plan and monitor their material andinformation flows continuously andefficiently – from procurement, throughproduction, and up to marketing. It isoften the case, however, that marketingplans are noted for their inexactness andsubsequent lack of verification as totheir implementation ability, and that asa result companies are increasinglyforced into overstocking and cost-intensive bottleneck monitoring.22

Production and procurement can oftennot react flexibly enough to fluctuationsin demand, resulting in an increasinginability to meet scheduled deliverytimes and, often, the accumulation ofcost-intensive stockpiles of goods orresources.

It is now acknowledged bycompanies involved in such fields thatsuccess or failure is regulated by the“weakest link in the Supply Chain.”Gutenberg’s Balancing Law of Planning(Ausgleichsgesetz der Planung)23 is ofgreat importance in this capacity.Although originally formulated aroundthe internal structure of a business, thislaw can also be applied to the complete

supply chain and therefore necessitatesincreased cooperation between thosecompanies involved in order to displacethe supply chain bottlenecks.Consequently, companies see themselvesfacing the following questions:24

By which means, keeping in minddeadlines, cost and level ofservice,25 can a constant balance becreated between the supply-side(stock, production and transportcapacity, etc.), and the demand-side?In which way, and at which point intime, must the supply-side berespectively enlarged or reduced?

Leading companies proactively

occupy themselves with these questionsand integrate their partners more closelyinto the planning process. Their aim is toconstantly increase the continuity andtransparency of all the businessprocesses and to simultaneouslyrecognize and remedy bottlenecks andmissed deadlines. The main challengefacing a company in this process is tosecure an economically favorable andflexible integration of the businesspartners’ data (suppliers, logisticservices, sales branches etc.) into theirown marketing, procurement,production, distribution and transportplanning, and thereby create unified and

consistent plans.26

These topics and requirements are

at present being intensively discussed ineconomic science as well as in businesspractice. One question that has becomeassociated with this debate is that ofwhether Supply Chain Management(SCM) is just a fashion or is poised todeveloped further into a recognizedmanagement concept in its own right.27

This study cannot offer a definitive

answer to such a question at this time: anumber of scientific contributionsalready exist for that purpose.28 Rather,this study stands as an empiricalcontribution that answers the question

how companies’ Supply Chains can beanalyzed and optimized through anongoing process of study. 1.3 Representation of theSupply Chain as a BusinessReference SystemIn order to be able to observe the SupplyChain (SC)29 more closely, it isnecessary to establish a definition of theterm. In literature a multitude ofdefinitions can be found which aredescribed in this chapter. As the termwas mainly developed and disseminatedin the USA, its characterization isstrongly influenced by authors from theAnglo-Saxon speaking regions.

An understanding of the Supply

Chain is important for those involved inthe implementation of proceduralimprovements to the Supply Chain. TheSupply Chain can be widely or narrowlydefined, depending upon the perspective.At present, the tendency is for a widerdefinition, as seen at the conferencecarried out by the Council of LogisticsManagement (CLM)30 in the year 2002and integrated into the definitionadvocated by the CLM. In accordancewith this the Supply Chain can now bedescribed as the total of all activities,procedures, etc., that are applied to aproduct from beginning to end.31

In this sense the Supply Chain maybe seen as beginning, for example, in themining of ore, the extraction of rawmaterials from the ground, or theplanting of seeds. The chain continuesthrough a multitude of transformationsand distributions, which deliver theproduct to the end user. It ends with theconclusive disposal of the product andits residues. In line with thisunderstanding, the Supply Chainrepresents more than just the physicalmovement of the goods: it also takes intoaccount movements of information,finance, and knowledge.

It can therefore be taken from thisthat the Supply Chain comprises all

procedures within the product lifecycle,32 including the physical,informative, financial and knowledge-based procedures for the movement ofproducts and services (from the supplierright up to end consumers).33 On theprocess side, a Supply Chain consists ofall organizations included in the design,production and delivery of a product tothe market.34

1.3.1 Definition of theSupply ChainThe definitions that can be found inacademic and business-orientatedliterature include the whole span ofperspectives – from the very narrow to

the very wide demarcation of the term.Although the Supply Chain’s spectrumhas expanded in the past years, morenarrowly demarcated or emphasizeddefinitions can still be found today. Anoverview of the various approaches tothe definition of the Supply Chainfollows. An appreciation of thedifferences involved in this field is anecessary precondition for the fullcomprehension of Supply ChainManagement.

A key decision in this field is thatof the side from which the Supply Chainis viewed, i.e., from the customer or thesupplier side. In the supplier-centricapproach, the Supply Chain represents a

network of suppliers which manufacturegoods. These goods are traded amongsteach other as well as with additionalparties. The goods originate with thesupplier and arrive finally with thetarget customer. Between these start andend points, they often run throughdistributors and processing companies.35

This supply-side view is countered

by the customer-centric approach,which assumes that a Supply Chainconsists of all necessary stages that are –directly or indirectly – involved infulfilling a customer requisition. In thisspecific case, the focus is upon thetransportation businesses, warehouses,dealers and the customers in question.36

The combination of both approachesleads to a superior definition, wherebythe Supply Chain is seen as an agreementbetween companies in order to providethe market with products and services.37

Furthermore, this comprehensive

point of view can be raised to a globallevel and placed in the context of aglobal association of organizations. Inthis sense a Supply Chain represents aglobal network of organizations,working together to improve the flow ofmaterial and information betweensupplier and customer. The operationalobjectives are the lowest possible costand the highest possible speed. Theultimate objective is the satisfaction of

customer needs. The information flowruns, as it were, forward-facing (i.e.,from customer to supplier); the materialflow, on the other hand, runs backward-facing (i.e., from supplier to customer).Furthermore, information flows fromcustomers to dealers, manufacturers,logistic services and raw materialsuppliers. Material flows from rawmaterial suppliers or componentsuppliers to customers. The commontrait of both the material and informationflows is that the process amongst theSupply Chain partners should becoordinated, and this also implies thatsome degree of forward and backwardcoordination isrequired.38

To take this line of thought further,the approach can also be differentiatedfrom the supply and demand aspect. ASupply Chain has the purpose oftransferring products and services fromthe suppliers up to the consumer (forexample organizations, stores,individual people). The actions withinthe Supply Chain change depending uponthe product and type of demand, but it ispossible to identify a number ofgenerally valid value-creating processesas follows:39

Make:Manufacture of material orbuilding components, etc.Combine:Assemble, package, etc.

Move:Distribute, collect, etc.Store:Stock, trade, etc.Customize:Install, configure, etc.

The demand-side Supply Chain can

be described as the demand chainfocusing upon market demand towardssuppliers. It becomes clear during theexplicit consideration of the demand thata respective Supply Chain is customerdriven.40 The term pull concept isoccasionally used to describe thisprocess of government by a form of“demand vacuum.”41

In exactly the same way as a

supplier can have a multitude of Supply

Chains to monitor, the supplier’scustomer also has limited demand chainsthat can be individually analyzed. Thedemand chain translates a customer’sobjective into information that thesupplier can use as an instruction to actupon and is, in this sense, determined bya decision process. The four generalstages by which the decision process ischaracterized begins with the definitionof the purpose (define purpose). At thesecond stage planning takes place(plan), for example in the form of acategory plan. The third stage comprisesthe management of consumption andrequirements (manage consumption andrequirements); for example, within thefield of stock management. Buying

transactions (purchase transactions) areat the center of the last stage; forexample, a call-off order as part of amaster agreement.42

An alternative approach is the

organization-orientated view of theSupply Chain. In accordance with this,the Supply Chain represents analignment of processes within acompany, as well as with othercompanies (inter- and intra-businessprocesses), which produce goods andservices and deliver them to thecustomer. It comprises actions such as,for instance, procurement of material,production planning and distribution.Purchasing, production, inventory

management, stock-keeping and transportare usually regarded as part of the SCorganization. Marketing, sales, financialareas and strategic planning, on the otherhand, are not considered to be a part ofthe SC organization. Productdevelopment, marketing plans, orderregistration, customer service andcompany accounting are not clearlyassigned. They clearly belong to the SCprocesses, but are only seldom part ofthe SC organization.43

The combination of the process and

organization-orientated points of viewcan be summarized as follows:

“The supply chain includes the

organizations and processesfor the acquisition, storage,and sale of raw materials,intermediate products, andfinished products. Supplychain product flow is linkedby physical, monetary, andinformation flows.”44

The present view of a Supply Chain

is largely one-dimensional, but a furtherdifferentiation can take place with theaid of its layered arrangement of variouslevels. In accordance with this view, aSupply Chain is an alignment ofsuppliers and customers, beginning atone end with raw material and ending atthe other with the delivery of a

completed product to an end customer.The Supply Chain can be dissected intoseveral layers.

A single-level Supply Chain purelyillustrates the direct customer andsupplier, whereas a multi-level SupplyChain can reach as far as the rawmaterials on the one hand and thedisposal of worn-out finished goods onthe other. The complexity increasesproportionately with the increase in thenumber of levels. Most companies,therefore, have neither the means nor theresources to monitor the Supply Chainnetwork and because of this restrictthemselves to one or two levels.

In addition to the levels, the

components flowing through the SupplyChain must be considered andillustrated: Goods and services in onedirection, payments in the other, andinformation in both directions.45 Theinterpretation of a bidirectionalinformation flow advocated hererepresents the reality far better than themulti-directional flow of informationdescribed above. Actual concepts, likethat of Collaborative Planning,Forecasting and Replenishment(CPFR), for example, build upon a flowof information in both directions.46

A further criterion that can be

included into the Supply Chain’sdescription is the decision aspect. A

multitude of decisions must be reachedwithin a Supply Chain showing a largenumber of SC partners. These decisionsrefer, for example, to investments,strategies for coordination andcooperation with partners, customerservice, and profit-maximizingstrategies. Some of these decisions havefar-reaching influences upon the SupplyChain and are of a complex naturebecause with increasing marketdynamics, a constantly higher level ofuncertainty exists regarding the effects,and a multitude of variables must betaken into account. The Supply Chainresulting from this can be described as amarket-driven Supply Chain.47

The inclusion of the respectivecompany’s functional areas and the mainactivities connected therewith lead to afunctional description of the SupplyChain. The following five mainactivities can be identified with regardsto a company’s functional areas:48

Purchasing: includes the tasks ofpurchasing raw materials,components, resources andservices.Manufacturing: refers to themanufacture of products or servicesin addition to resource maintenanceand repair, as well as the trainingof co-workers. It may be

summarized therefore as theimplementation of all tasksnecessary for production.Movement: consists of thetransportation of materials andpersonnel inside and outside theSupply Chain.Storage: refers to those productswhich find themselves beingprocessed (work in process, WIP)in addition to raw materials, whilstthese await transportation orreformation, and the finished goodsbefore these are sent to thecustomer.Sale: comprises all market-orientated activities, includingmarketing and sales.

The step from a static to a dynamic

view of the Supply Chain is achieved byincluding activities pertinent to thefunctional area. Up to now, thepreviously mentioned flows of material,payments and information have beenregarded as linear and coupled. Due tothe introduction of the internet49 and theacceleration of information flowsassociated with it, these flows have (to acertain degree) become uncoupled fromone another. Information now flows in apredominantly independent manner fromthe respective flows of material andpayments.

As a result of this, Supply Chains in

the traditional sense have furtherdeveloped themselves into networkedSupply Chains, which network the SCpartners together with the best suitedcomponents, technologies and customerservices. SC networks are additionallydynamic in nature and make it possiblefor SC partners to be included orexcluded according to certain criteria;for example, technological advantages,product life cycles and customerpreferences.50

These dynamic Supply Chains

promote, amongst other things, thedevelopment of new business strategies.Within this framework, focus is placedupon new methods of customer

integration, outsourcing of businessfunctions, cooperation with customersand suppliers51 and inventorymanagement.52 By these meanstraditional linear Supply Chains areconverted into dynamic SC networks.53

A further integral SC element isrepresented by its value generativecharacter (value-add).54

According to this, the Supply Chain

is a network of organizations, which areassociated with each other in a forward-and backward-facing manner, in order togenerate value within diverse processesand activities. This value is reflected inproducts and services which aredelivered to the end consumer.55

Normann and Ramirez describe the

connection between value generationand the respective business and SupplyChain strategy as follows:

“Strategy is the art of creatingvalue. It provides theintellectual framework,conceptual models, andgoverning ideas that allow acompany’s managers toidentify opportunities forbringing value to customersand for delivering that value ata profit. In this respect,strategy is the way a companydefines its business (…).”56

With consideration given to value

generation, aspects of ?informationtechnology can finally be integrated intothe operationalization of a Supply Chain.The result is a so-called Value Chain(VC). Accordingly, a Value Chainrepresents a business outline that usesdigital SC concepts to ensure not onlycustomer satisfaction,57 but alsoprofitability.58 The VC focuses mainlyupon the competitive factors of time andflexibility59 and has the primaryobjective of being able to react quicklyand flexibly to changing customerrequirements.

The special characteristics of a

value chain pronounce a distinctdifference from a traditional businessoutline and may be described asfollows:60

Customer-alignedCollaborative and systematicAgile and scaleableFast material, payment andinformation flows (fast flow)Upheld by Information Technology(IT) (digital).

A Value Chain is hence positioned

above the concept of the Supply Chain. Itassumes the reality of a Supply Chainand focuses explicitly upon the

generation of value for all involvedparties (the company itself, customersand suppliers). It still represents, to agreater extent, a static system, but the(bilateral) information flow is oftensupported by modern IT systems.61 Theapproaches outlined above assumephysical partners to be participants inthe Supply Chain.

Virtual value nets62 originate dueto the increasing usage of virtualization.The linear, physical Value Chain modelhas adjusted itself accordingly. Thisreformation reaches above the physicalboundaries of a marketplace and into theglobal and fast-developing digitaleconomy. With the introduction of the

internet and the increasing role oftechnology as a catalyst of newstrategies, companies find themselvesconfronted with new strategicrequirements and managementproblems.63

The real-time information exchange

and the interactive performance potentialof the internet have changed the businessenvironment to the point where it is nownot only customers, but also othercompanies that have access toalternative products and services. Newdistribution channels are establishingthemselves and leading to opportunitiesfor optimizing value generation andsimultaneously enabling interactions to

become more transparent. The winnersin these virtual value chains will bethose who have faster access toinformation and resources, and can at thesame time extract from this the suitablecompetitive and SC strategies.64

Because of this, the traditional

physical alliance has developed itselfinto a virtual alliance, in which there arean increased number of possible SCpartners who exchange information. Thevirtual Value Chain represents analignment of market partners who worktogether as a unit, whereby each of themcontributes, so to speak, a component ofthe value. The value-donating activitiesextend outwards from the supply-side in

the form of the raw materials, incominglogistics and production procedures,right up to the demand-side in the formof outgoing logistics, marketing andsales.65

Michael Dell, founder of the Dell

company,66 describes a virtuallyintegrated organization as anorganization that is not networked byphysical objects of wealth, but byinformation67 – or, alternativelyexpressed, by information technology(IT).

The Supply Chain is, therefore, acomponent of a superior ElectronicBusiness (E-Business) concept. This

association is illustrated by thedefinition developed by Seibt:

An organization practicesElectronic Business, if several or allbusiness processes

within the organizationbetween itself and its businesspartnersbetween itself and a third party(e.g., authorities)

are totally or partially realizedvia the assignment ofelectronic communicationnetworks and are supported by

Information andCommunication Technology(ICT) systems.68

In the interests of clarity, it is

important to differentiate here betweenE-Business and the related concept ofElectronic Commerce (E-Commerce)that generally denotes the electronicexecution of business transactions.69 Thepart of the E-Business concept relevantto the Supply Chain is also oftenreferred to as Electronic Supply ChainManagement (E-SCM).70

Ross describes E-SCM as the

tactical and strategic components of thebusiness strategy that aim to combine the

common production capacities andresources of overlapping SC systems bymeans of internet technology, with theobjective of creating customeradvantages.71 The main difference fromthe “traditional” SCM or respectivevalue chain management is thus seen inthe fact that information technology isapplied in the process in order tosupport the optimal completion of theflow of goods and information.72

1.3.2 Categories of SupplyChainsThe definitions included above focus onvarious Supply Chain features orcharacteristics. Building upon these,however, a variety of extra

categorizations should also be includedin the equation, and these aresummarized below.

One possibility lies in the questionof whether the Supply Chain is chieflyaimed at the product or the endcustomer. Ayers suggests the followingdifferentiation in this context:73

Product-centric Supply Chains areSupply Chains tailored inaccordance with special products.One or more product offers canresult from this, which constitute aseparate Supply Chain.Customer-centric Supply Chainsare Supply Chains tailored in

accordance with special marketsegments. One or more SupplyChains may result from this, whichare organized around marketsegments.

A further difference can be

identified by looking closely at thebusiness strategy, and requirementsassociated with it:74

Arm’s length, open competition :competitive offers and tenderaction. The emphasis here is uponintense trading.Commodity trading: independentmarketing, forced by the necessity

of the business agreement. Theemphasis here is upon monitoringthe deviational range of commoditygoods.Partnering for customer delight:openness, trust and splitting of thework to be carried out. Theemphasis here is upon supplierperformance extended towards thecustomer (forward-facing in theSupply Chain) and the value aspectextended from the customertowards the supplier (backwards-facing in the Supply Chain).From suppliers’ suppliers tocustomer’s customers : here thereis a linkage of all marketparticipants within a horizontal

Supply Chain. The emphasis isupon seamless delivery,optimization and integration.Lean supply chains and systemsintegration: these are associatedwith cost minimization andreformation of the cost structure bystages. Their emphasis is uponefficient cooperation, but not,however, upon economizing, whichcould lead to resource bottlenecks.Competing constellations of linkedcompanies: here, market leadersform an alliance with the bestmarket partners. The emphasis isupon performance potential,capabilities and organization-cultural combination ability.

Interlocking network supplybetween competitors: theseconsolidate the step-by-stepcompletion of transactions. Theemphasis is upon unification wherea minor competitive advantage75

exists, with the aim of usingsynergies.Asset control supply – dominate ordie: this approach is used to gaincontrol over the assets and targettheir application. The emphasis isupon the correct usage ofcompetitive instruments at tenderaction stage.Virtual supply – no production,only customers: here, fixed costsare kept low by outsourcing of

production.76 The emphasis is uponmarketing and distributioncapabilities.

In focusing upon the primary

Supply Chain area or (to use anothertitle) the corporate-policy point of view,it is possible to categorize further anddifferentiate on the grounds of strategy,function, logistics-transportation, andinformation management points of view.

T he strategic view considers theSupply Chain design to be the mostimportant element of the competitivestrategy. As a part of this, the SupplyChain represents an alignment ofresources which are used to support the

product’s position in the market withregard to the combination of endcustomers, price calculation and salesmeasures. The purpose of such a processis the improvement of profit margin uponproduct turnover.

In the functional view, the SupplyChain consists of the individualorganizations that are required in orderto purchase, transform and sellmaterials. The focal point is occupied bythe material: its procurement,transportation, and other costs areimportant. The aim is to lower cost inthe functional areas relevant tosuccess.77

T he logistics-transportation viewassumes that the Supply Chain representsthe physical course of a product througha number of operating plants andfacilities which are connected by meansof a transport association. Thesefacilities and installations includefactories, warehouses, sales centers,vehicle pools and distribution centers,and the view seeks to bring about theminimization of logistic cost.

In the information managementview, the information flow between thevarious parties represents the integrationfactor. In this sense, an integrated SupplyChain possesses a communal basis ofinformation, as well as mechanisms with

which to exchange this informationamongst the participants. Accordingly,the aim of this view is a reduction of theinformation process cost.78

This study follows the latter

categorization. This type of linkage isanchored most strongly into the SCORmodel, which will be dealt with later.But for the purposes of this study, it mustbe decided what the importance of theSupply Chain is with regard to thecompetition between companies. Theunequivocal – and simultaneously most“radical” – answer, with which theauthor agrees, is as follows:

“The leading-edge companies

(…) have realized that the realcompetition is not companyagainst company, but rathersupply chain against supplychain.”79

There is not much more to add to

this quotation, although it does serve asa powerful example of the way in whichthe term has brought forth and seen therapid development of a new disciplinein the last decade: management of theSupply Chain or Supply ChainManagement, respectively.80 This willbe the focus of the study in the pageswhich follow. 1.4 Overview of the Present

Status ?of Supply ChainManagement ?in LiteratureThe role of Supply Chain Management(SCM)81 within an organization haschanged considerably over the roughlylast three decades. In the 1970s, whenthe area was better known as logistics, itwas largely restricted to the integrationof storage and transportation policieswithin a company. In addition to this, thehigh interest rates (often in the two-digitregion) that most countries experiencedduring that decade forced companies tobe particularly vigilant when it came tothe investment of their capital. At thistime, leading logisticians were primarilyconcerned with reducing their stocks.

Their focus was mainly upon how thebusiness could implement internalchanges, which would lower theinventory and logistic costs. Evenattempts to reduce production anddelivery cycle time and as a result ofthis, safety stock, were carried outinternally because cycle times weremainly considered to be incominginformation for the forecasting andprocurement process.

In the 1980s, the focus shiftedtowards restructuring the cost structureswithin the Supply Chain. Attention wasdiverted to integration of Supply Chainprocedures in order to reduce SCbusiness cost and assets for the Supply

Chain. Around the end of the 1980s,SCM then changed its focus from costreduction towards the improvement ofcustomer service. The advantages soughtby means of an improvement of theSupply Chain’s performance includedhigher turnover and higher profitability,due to a greater share in the market, andpricing advantages over the competitionwhich manifested themselves in highermargins.

The level of interest in improvingcustomer service was an equallyimportant feature of businesses duringthe 1990s. In the same way, businessgrowth – which had been consideredwithin many companies to be the

responsibility of product development,marketing and sales – was adopted as anSCM objective.

In the present decade, the field ofSCM has seen further changes, namelythe development of Strategic SupplyChain Management. As opposed to thetraditional point of view, in which itwas only a partial definition ofobjectives, SCM has achieved astrategic function which immediatelycontributes to the organization’s successand has simultaneously become animmanent component of businessstrategy. It is increasingly the sharedview that SCM not only determines thebusiness strategy of many companies, but

also makes their successful tradingpossible. Alternatively expressed, SCMis simultaneously conditional forsuccessful business strategy and adetermining factor of business strategydesignation.82

In addition to these factors SCM

was, above all, strongly concentrated onimprovements with regard to the supply-sided processes. Thinking in this areatended to overlook, however, the factthat companies who wished to monitortheir Supply Chain in an optimal waycould only achieve this goal if they wereable to recognize the fundamentalconnection between supply and demand– and the resulting effects of this upon

the SC strategy. In many cases, however,companies scrutinized their supply-sidedpossibilities, but neglected the demandfactor. The relationship between thesupply and demand side lies in the factthat demand determines the SupplyChain’s aim and therefore has adeterminative character, whilst thesupply-orientated performance potentialsupports the fulfillment of the demand.Now that this link has been affirmed,companies must find new means ofcreating the coordinated monitoring ofsupply and demand chains. SCMrepresents a central component of theseefforts.83

The ability of a business to

reconcile supply and demand is afunction of its capability to react, oralternatively expressed, its capability toanswer to market signals in a timelymanner. This flexibility, on the otherhand, is mainly influenced by thecompany’s working capital andoperating capital expenses.Organizations have often fought to adaptsupply and demand in this mannerbecause during this process, the focusfalls upon improvement in forecastaccuracy, production, and inventoryoptimization and the reduction of cycletimes.84

Consideration should thereby be

given to the fact that, although useful,

these measures do not offer a unifiedsolution. Companies must therefore alsoconsider such measures which includelabor and capital equipment costs, andthey must find new ways to adjust theincentive systems, not only internally,but also within the extended SupplyChain (i.e., with reference to the SCpartners).85

1.4.1 Evolution of SupplyChain ManagementLong before the term Supply Chain wascreated and the new discipline offormation and optimization of thisSupply Chain—Supply ChainManagement—emerged, people werespeaking of a so-called logistic chain.

This logistic chain stood in the center ofa discipline described as logistics (andnowadays is partially still described assuch). Hugos submits the followingdescription:

“The term ‘supply chainmanagement’ arose in the1980s and came intowidespread use in the 1990s.Prior to that time, businessesused terms such as ‘logistics’and ‘operations management’instead.”86

To this end and for the purpose of

demarcation, it is useful to introduceseveral definitions of logistics at this

point. In the classic terminology of theCouncil of Logistics Management(CLM),87 logistics are described as theprocesses used to plan, implement andcontrol the efficient flow of material,beginning with storage of raw materials,through work in process (WIP), tofinished products and services, as wellas the respective information from theoutlet to the point of consumption. Thisfield includes incoming and outgoinggoods as well as internal and externalmaterial movements. The ultimatepurpose is to be able to fulfill customerrequirements.88

Logistics can also be seen from the

organizational aspect, as representative

of an objective-orientated logic whichexists to monitor the processes ofplanning, allocation and control offinancial resources - processes whichare reserved for the physicaldistribution, production support andpurchase transactions.89

Other definitions focus upon the

issue of conceptual integration by whichlogistics include the creation ofrelationships to time, space, amount,shape and possession, not only withinone company, but also in conjunctionwith other companies. The tools used toarrive at a logistical target are strategicmanagement and infrastructure andresource management. The aim is to

create products and services that satisfycustomer needs. Within this, logisticsare involved at all levels of planningand implementation on strategic,operational and tactical levels.90

Logistics management also

inevitably presents limitations anddependencies. Accordingly, logisticalactivities usually consist of incomingand outgoing logistics, vehicle-pool orfleet management, respectively, stock-keeping, material movement, orderregistration and completion, logisticnetwork design, inventory management,supply and demand planning, and thecoordination and monitoring of logisticservice providers.91 Only in a limited

way do such activities cover issues ofprocurement and purchasing, installationand packaging, and customer service.92

From such definitions, it is but a

short step to move to management of theSupply Chain (i.e., Supply ChainManagement) which is identifiable onaccount of its integrative character.Therefore, Supply Chain Management(SCM) includes not only logistics butalso, above and beyond this, businessareas such as purchasing, marketing andinformation technology. The majorpurpose of this field is to improveSupply Chain efficiency.93

Expressed another way, SCM can

be defined as the integrated planning andmonitoring of processes in the valuechain. The representative objective inthis case is the optimal satisfaction ofcustomer needs. In this sense, logisticsmanagement represents a component ofSCM. This component has the task ofplanning, implementing and controllingthe efficiency and effectivity of theforward- and backward-facing flows ofgoods, services and appropriateinformation, with the intention offulfilling customer requirements.94

As a result of this, SCM represents

an integrative functional area whosepriority is the responsibility for theconnection of main business functions

and processes within an organizationand also of other firms included in theSupply Chain. These connections areintended to help the business arrive at aconsistent and achieveable businessmodel that comprises the logisticsmanagement functions in addition toproduction flows and has the task ofensuring the coordination of the SCprocesses with the functional areas ofmarketing, sales, product design,finances and information technology.95

SCM also comprises the planning

and monitoring of all logisticsmanagement activities. Beyond this,however, it comprises the coordinationof and cooperation with the business

partners within the Supply Chain, suchas suppliers, distributors, logisticservice providers and customers. In themain, SCM integrates the management ofsupply and demand – Supply andDemand Management – within onebusiness, and also throughout variousother firms.96

A clear definition of the term SCM

is, however, nowhere near as simple asone would imagine due to the diffusionof the term in modern usage. In actualfact, the term SCM is associated withvarious meanings. In the widest sense, itencompasses all logistical activities,customer-supplier relationships,development and introduction of new

products, inventory management andfacilities. The concept allows itself tobe applied, in analogue, to the area ofservice provision. Many practitionersdefine SCM more closely and restrictthe definition to activities within onecompany’s Supply Chain. By this theyinevitably reduce the application area ofimprovement measures to their ownbusiness and the internal Supply Chain,without the inclusion of external SupplyChains.97

1.4.2 Definition of the termSupply Chain ManagementAt this point, it is useful to define SupplyChain Management in order to outlinethe applicable boundaries of this study.98

Originating from the classical planningand control approach, Supply ChainManagement represents an expansion ofthe existing approach into a company-spanning planning and control strategy.This is also inherently connected withthe Advanced Planning System (APS),99

which also explicitly includes aninformation technology (IT) supportaspect.

If the time dimension or theplanning horizon is included, SCM canbe defined as the coordination of thestrategic and long-term orientatedcooperation between all participantswithin the whole SC network.100 Thisincludes the purchasing area as well as

the production area, and extends into thefields of product and process innovationwhere the purpose is to develop andmanufacture products. Each SCparticipant is active in the area forwhich he possesses corecompetences.101 The choice of furtherSC partners is mainly made from theaspect as to which potential is presentfor the realization of shorter leadtimes.102

SCM may be described as the

process of planning, introducing andcontrolling an efficient and effectiveflow of goods, services and relevantinformation, from the starting point of theSupply Chain right up to the point of

consumption. The focus of such aprocess is the satisfaction of customerrequirements.103 By a furtherdifferentiation of the process-relatedpoint of view, SCM can also be seen asthe design, maintenance, and applicationof SC processes for the satisfaction ofend customers needs. In this sense, itcovers Supply Chain formation as wellas the consequent operation andmaintenance. New tasks ensue for theinvolved executives, because traditionaltasks have to be completed in a newway. Principally, the introduction of an(explicit) SCM discipline has, as aconsequence, an extension of the rangeof tasks and responsibilities of co-workers.104

Apart from this, the business

process-related definition can beextended to the point where SCMrepresents the integration of businessprocesses from the end customer right upto the suppliers. This integrationprovides the products, services andinformation that generate value for thecustomer. Having said that, SCM leadsto a change in the existing Supply Chainand generates customer benefits bymeans of the targeted usage ofinformation associated with the SupplyChain.105 The organizational processeswithin the Supply Chain must also beplanned, monitored and controlled, atask that requires a generally accepted

system of objectives.106

Extending from the (physical)

goods flow, the Supply Chain subsumesall those activities associated with theflow and transformation of goods,starting with raw material right up to theend consumer, as well as the associatedinformation flows. SCM thereforerepresents the integration of theseactivities by means of improvedrelationships with the SC partners, inorder to gain a permanent competitiveadvantage.107

The definition also reminds us that

SCM arises from a constantly self-developing management philosophy. In

the framework of this philosophy, theobjective is to combine the commonproduction competences and productionresources of the business functions thatlie not only within the organization, butalso with the external allied SC partners.The aim is to create a highly competitiveSC system, furnished with customerbenefits which targets the developmentof innovative solutions and thesynchronization of the product, serviceand information flows. The ultimate goalis the generation of maximum value forthe customer.108

If one continues this almost

dialectic development of SCM, thefurther developments of earlier

management concepts, such as LeanManufacturing,109 may be seen asprecursors of the practice. In suchconcepts the application area isextended into the sphere ofdistribution.110 In this sense, the aim ofSCM is to improve the efficiency of theproduct delivery process, starting withmaterial suppliers right up to the endcustomer, in order to deliver the correctproduct at the correct time, with theminimum of completion effort and safetystock.111 The focus of improvementmeasures lies in the areas ofcoordination, distribution, productionand purchasing – spread overorganizational units and various firms.112

Seen from the functional side, SCMmay be defined as the systematic,strategic coordination of traditionalbusiness functions and of the tacticalmeasures beyond these businessfunctions. This means that it includes thefunctions within the respective business,as well as throughout various firmswhich are integrated into the SupplyChain. The practice aims for long-termimprovement of the performancecapacity of the individual firms inaddition to the Supply Chain as awhole.113

Seen from a behavioral angle, SCM

can be defined as those activities carriedout in order to influence the Supply

Chain’s behavior. In this form, SCMrepresents the coordination ofproduction, inventory stocks, locationsand transportation amongst the SCparticipants, in order to ensure the bestrelationship between performancecapacity (capability) on the one side,and efficiency on the other.114 Bothobjective criteria – performancecapability and efficiency – will be moreclosely examined later, as theyrepresent, so to speak, the two pillars ofthe SCOR model – or more specificallythe two sides of the equation with regardto the performance indicators whichform the basis of the model.115

A further possible differentiation

can be undertaken by looking closely atthe two sides of the Supply Chain, i.e.,monitoring the supplier-side (supplier-centric supply chain management) onthe one hand, and the customer-side(customer-centric supply chainmanagement) on the other. Inaccordance with this, the distinction ofthe supplier-centric approach exists inthe fact that the business and itssuppliers, distributors and customers, –i.e., all the business associations in thefurther sense – cooperate in order toprovide the market with a commonrespective product or service, for whichthe customer is prepared to pay therequired amount. The group of firmsrecruited from the respective partners or

participants functions like an expandedbusiness116 and ensures the optimal useof shared resources (manpower,procedures, technologies andperformance measurement), in order toattain synergies. The results are productsand services which combine high qualitywith value for money and can be quicklydelivered to the market.117

The definition of the customer-

centric approach purely requires that theconventional definition be expanded asfollows (emphasized):

The business and its suppliers,distributors and customers –i.e., all Supply Chain parties

in the further sense – worktogether in order to providethe market with a commonproduct or servicerespectively, for which thecustomer is prepared to paythe required amountthroughout the total life cycleof the product. The group offirms recruited from the SCpartners or participantsrespectively, functions so tospeak as an expanded businessand ensures the optimal usageof shared resources in order toattain synergy. The results areproducts and services of highquality that can be quickly

delivered onto the market andensure customersatisfaction.118

For the SCM’s focus upon the

customer side, the terms demand-supplychain management or demandmanagement can occasionally be used.The primary purpose of this concept isthe generation of value for the customer,with simultaneous performancecapability improvement regarding assetperformance and cost-efficiency.119 TheSCM’s primary objective is theenhancement of the marketing of goodsand services to the respective endcustomer or end consumer whilstsimultaneously lowering inventory

stocks and minimizing costs.120

Conflicting objectives – so-calledtrade-offs – inevitably arise from this,because the underlying competitivefactors (cost, time, quality andflexibility) compete with one another.As a result, SCM seeks to optimize theefficiency of the companies involvedand harmonize the conflicting objectives(under the provisions of the prioritiesaccording to each chosen competitivestrategy).121

1.4.3 Value-based SupplyChain strategiesIn recent years the number of companiesfollowing a so-called Value Chainstrategy has risen markedly. This

tendency has been mainly promoted byfirms who use highly developedinformation technologies to improvetheir capability in the field of SCM. Adecisive factor to business success istheir competence in being able to offerinnovative products of the highestpossible quality, at marketable pricesand faster than the competition.122 TheSCM ompetence’s123 objective, usingrespective SC processes, is to improveservice of customer requirements, makebetter decisions, and enhance businessperformance to secure a competitiveadvantage.124 The consequence is that amultitude of organizations have draftedstrategies which focus upon the relevantprocesses for the fulfillment of demand

(demand fulfillment process). Suchstrategies are ultimately supposed tocontribute to optimizing order cycletimes, financial flows (cash flow),125

Return on Equity (RoE), market shareand profitability. In this sense, theyrepresent the basis of the SC strategy.126

SCM represents a mutually

dependent organizational structure whichconnects functions, firms and countrieswith one another, synchronizes goodsmovement with demand rate andpropagates the value generated on theglobal market. For each product there isa Supply Chain, and for each SupplyChain a competitor. These chains aredeveloped by large corporations –

typically distinguished wholesale chainsand Original Equipment Manufacturers(OEMs)127 – who have the necessaryvision and enforcement potential toadvance their SC partners’ performancecapability, exchange data and work in analliance, in order to ensure a superiormarket position and the improvedefficiency of the business.128

The development of the value-

oriented SC approaches results from therecognition that the isolated optimizationof individual parts of the Supply Chaindoes not lead to an overall cost-favorable solution. Goldrath summarizesthis in the recognition that the sum oflocal optima is not equal to the global

optimum.129 It is therefore necessary toview the alignment of events within theSupply Chain as a whole (holistic),starting with the customer requisition, asfar back as the purchase order to the rawmaterial supplier, as well as forward-facing through all businesses included inthe manufacture and delivery of theproduct to the end customer. Focusing onthe Supply Chain as a whole representsthe first stage; focusing on the productthe second; and the inclusion of thevalue-generating flows in the sense of avalue-oriented, SC-focused processorganization, as opposed to thetraditional performance measurementthat was built-up on structuralorganizations,130 represents the third

stage. The assignment of a “valuestream” is thereby possible, whichillustrates the present-day businessprocesses more effectively than wouldbe the case within the framework of theconventional Supply Chain.131

1.4.4 De-integrated SupplyChain strategiesDe-integrated Supply Chain strategies132

are a diametric oppositional approach inconnection with SC strategies, becausethe latter specifically shifts theimportance of integration into the focalpoint. Within the developmentframework of the so-called SMARTautomobile,133 a feasibility study was

carried out in the first instance. TheSupply Chain developed in this contextand at that point in time, mid 1990s,represented a completely new approach.In this way, for example, new modelswere created for supplier inclusion andproduction outsourcing, which weredistinguished by pre-installation at thesupplier’s location, integration ofsuppliers in the design and finalassembly, and the proportionalownership-splitting of productionlocations.

Additional questions arose, forexample, from the fact that theinitializing company only contributedroughly 15 percent of the value-add

within the Supply Chain. The concretequestion resulting from this was how aSupply Chain, within which the centralbusiness only provides a relativelyminor contribution in value, could bemonitored.134 The de-integrated SupplyChain developed within the frameworkof the feasibility study represented thebasis for the introduction of so-calledcustomer-specific series production(mass customization).135 Campbell andWilson describe the approach of a de-integrated Supply Chain with the termstrategic network and define this as anopposite pole to the previouslyrepresented, value-orientated SCapproaches (value concepts). Inaccordance with this, the value-

orientated approaches within businesssystems that simultaneously postulate aclose cooperation and the retention ofindependent firms are the most effective.

Four characteristic features of abusiness system allow themselves to beidentified, which are advantageous to thedevelopment of strategic networks:136

Some critical SC activities must show

advantages if they are to beimplemented in a de-integrated form.This can be determined by differencesregarding market entry barriers andcompetitive advantages.

Specialized investments lead to higherefficiencies. These can be represented

in the form of capital investments orinvestment in the workforce.

The adaptation speed (speed ofresponsiveness) is of fundamentalimportance.

Innovation presupposes thecomprehension of the SC system as awhole.

1.5 Methods of Analysis andMeasurement of thePerformance Potential of theSupply Chain1.5.1 Description of SupplyChain ProcessesA process can be defined as a line of

sequential activities and actions whichlead, over time, to a result. Processesmay be further subdivided into partialprocesses. Furthermore, a differentiationcan be made between key processes,which include main or partial processesand immediately contribute to purpose-fulfillment in the business core, andsupport processes, which representassociated activities in support of thekey processes.137 The following listingshows typical key processes inproduction businesses:138

Product designDevelopmentOrder acquisition

Production planningProcurementProductionDistribution and disposal.

Often the key processes named are

also distributed amongst severalcompanies if a respective division of thework is predetermined. The keyprocesses are integrated into thepreviously mentioned product lifecycle.139 Fundamentally, two varyingprimary approaches for the respectiveillustration or description of SupplyChains can be differentiated: the ProcessChain Approach and the Supply ChainOperations Reference Model (SCOR).

The SCOR model extends itself

over the complete Supply Chain,beginning with the procurement process(source of supply), up to the point ofconsumption. It is an ideal industry-spanning approach, in which theprocedures within a Supply Chain areagreed upon by the partners.140 As theSCOR model will be dealt with moreexplicitly later, the alternative approachwill be explained in more detail first.

The process chain approach , alsoreferred to as the Process Chain Model,forms a businesses Supply Chain seenfrom a purely process-orientedperspective. The result is a type of

process-focused Supply Chain, forwhich the description Process Chaincan be found.141 The process chainmodel enables a visualization andanalysis, in addition to processorganization within the Supply Chain.With this, every process within theSupply Chain, which is reflected in theform of process chain elements, can berepresented by means of the followingparameters:142

InputOutputResourcesStructuresControl

A process chain element is

associated with the businessenvironment via the input, whichdescribes the “load” under which theSupply Chain stands, and also refers tothe output. According to process chaindesign, the respective process chainelement transforms a given input into agiven output. The process, whichunderlies the design, is described byprocess chain elements upon a lower,i.e., more detailed, level.143

1.5.2 Quantities/Times-framework in the context ofthe Supply Chain

The comparison between input andoutput quantities allows conclusions tobe drawn both with regard to theproductivity144 of the process chain andto its effectiveness and efficiency.145

The approach also aims to make thenecessary information available for theimplementation of model-supported,quantitative Supply Chain performanceanalyses. This quantities/times-framework is vitally important in thefield of indicator measurement and itsnecessary factors.

Cost accounting is an important partof the successful business operations.Before costing can be undertaken, anumber of decisions have to be made for

example with reference to the timing ofthe cost accounting. Pre- and post-costing can be differentiated withregards to the timing:146

Pre-costing is necessary if aproduct is newly introduced ontothe market. Exact cost data aretherefore not known, but can beestimated on the basis of factorslike presumed purchase prices orpreparation periods. Pre-costingthus enables an initial pricedetermination.Post-costing is mostly undertakenupon expiry of an accounting periodand namely on the basis of actual

cost data. Deviations identified inthis process can then be used toinfluence price corrections.

Costs represent the assessed

consumption of production factors forthe provision and marketing of businessperformance, as well as the maintenanceof operational preparedness.Alternatively expressed, quantities andtimes are multiplied, or respectivelyappraised, by prices or rates, leading toa cost figure.147 Costing, also describeda s cost accounting, is concerned withthe distribution of costs to the individualproduct or performance. It thereforeidentifies the personal cost and with that

creates the basis for price politicsthrough the identification of lowest pricelimits.148

Business leaders are usually most

concerned with the measurement ofSupply Chain costs (SC costs). Thisrefers to an area that often comprises acomplex alignment of activities, and anexact measurement is often difficult.

There are two primary stageswhich must be successfully implementedin order to ensure the exact measurementof the SC costs:149

First, the cost structure of theSupply Chain must be located as

close to the reality of the situationas possible.Further, the system for themeasurement and reporting of thesecosts must be well designed.

In addition to the specific SC costs,

the most generally used components thatcan be accurately measured (in post-costing) or estimated (in pre-costing) arequantities and times. Thequantities/times-framework canconsequently serve to determine themerits or demerits of a certainalternative, without requiring largeamounts of time devoted to theevaluation of prices and/or calculatoryrates. This is particularly important

when it can be assumed that nosubstantial differences are to beexpected with regards to quantities andtimes. Stemmler combines the meaningof the quantities/times-frameworkconstituted by these diverse aspects andwithin the context of the Supply Chain asfollows:

There is no doubt that asuccessful business dependson accurate and timelydelivery of goods or servicesto its customers. Supply chainmanagement aims atminimizing mass and time.Needless to say, an efficientlymanaged supply chain requires

measurement of the costs150

associated with the physicalmovement of goods andrelated information flows.151

The quantities/times-framework

and the costs connected with it will bereferred to in the context of performanceindicators described below. 1.5.3 Special PerformanceIndicators of the SupplyChainIndicators for the purpose of evaluatingan organization’s performance capability(performance indicators) should coverthe financial area as well as the

operational procedures, as the objectiveis to attain customer satisfaction at lowcosts and to ensure long-termcompetitive capability. In this sense,performance indicators are not onlyintended to contribute to the continualimprovement of the Supply Chain’sperformance, but also to further refine acompetitive business strategy. To bemost effective, the performanceindicators should be easy to define,simple to apply and easy to comprehend,in order to enable the executives whouse them to react speedily and suitablywith adequate measures.

The performance of the operatingprocedures is a substantial premise for

(external) customer satisfaction. Thefinancial performance potential, on theother hand, reflects the company’s(internal) profitability and its ability tobe competitive in the long-term. In theshort-term period, the estimation of thefinancial performance potential consistsof the measurement of incremental costper unit152 for every activity and everyproject, in addition to the measurementof non-value generating expenditure.153

In the mid and long-term, a reliableestimate is problematic. This fact can beattributed to a number of causes; forexample, the consideration of costs forResearch and Development (R&D),since R&D costs cannot be split andapplied to each individual product.

During all this, the business

executives must consider that capitalinvestors are focused upon maximizingthe capital productivity of the investedcapital, and that such a focus favorsmaximization of the profit margin andcapital turnover. Finally, they mustallow sufficient financial clearancewhilst making strategic decisions or, inother words, ensure the business has asufficient cash flow.154

Business Performance

Management seeks to ensure, within theframework of business leadership, thatthe focus is upon achievement of thedefined strategic and company

objectives. To this end performance ismeasured and monitored by means ofperformance indicators. In this case,however, not all measurementprocedures and indicators lead to theirobjectives. Many organizations arebarely in a position to cope with theamount of data, which is eitherirrelevant, too explicit, badly classifiedand of low value for decision making, oron the other hand can be difficult toobtain. A glut of information can, in fact,have a detrimental effect.

Several of the indicators definedabove may only have a nominalrelationship to an organization’s goals.They are, therefore, not relevant to the

achievement of objectives. Otherindicators can be misinterpreted,because their meaning is unclear orambiguous, resulting in wrong decisionswith far-reaching consequences. Thisleads to a management reporting systemthat is characterized by KeyPerformance Indicators (KPIs)155 andtheir application – for example, in theframework of a Balanced Scorecard ,which will be dealt with more closely inthe next section. The key performanceindicators must be seen in conjunctionwith the so-called Critical SuccessFactors, (CSF): Critical success factorsserve the purpose of identifying thesubstantial factors for the organization’ssuccess.156 These more qualitative

critical success factors are measuredand quantified by the key performanceindicators.157

Various studies have shown that

companies which objectively controland monitor their performance byindicators are more successful than thosewhich do not do this at all.158 If businessexecutives are informed of theperformance indicators and the factorsthat influence them and lead to results,they can make better and more effectivedecisions. Control of the performanceindicators must, therefore, be directedtowards the targets, problem areas anddecisive factors, in other words: thecritical success factors. The resulting

advantages allow themselves to becollected as follows:159

Better achievement of objectivesBetter and quicker decision makingAll staff are aligned to commongoalsManagers and staff have greaterconfidence and motivation.

The problems immanent to the

general performance indicators havelead to the development of specialperformance measures and metrics, usedto support companies in specific areassuch as SCM. Novack et al. havedeveloped a questionnaire for

performance measurement in thelogistics field (logistics serviceperformance). In this a differentiation ismade between ten so-called logisticsactivities and five logistics serviceoutputs. The logistic activities containthe Supply Chain’s partial processes:160

PurchasingInbound transportationPackagingInventory managementWarehousingManufacturingIntra-company transportationOrder processingOutbound transportation

Logistics design and strategicplanning

The logistics service outputs

measure the performance of theaforementioned activities and thereforerepresent performance indicators. Theseinclude:161

Product availabilityOrder cycle timeLogistics operationsresponsivenessLogistics system informationPost sale customer support.

Another possibility for improvedcontrol may be found in thedifferentiation by process performanceindicators (process measurements) andthe method used to measure theindicators (metric measurements). Theprocess performance indicators include,in the first instance, customersatisfaction. This can be measured by thecollection and evaluation of customercomplaints, thus enabling the customer tobe included in product- and procedure-orientated performance evaluations.162

A further indicator is the quality of

customer deliveries. This focuses upon aproduct’s successful delivery to acustomer, fulfillment of his expectations

and the extent to which the product isuseful to him. These customerexpectations include, as a rule, perfectorder rates as well as the delivery of theproduct to the correct location, in goodcondition and at the correct time.

Finally, there is also the timebetween order submission andrespective delivery and payment (order-to-deliver/cash cycle time). Thisrepresents that part of the cycle, whichcovers the period from the submission ofthe order up to delivery, and measuresthe amount of time which passesbetween placing the order on thecustomer side and the receipt of thedelivery/payment.163

A procedure that utilizes the range

of such cost measurement elements isthat of Activity-Based Costing (ABC).164

Aside from the classical allocation ofcost by areas of expenditure, Activity-based Costing has been particularlypopular and important in the field oflogistic services in recent years. Due tothe fact that performances within aSupply Chain often involve overlappingand cross-sectional tasks, the formationof a cost distribution by areas ofexpenditure is often difficult.Additionally, cost distributiontransparency on an internal and higheroperational level is often notpossible.165

It is therefore necessary to identify

those factors that can influence costswithin the framework of process costidentification. These influencing factorsare described as cost drivers. Thosedrivers that are themselves shaped byquantity (amount) and those that aredependent on performance aredifferentiated. The aim is to identify thecost per process implementation. Therelevant basis data is collected from thestudy of the individual activities of theprocess.166

1.5.4 Measurement ofPerformance Indicators:

Balanced Scorecard andSupply Chain ScorecardIn order to illustrate how theaforementioned performance indicatorsare respectively measured or can bemade concrete, it is useful to give anexample and explain the procedureapplied in it. The following example issupposed to show how performanceindicators in the logistics area can beclassified and identified.167 For thispurpose, reference will then be made tothe previously mentioned study byNovack et al. concerning themeasurement of logistics serviceperformance.168

The method of procedure for

measuring the performance of thedefined indicators was carried out inthree stages by means of aquestionnaire.169 The rate of return wasapproximately 1,600 executives from thefield of logistics. The companies weredistributed over a multitude of industrysectors, whereby the majority – roughlya quarter – came from the Food andBeverage industry.

The first stage consisted of theidentification of logistics activity costsand performance. The purpose of thispart of the questionnaire was to find outwhich percentage of the companiesquestioned measured the costs connectedwith performance and logistics

activities. During this the preconditionwas taken as a basis that fundamentalmeasurement of costs and servicesrepresents a necessity for quantificationof the logistic value.

The second stage consisted of theidentification of relative cost andrelative value creation. The peoplequestioned were asked in the survey toprioritize the ten logistic activity areaswith regards to their percentage of thelogistics activity costs as part of the totalof the firm’s expenditure, and therelative value generation of each activityin their respective company. A rankingof 1 was allocated to the highest relativelogistic activities cost and value

generating percentage, a ranking of 2 tothe second highest and so on, up to aranking of 10 for the lowest percentage.This was done for two reasons: firstly, itwould determine whether a relationshipexists between what a companymeasures and the relative logisticactivity costs and value generatingpercentages which are actuallymeasured. Secondly, it would helpidentify whether a relationship existsbetween the cost of an activity and thevalue generation for a business thatresults from it.

In the third and final stage, thelogistics service performance wasidentified. This part contained two

separate questions. First, the peoplequestioned were asked to specifywhether they measure the five logisticservice outputs (product availability,order cycle time, logistics operationsresponsiveness, logistics systeminformation and post sale customerservice) at all. They were then asked toallocate a ranking for the five outputswith 1 for is most important and 5 for isleast important. One of the resultsidentified within the framework of thestudy was that logistics activities aremore important from the company’spoint of view, whilst logistics serviceoutputs are more important for thecustomer.170

The problems that the study broughtto light were the trigger for a study of thethe me Performance Measurement inBusinesses of the Future that wascarried out at the beginning of the 1990sat the Nolan Norton Institute,171 which atthat time was the research branch of theconsultancy company KPMG.172 Thestudy confirmed that in addition to theproblems of redundant effort and lack ofcomparability, conventional approachesfor performance measurement restrictedthemselves too strongly to monetarymeasures, and therefore the value-generating and future-directionalprocesses, such as SC processes,received only limited consideration.173

Apart from this the studyrepresented a milestone for themodification of business performancemeasurement by the development of abalanced evaluation list, the so-calledBalanced Scorecard (BSC) . The furtherdevelopment lay in, above all, not onlyoptimizing existing processes, but alsoincluding new processes, structures andprocedures. By these means the methodgains in innovative strength.174

The concept of the BSC was

introduced by Norton and Kaplan withthe intention of contributing to thedevelopment of business objectives,building upon the support of thedefinition of strategic initiatives in order

to achieve these objectives, and finallymaking possible the measurement ofresults over the course of time. Themethod of the BSC was not completelynew at the point in time of itsdevelopment (the early 1990s), becausecompanies were already using a numberof indicators – financial as well as non-financial, tactical and operational – butthe application of such a structuredconcept, leading to an accuratemeasurement of the company’sperformance against its set objectives,was relatively new.175 The BSC thenbecame the preferred measurement toolof the largest consultancy companies.176

The method has also become an aid

for the evaluation of value-generatingstrategies, in order to monitor thesuccess of value-orientated processesand to monitor whether the involvedinterest groups (stakeholders) receivethe value they expect – for example, withregards to the Return on Investment(ROI).177 BSC achieves both thebalance and the visualization ofindicators by means of an evaluation list(scorecard). The balance aims at theequality between the followingcomponents: strategic vs. operationalindicators, monetary vs. non-monetarymeasures, long-term vs. short-termpositions, cost drivers vs. performancedrivers, hard vs. soft factors, internal vs.external processes, past vs. future

performances.178

During the visualization of the

indicators by means of an evaluation list,the company’s vision, determined bybusiness executives, remains the focus ofobservation.179 This vision must beoperationalized by strategies as well asactivities. The business vision,strategies and activities are usuallyobserved from four perspectives:180

Financial, which covers the capitalbackflow and value generation.Customer, characterized bycustomer satisfaction, customerretention, market share.

Business process, containingquality, reaction time, cost andintroduction of new products.Learning and growth, whichincludes satisfaction of co-workersand availability of informationsystems.

Each of these perspectives within

the BSC framework is itself determinedby four expressions: objectives,measures, targets and initiatives.181 TheBSC guarantees, as it were, a balancedview of the chosen financial and non-financial indicators that are necessary inorder to drive the strategic plan andmonitor the company’s performance. In

most cases, the indicators are convertedby means of databases (datawarehouses)182 and spreadsheetanalysis. These were often problematic,however, as they were often focused notexclusively on those processes that werecritical to the business success, but onthe entire spectrum of processes andsystems. Equally, the collection,aggregation and analysis of the (correct)data, was often presented in a mannerthat made sustained analysis difficult(and sometimes even impossible),because the necessary data was notalways available.

BSC has developed itself into oneof the most far reaching and recognized

methods of definition and monitoring ofbusiness strategy. According to anexemplary study on the theme ofperformance monitoring, almost half (46percent) of the people questioned statedthat their companies were implementingthe BSC method or planned to use it inthe future. It was remarkable that thediffusion was greater in the industrialand marketing sector than in the servicesector. The application of the BSC insuch cases was mainly done with thesupport of the Chief Executive Officer(CEO) or the Chief Finance Officer(CFO).183

The exact characteristic of the

Scorecard depends, in the main, upon the

business area under examination. For theSC field, a special Supply Chainevaluation list, a so-called SupplyChain Scorecard , was developed.184

The particular indicators necessary tomeasure the supply chain performancevary, depending upon customer type,product line, industrial sector, inaddition to other factors. Because theSupply Chain ultimately targets the endcustomer, the point of view of the endrecipient must be included during thedevelopment of a SC Scorecard and theidentification of the particular metrics.That consequently includes aspects thatare relevant to the capabilities of theSupply Chain: those which satisfy theend customer’s requirements in the most

cost-effective manner.185

Because development of a

Scorecard specially directed at theSupply Chain conditionally requires SCpartners to reveal business objectivesand data, the implementation is notpractical if no trust exists amongst thefirms cooperating within the SupplyChain. Therefore, a SC Scorecardshared by all parties within the SupplyChain requires a considerable degree oftrust. Simultaneously, however, thecommunal development of a Scorecardand the sharing of data associated with itcan serve to strengthen the mutual trustand the sense of partnership. Despitethis, the introduction of a Scorecard

directed at the whole Supply Chain,although theoretically desirable, isrelatively difficult to attain inpractice.186

Next to the Scorecard, the

previously mentioned SCOR modelrepresents another unified approach,especially for the measurement ofSupply Chain performance. Because theapproach is extended to cover the wholeSupply Chain, the procedures areconfigurable and the possibility ofillustrating various alternatives of thesame process exists. Through this, a“standardized” language evolves, so tospeak, for internal, intra-company, andintegrated communication, respectively,

which in turn is a substantial conditionfor the performance comparison betweenthe SC partners.

The performance of each of theprocesses in standardized Supply Chainsis measured with the aid of specializedperformance indicators.187 Theindicators used within the SCORmodel’s framework will be explored inmore detail in subsequent chapters. 1.6 Focus of the Work on theSCOR ModelIn 1996, the so-called Supply-ChainCouncil (SCC) was founded in theUnited States.188 With the Supply ChainOperations Reference (SCOR) model,

this organization created a support forthe standardization or respective“normalizing” of Supply Chains withinan organization, as well as between thebusiness itself and other organizations.The primary objective of the SCC is topromote a common understanding of theprocesses and activities in the variousbusinesses which participate in a SupplyChain network. The process categoriesin the SCOR model are differentiatedwith the aid of the dimensionsproduction concept and orientation ofproduct structure . The expressiondiscrete corresponds in this instance tothe orientation within the installation,i.e., a convergent production structure,whilst the expression process

corresponds to orientation within theprocess, i.e., a divergent productionstructure.189

The main task of the previously

represented Supply Chain Management(SCM) concept is the continuoussynchronization of value generationwithin the whole Supply Chain networkand subsequent harmonizing withconsumer demand. The SCOR model-based Supply Chain uses the SCORmonitoring processes as its foundationwithin all businesses involved both onthe supplier and customer sides.190

All conditions required to fulfill the

process stages are upheld and mutually

agreed, as a whole, by the alliedparticipants. The planning and controlmethods required to make this possibleare consequently identical to themethods used within firms for internalplanning and control. Further measuresinclude procedures necessary to accessdata between companies, especially datapertinent to inventory and capacity.191 Agreat advantage of the SCOR model liesin its definition of a common languagefor communication between the variousbusiness-internal functions and thebusiness-external Supply Chain partners.Only within a common comprehension ofthe relevant processes is the formationof customer-supplier relationshipspossible.192

The definition of indicators for the

Supply Chain performance in the SCORmodel creates the prerequisite for itscontinual evaluation and optimization.Furthermore, the comparison of SCperformances is only possible with theaid of a special comparative procedure,called Benchmarking,193 based uponsuch indicators.194 The increasingdiffusion area of SCOR modelacceptance in the USA since the late1990s, seen alongside the rapidlyclimbing number of SCC members, is anindication that a de facto standard forSupply Chain analysis is developing.With the reinforced efforts of the SCC tocreate a user basis in Europe via the

foundation of a European Chapter, theSCOR model will, on these indications,continue to be diffused throughoutEurope.195

Welke grasps the standardization

aspect and describes SCOR as anormative model. A normative modelconsists of a predefined set ofalternatives, and Welke describes howan object of the model “should be seenand behave.” The value of normativemodels lies primarily in the followingareas:196

Simplification of modeling –constrained choice vs. green field,

by means of a higher degree ofabstraction.Making model exchange possiblethroughout business units andorganizations by means ofstandardization.Description of common problemsand metrics by means ofstandardization.Exchange of benchmarking and bestpractices197 by means ofstandardization.

Normative models do not represent

anything fundamentally new and havebeen around since the 1980s. Theirorigin stems back to the so-called

Business Information Analysis andIntegration Technique (BIAIT)according to Burnstine and Soknacki.Whilst the growth in the computerindustry at this time was way aboveaverage, this growth was notaccompanied by a comparableimprovement in communication betweenexecutives in the companies applying itand their IT managers with regards to theeffective assignment of the newcomputer-supported technology. Itbecame obvious that there was a realneed to find ways of making clear toexecutives the importance of ITapplications. BIAIT was developed tosolve these communication andestimation problems.198

In addition to BIAIT there were

other, earlier, approaches to developingnormative business process models: forexample, the so-called KölnerIntegrationsmodell (KIM) (roughlymeaning Cologne Integration Model),developed by Grochla; the model of thec o m p l e t e Informationssystem-Architektur (ISA) (roughly meaningInformation System Architecture ) byKrcmar; and the Architektur integrierterInformationssysteme (ARIS) (roughlymeani ng Architecture of integratedInformation Systems) developed byScheer.199

Normative models are

predecessors of the SCOR model, in thesense that they are particularly focusedupon companies’ Supply Chains. Insteadof the term normative models, one oftenfinds the term reference model forSCOR. This will be dealt with moreclosely in Chapter 2.200

The normative models presently

available allow themselves to besystemized according to the followingthree categories:201

The modeling viewpoint, i.e., to do with

a structural or behavioral modelThe E-Business-field, i.e., to do with

Business-to-Business (B2B),Business-to-Consumer (B2C) or

Government-to-Consumer (G2C)202

The commercial branch, i.e., to do withbusinesses from industry, marketing orthe public sector.

This study focuses upon industrial

companies, because the author haspractical experience in this commercialfield. Furthermore, it focused upon theE-Business field (B2B), because it canbe assumed that the greatest scope forcompetitive success resides there.203

Apart from that, this field has by far thegreatest influence upon the formation andmonitoring of Supply Chains, or,alternatively expressed, the massivegrowth in the B2B field has, without adoubt, highlighted the importance of

SCM.204

With reference to the models

available to industry for B2B, there ispresently only one model that can betaken seriously, namely the SCORmodel. Further models for E-Businessare described in literature, but have notbeen comprehensively documented, ordo not represent a normative model inthe sense introduced here.205

For these reasons, this work will

focus upon the SCOR model, which hassuccessfully entered into use in thepublic sector area and gained inimportance over its time there. This doesnot, however, refer to the previously

illustrated E-Business area Government-to-Consumer (G2C), but rather more tothe Government-to-Government (G2G)and Government-to-Business (G2B)areas. The aforesaid arrangement cantherefore be expanded by these businessareas in which the SCOR modelpresently takes a primary position, aswill be made clear.206

1.7 Analysis of Supply ChainProcesses by Use of theSCOR ModelRazvi explains the essential character ofSupply Chain Planning and Analysis asfollows:

“Business today has evolvedso that competition is betweenwhole supply chains ratherthan individual companies.Selecting a few targeted keyperformance indicators canhelp a company to concentrateon its supply chain goals.Choosing the wrongindicators, on the other hand,could lead to a decline insupply chain performance. Inaddition, analyzing the supplychain based solely onindividual events can have theopposite effect, causingturbulence in the supplychain.”207

The statement is made with

reference to the degree of importanceattributed to the analysis of the SupplyChain and its performance. Hugosdefines five so-called supply chaindrivers, which dominate the SC’sperformance potential. Each of thesedrivers has two competences (orrespectively, and more precisely, SupplyChain competences, as in the submittedcontext):208 The performance capabilityand the efficiency. The five drivers andthe two expressions are connected in thefollowing way:209

Production: This driver can be

arranged highly capable from aperformance point of view, forexample, by the construction ofadditional factories showingsurplus capacities and use offlexible manufacturing procedures,in order to produce a largeassortment of products. If efficiencyis sought, a firm can build factorieswith low surplus capacity andoptimize the factories for themanufacture of a limitedassortment.Inventory: Performance capabilitycan be achieved here by holding ahigh amount of inventory stock for alarge assortment of products.Efficiency, with regards to

inventory management, woulddemand reduction of inventoryamounts for all products –especially those which show a lowturnover rate.Location: A location approach thatemphasizes performance potentialwould be seen, for example, wherea computer firm opens manybranches to be physically close tothe customer base. Efficiency canbe achieved by serving all thecustomers from only a fewlocations and thereby centralizingactivities.Transportation: Performancecapability can be achieved by atransportation mode that is fast and

flexible. Efficiency can beachieved by transporting productsin collective deliveries (batches)and by fewer deliveries.Information: The influence of thisdriver grows continuously, whilstthe technology for collection anddistribution of information spreadsincreasingly, is simpler to use andbecomes less expensive. A highperformance potential can beachieved by companies with theaccurate and up-to-date collectionof data, whereby that task isrepresented by the four driverspreviously mentioned. If theobjective is greater efficiency, itcan be achieved by the collection

of a smaller amount of data,resulting in a reduction in theassociated and required activities.

The following illustration combines

these connections once again in agraphical manner:

Diag. 1-2: Connection between Supply Chain driversand Supply Chain competences.210

Both of the Supply Chain

competences named above – capabilityand efficiency – will be examined moreclosely in due course. Both aresubsumed under the term Performancein this study and measured by theillustrated KPIs.

1.7.1 Efficiency of theSupply ChainEffectivity is defined in business scienceas the degree of objective achievementand is consequently a proportionatemeasure for work performance (output).It is therefore about doing the rightthings.211

Efficiency as a possible sub-

objective of effectivity represents arelationship between input-variables andoutput-variables and can therefore serveas a yardstick for resource efficiency.212

Thus, it is about doing the things right.Effectivity implies, for example, anattractive price-performance ratio for

the customer, competitive advantages inthe usage-related quality elements, apoint of entry into the market whichconforms to the objective, or a means ofmarketing products in accordance withor in excess of the planning level.Activities are considered efficient ifthey accompany relatively low cost, arelatively short development period orthe use of synergy effects. Together,effectivity and efficiency influencecommercial success.213

In a commercial business,

efficiency is therefore respectivelydetermined or reflected mainly by costs.In connection to the costs within theSupply Chain, one also speaks of Supply

Chain costs. SC costs can be recordedwithin the framework of a target costingin supply chains.214 Target costing inSupply Chains expands the marginalcosting approach to cover the wholeSupply Chain. The scope ofconventional cost control is a singlefirm. The fundamental idea of costcontrol within a Supply Chain is toextend the cost control approach tocover the whole Supply Chain, whichrequires an approach that goes aboveand beyond organizationalboundaries.215

The inter-organizational cost

control resulting from this represents anapproach that seeks to monitor costs and

the profits resulting from them.Synergies are thereby used, which existthroughout several firms in a SupplyChain. Traditional cost controllingsystems are only partially successful inensuring an exact analysis of the costsbeyond the domain of production. Asopposed to this, activity-based costingsystems216 assist organizations toallocate costs associated with supplierand customer relations more closely.This supplier- and customer-orientatedcost information enables firms toidentify opportunities to increase costefficiency of their market relationswithin the Supply Chain.217

SCM has substantially contributed

to lowering operational inventorybuffers and costs pertaining tomanufacturers, wholesalers andretailers. Firms, for example, whichparticipated in an initiative by theMassachusetts Institute of Technology(MIT)218 under the name IntegratedSupply Chain Management Program(ISCM)219 reported a significantimprovement with regards to theirSupply Chains. According to this, firmsreduced inventory buffers by half,increased on-time deliveries by 40percent, and reduced the percentage ofnon-deliverable products to a fraction,simultaneously doubling the rate ofinventory turnover.220

The business consultancy Pittiglio,Rabin, Todd & McGrath (PRTM) 221

discovered in a study intended tocompare Supply Chain performance(Supply Chain Benchmarking Study)that leading Supply Chain companiesinvest on average between three percentand seven percent less of their profit forthe management of their Supply Chain astheir competitors. This degree of costeffectivity directly improves thepercentage in trading margin or createsan opportunity of permanently loweringprices. As an example, leadingcompanies within the food industryreport about five percent lower SC coststhan their competitors. In that industry,an increase of five percent in the margin

(or a consequent permanent decrease inprice, if the savings are – at leastpartially – passed on to the endconsumer) is of great importance.Having said that, the trading margin forthe typical retailer comprises less than ahalf of cost savings achievable byleading businesses within the SCMarea.222

Stock-keeping223 is a substantial

component of Supply Chain costs, whichunder certain circumstances can quicklyform a high percentage of the totalinventory value. In the computerindustry, for example, a rough estimateof the annual stock-keeping costs can beidentified by the combination of the

capital costs (10 percent) and priceerosion (25 percent), which equate to 35percent of the net asset value. A goodmeasure for orientation can be identifiedby the calculation of stock-keeping costsover a 10 day period. The formula forthis is as follows: 10 days multiplied by35 percent, divided by 365 days, equals1 percent. This means that a respectivereduction or increase of the storageduration of 10 days leads to a respectiveone percent improvement ordeterioration of the backflow ofgoods.224

For many firms, stock-keeping

costs have been a substantial impulse forimplementing value innovations in the

sense of a new offer, which givescustomers a significant increase inadded value. Stock-keeping costs consistof the following components:225

Obsolescence, i.e., price erosion,wear-and-tear.Lost salesPersonnel costs, i.e., work inducedby stockFixed assets, i.e., storage space andaccessoriesInsuranceAdministration, i.e., stock checkingand costs for informationtechnologyCapital costs, i.e., raw materials,

finished products and goods whichfind themselves in production.

Stock-keeping costs have been

identified as one of the main cost driversand have a substantial influence upon acompany’s profitability. The primaryfactor in positively influencing stock-keeping costs is, therefore, stock-keeping management, which representsan integral component within SupplyChain monitoring.226

1.7.2 PerformanceCapability of the SupplyChainAccording to Bovet and Martha, the

capability in the Supply Chain contextincludes reliability and flexibility. Theydescribe this connection as follows:

“Reliability is an importantdimension of world classservice. Reliability meanspredictable, on-time deliveryof perfect orders, as expectedby the customer. A perfectorder is one that is shipped ontime and complete, but moreimportant, one received at thecustomer’s desired locationwithin a precise time window,in excellent condition, andready to use. It also includesthe flexibility to respond to

last-minute changes by thecustomer at equally highservice level.”227

The associated improvements

directly or indirectly influence a SupplyChain’s performance capability. Themeasurement of these indicators plays,as already explained in section 1.5, acentral role within the framework ofchange implementation. It constitutes akind of “strength effect” and drives theactivities onwards. The implementationof effective measurement proceduresbrings with it permanent challenges forbusiness executives. It requires not onlythe restructuring of existing performancemeasurement procedures, but also the

establishment of a structured process formonitoring the Supply Chain.228

Evans and Danks extract the

process of value creation by means ofstrategic Supply Chain monitoring (valuecreation through strategic supply chainmanagement) straight from the value thatis achieved for the company’sshareholders (Shareholder Value,SV).229 Building upon this, they definethe so-called Shareholder ValueApproach.230 The approach focusesupon the businesses’ value or,respectively, the advantages of thisvalue for the shareholders of a business.The desire to positively influence the SVhas in many cases represented the

starting point for Supply Chainimprovement. The Supply Chain canimmediately influence the profitabilityas well as invested capital via thefollowing determinants:231

Profitability:232 On the one hand, itincludes revenue in the sense of ahigh market share, larger tradingmargins and higher productavailability. On the other hand, itcontains cost with the objective oflower costs for sales,transportation, stock-keeping,material movement and distributionplanning.Invested capital:233 This

comprises, on the one hand,working capital with the objectiveof lower stocks of raw material andfinished products, in addition toshorter payback cycles. On theother hand, it comprises fixedcapital with the primary intention ofbinding less capital into capitalgoods (for example, vehicle pool,warehouses, accessories for themovement of materials).

Repeated references will be made

to the determinants for Supply Chainperformance in the chapters that follow,because they also find a role within theframework of the SCOR model. Duringthis study recourse will often be made to

the division of performance potentialinto performance capability on the onehand – as a respectively external orcustomer-related component – andefficiency on the other hand – as arespectively internal or business-relatedcomponent.234

Chapter Two

The Supply ChainOperations Reference

Model (SCOR model) ofthe Supply-Chain

Council This chapter focuses primarily upon thecontext of discovery as defined byFriedrichs within the framework of aresearch-logical course.

Under context of discovery,

the motive that leads to aresearch project isunderstood. The motives aredifferent in their startingpoints, leading to anexamination. They all refer,however, to socialproblems.235

2.1 Origin and Objectives ofthe SCOR Model2.1.1 Intention of the SCORmodelThe Supply-Chain Council (SCC)236

was founded with the aim of creating an“ideal” model of the Supply Chain. Forthis purpose, the Supply Chain

Operations Reference Model (SCORmodel) was defined as a standardizedprocess reference model of the SupplyChain, and has been continuouslyenhanced. With the SCOR modelproviding a unified description, it istherefore possible to consider analysisand evaluation of Supply Chains not onlybetween one company and another, butalso across sectors of wider industry.The SCOR model is used in threeexercises:237

1. To evaluate and compare Supply

Chains’ performance potential2. To analyze and, if necessary, optimize

integrated Supply Chains throughoutthe partners within the logistic chain.

3. To determine suitable places for theassignment of software and itsfunctionality within the Supply Chain.

The initial concept of the Supply

Chain is that every production andlogistics network can be described usingfive fundamental base processes.238

With each of the four main executionprocesses – source, make, deliver andreturn – materials or products are usedor transported. By joining theseprocesses into a chain it is possible todefine customer-supplier relations andfactor in the fifth base process, that ofplanning. If one combines all the mainprocesses the result is a complete modelof the production and logistics network.

The description of these fundamentalprocesses within the Supply Chain is asubstantial component of the SCORmodel.239

Another factor here is the definition

of metrics for the evaluation of theprocesses’ performance within theSupply Chain. This definition isimportant as it can be used to form thebasis of a performance comparison(Benchmarking) either with othercompanies or other Supply Chains in thesame industry. For the main processesthe SCC members compiled the bestknown methods for achievement of highperformance, the so-called BestPractices,240 and integrated them into

the model.

Finally, the SCC also addedsoftware system requirements into themodel, which are helpful with therealization of these practices.241

Collectively the intention of the SCORmodel can therefore be described asfollows:

“The Supply-Chain Councilhas published a SCOR modelthat describes the (supplychain) at multiple levels ofdetail, identifies bestpractices, and definesassociated KPIs242 for eachprocess. Organizations are

beginning to leverage SCORstandards to drive consensuson terminology, processes,and expectations amongtrading partners.”243

2.1.2 Descent of the SCORmodelThe SCOR model was developed andpromoted by the SCC as a pan-industrystandard for Supply Chain monitoring. The SCC was founded in 1996 by theBusiness Consultancy agency Pittiglio,Rabin, Todd & McGrath (PRTM) 244 andAdvanced Manufacturing Research(AMR),245 and originally included 69voluntary member firms. Of equal

importance for the SCOR model’sdiffusion are the respective inputs ofmanufacturers and implementers ofsystem technologies, researchers andscientists, and governmentalorganizations. All of these groupsparticipate in the SCC’s activities and inthe development and enhancement of themodel. By the beginning of 2006, theSCC had more than 1,000 membersworldwide and had branches in NorthAmerica, Europe, Japan, Australia/NewZealand, Southeast Asia and SouthAfrica.246

The SCC is greatly interested in

promoting the widest possible diffusionof the SCOR model with a view to

building better customersupplierrelationships. It is also interested in theimprovement of its members’ softwaresystems through the use of mutualmetrics and terms. Apart from this, thegoal is to quickly recognize and adoptbest practices, regardless of theirorigins.247

Whilst much of the model-based

content has been used by practitionersfor years, the model offers a specialframework within which businessprocesses, performance indicators, bestpractices and system technologies can belinked with one another. The result is aunified structure for both supportingcommunication amongst the Supply

Chain partners and increasing theeffectivity and efficiency of SupplyChain monitoring and other activities.248

Member firms pay a small yearly

subscription in support of the SCC’sfunctions. All who use the SCOR modelare asked to make reference to the SCCin documents or representations applyingto the model, in addition to all cases ofits application. Additionally, membersare urged to regularly visit the SCC’sinternet page249 and make themselvesfamiliar with the latest informationavailable in order to ensure that they areusing the latest version of SCOR.250 TheSCOR model represents, in a transposedsense, the SCC’s consensus with respect

to the management of the SupplyChain.251

2.1.3 Structure andprocesses of the SCORmodelThe five basic management processes orchevrons, which form the SCORmodel’s basis, are Plan, Source, Make,Deliver and Return. In addition to thesefive main processes, which form theorganizational structure of the SCORmodel, the following three process typescan be differentiated:252

Planning: A planning element is a

process that adjusts the expectedresource need to the expecteddemand conditions. Planningprocesses balance out the aggregatedemand over a certain planninghorizon. Planning processes usuallytake place at regular intervals andcan contribute to Supply Chainreaction times. This type of processis referenced to the above-namedmain process Plan.Execution: Execution processes aretriggered by planned or actualdemand, which changes thecondition of a product. Theyinclude dispatching and sequencing,changes in materials and servicesand product movement. This type of

process therefore incorporates theaforementioned main processesSource, Make, Deliver and Return.Enable, formally known asInfrastructure: Enabling processesare responsible for the preparation,maintenance and monitoring ofinformation or relationships, uponwhich the previously-mentionedplanning and execution processesdepend.

The following illustration collates

the model’s structure in graphical form.

Diag. 2-1: Arrangement of the SCOR model aroundfive main Business Management Processes.253

In accordance with the illustration,

the model includes an organization’sown Supply Chain and its respectivefive basic processes. Beyond this,however, it can also span the customers’Supply Chains on the one hand, as wellas those of the suppliers on the other. Totake the process a stage further, thesupplier’s suppliers and customer’scustomers can be included. In this sensethe model contains all interactions withcustomers, from order entry up to the

paid invoice. Furthermore, it comprisesall products, i.e., physical, material andservices, from the supplier’s suppliersright up to the customer’s customers,inclusive of equipment, accessories,spare parts, and software. Finally, ittakes into account all interactions withthe market – beginning with theunderstanding of demand as a whole,right up to completion of the order.254

The model’s notation is

predetermined and follows consistentconventions in the processes’descriptions:

The letter P stands for Planelements

The letter S represents SourceelementsThe letter M stands for MakeelementsThe letter D represents DeliverelementsThe letter R stands for Returnelements.

These main processes can also take

the form of enabling processes. In thatcase the respective process is prefixedwith an E, which indicates that theresulting process represents an Enableelement. Example: EP represents anenabling element within the planningprocess.255 Within the main processes

there is also a universally validstructure, whereby the model focuses, soto speak, upon the product environment.This is shown in the following example,which represents the manufacture orMake process:256

Make-to-Stock – M1Make-to-Order – M2Engineer-to-Order – M3Retail Product – M4.

The assignment is formed

respectively with reference to theprocurement process Source:257

Source Make-to-Stock Product –S1Source Make-to-Order Product –S2Source Engineer-to-Order Product– S3.

An analogue also applies for the

process Deliver:258

Deliver Make-to-Stock Product –D1Deliver Make-to-Order Product –D2Deliver Engineer-to-Order Product– D3.

T h e Return process inevitably

deviates from this and is distinguishedby the following sub-processes:259

Return Defective Product – R1Return Maintenance, Repair orOverhaul – R2Return Excess Product – R3.

The respective enabling elements

are also described within each sectionof the planning and procedure processes.In this case, the format shown above isalso applicable in the description andgraphical illustration.260

The following illustration, takenunchanged from the model descriptionreleased by the Supply-Chain Council(SCC), gives a collective overview ofthe associations and underlines onceagain the fact that the model spans allprocesses from the supplier right up tothe customer.

Additionally, in the case of SCORwe are dealing with a hierarchicalmodel with several levels. Thecompany’s Supply Chain itselfrepresents the starting level (level 1).The main process level following this,i.e., Plan – P, represents the secondlevel. This is shown by a single numberand is followed by the target item of the

main process, i.e., P1 – Plan SupplyChain. The exact number can beextracted from the relevant positionwithin the model’s structure. Furtherdown from that is the third level, wherethe respective concrete process stagesare located, i.e., P1.1 – Identify,Prioritize, and Aggregate Supply ChainRequirements.262

The following illustration, also

taken unchanged from the SCC’s modeldescription, represents the associationsin graphical form using the processPlan. Reference is made here to theplanning process contained in Diag. 2-2,namely P1 – Plan Supply Chain, whichis allocated to the second level and its

sub-processes represented on thefollowing third level. In this senseanalogue representations exist within themodel description for all main processesand their relevant sub-processescontained in Diag. 2-2. Further levels,i.e., those below the third level are,however, not included in the model,because they are of industry-specificcharacter and would therefore contradictthe basic concept of SCOR – that itrepresents an industry-spanningmodel.263

Diag. 2-2: SCOR model structure261

The processes from the fourth level

onwards prove themselves to be soindustry-specific and upon increasinglevels even company-specific thatstandardization is no longer realistically

possible. The fourth, and all following,levels represent the object ofimplementation projects, whereby thefourth level refers to task, the fifth levelto activities and the sixth level refers toinstructions.264

The SCC’s SCOR model

documentation contains seven basicsections: An Introduction, a section foreach process of the second level (Plan,Source, Make, Deliver, Return), as wellas a Glossary.

Diag. 2-3: SCOR process stages by example of theprocess Planning (Plan)265

For reasons due to Supply Chainmodelling, the basic process Return islisted in connection with two furtherbasic processes: Source and Deliver.

The process of returning to suppliers,i.e., the return of raw materials, isdocumented as Source Return activity.The process that connects anorganization to its customers, i.e., refersto the receipt of returned finished goods,is documented as the Deliver Returnactivity. This stems back to the SCORSupply Chain’s fundamental thoughtrepresented in Diag. 2-1, whereby themodel incorporates everything from thesupplier right up to the customer.266

The planning and execution

processes represent the center of thedocumentation, whilst the glossarycontains a list of those standard processand metric terms used within the

document. The sections that occupythemselves with the types of planningand procedural processes are organizedin the form of a unified structure: At thebeginning of each section is anillustration, which contains a visualrepresentation of the respective processelement, the relationships of suchelements to one another, and anyrelevant incoming and outgoinginformation (for an example see Diag. 2-3 illustrated above).

Tables with text follow theillustration, and comprise the followinginformation in the order mentioned:267

Standard name of the process

element, i.e., Process category –Plan Supply ChainNotation of the process element, forexample Process number – P1Standard definition of the processelement. Example: Processcategory definition with thefollowing description: Thedevelopment and implementation ofprocedures for resource allocationover a given time span, in order tocomplete certain Supply Chainrequirements.Performance Attributes associatedwith the process element. Example:Performances attribute Reliability,metric Delivery performance.268

Best practices for each respective

special process. In this case suchbest practices are examples, but nota complete listing. This sectionalso includes specialcharacteristics or respectivepossible features that can contributeto an increase in performance. Anexemplary best practice is that theSC process should possess a higherdegree of integration, starting withcollection of customer data right upto receipt of the customer order andthroughout production, right up topurchase requisitions uponsuppliers. A possible arrangementto this end could take the form of anintegrated SC planning system269

with interfaces to all supply and

demand sources by means of IT-based systems.

In a similar manner to the process

elements, the performance attributes andmetrics are built-up hierarchically.Although not explicitly represented inthe model, they are typically assigned tothe first level of the respective planningprocess (i.e., P1 – Plan Supply Chain).From there, and following the hierarchy,they become decomposed and assignedto the respective planning, execution andenabling elements.270 This will be dealtwith more closely below. 2.1.4 Performance attributes

and Level 1 MetricsLevel 1 Metrics are primarily forms ofmeasured data at a higher level, whichcan extend themselves through severalSCOR processes. These metrics do notinevitably and explicitly refer to one ofthe SCOR basic processes of the firstlevel (Plan, Source, Make, Deliver, andReturn).271 The metrics can rather beseen in conjunction with the performanceattributes. In the present version of theSCOR model (Version 8.0 as atbeginning of 2007) the following fiveperformance attributes are used:Reliability, Responsiveness, Flexibility,Costs, and Asset Management.

Each of these performance

attributes directly refers to the SupplyChain, which is why the prefix “SupplyChain” can be added (for example,“Supply Chain Reliability,” etc.).272 Theillustration below which is takendirectly from the model description bythe SCC gives an overview of thePerformance Attributes used within theSCOR model. In order to operationalizethe performance attributes, they must beconnected in a further stage with theMetrics Level 1. For example, the metricfor order fulfillment lead time can becoupled with the performance attributeresponsiveness.

The performance attributes arecharacteristics of a given Supply Chain

for analysis and comparison with otherSupply Chains with competingstrategies. Without such characteristics itwould be extremely difficult, forexample, to compare an organizationwhich follows a low-cost strategy to onewhose objective is the highest possiblelevel of delivery reliability.273

As described above, the

performance attributes are connected tothe metrics of the first level. The latterrepresent measures that enable anorganization to calculate how successfulit is with regards to the achievement (ornot) of its desired position within thecompetitive market place. Although theperformance attributes are critical for

the application of the model, formaldefinitions were only integrated intolater versions. For example, standardperformance attributes were introducedinto Version 4.0 of the model.275

In Version 5.0, the process

descriptions which are assigned to theactivities of the second and third levelswere adjusted in order to ensure that themetrics used actually measure theirintended objects. These modificationsare two examples which show how theSCOR model originated through aniterative process and, even now, isconstantly revising itself. Thisundoubtedly represents one of the greatstrengths of the model.

Diag. 2-4: Connection between SCOR performance

attributes and metrics of the first level.274

The metrics used are of a

hierarchical nature similar to that of theprocess elements. The metrics of thefirst level result from aggregatecalculations, which in turn are basedupon the metrics of the levels below

them (level 2 and so on). For example,the delivery performance is calculatedas the total amount of products deliveredpunctually and completely. Beyond this,metrics are also assigned on a lowerlevel in order to diagnose deviationsbetween the performance and the plan. Itcan therefore be thoroughlyadvantageous for an organization toexamine the correlation between therequested delivery date (request date)and the approved delivery date (commitdate).276

2.1.5 Changes in SCORVersion 6.0As previously explained, the SCORmodel originated through a type of

evolutionary process and went throughseveral revisions from version toversion. In due course, SCOR Version 6.0 findsitself applied mainly with respect to theassociated performance indicators. Thefollowing lines deal with its maindifferences from the previous version,an understanding of which is necessaryin order to be able to judge the model’sevolution. Version 6.0 of the SCORmodel represents the sixth substantialrevision since the introduction of SCOR.Model revisions are normallyimplemented when the members of theSCC deem that changes are necessary inorder to promote the continued effective

usage of the model. When the committeeresponsible for the metrics announcedthat the metrics of the first level did notconsistently correspond with the mainprocesses on the first level it becamenecessary to prepare a revised model.The shortcomings were mainly correctedin Version 6.0, but minor changes wereannounced for the later Version 7.0.277

In Version 6.0 changes were

implemented in three primary areas:Retail processes, Return processes andElectronic Business (E-Business). Thedelivery processes were extended withregards to the sales processes andexpanded by a new process element, D4– Deliver Retail Product. This

extension makes allowance for specialfeatures with reference to the activitiesand their sequence which are associatedwith the delivery (normally to the endconsumer).

Within the delivery return process,the process element R 2 – Return ofMaintenance, Repair and OverhaulProduct has been reconfigured afterover one year of assignment in order tobetter reflect the processes in practice.The processes associated with the returnof the said products (SR2, MR2), havebeen brought up-to-date for better useand the definitions associated with themhave been improved accordingly. In thisversion of the model, only the SR2 and

the DR2 elements have been revised. Itwas envisaged that revisions to coverthe SR1-, DR1-, SR3- and DR3-processes would feature in one of thenext versions.278

Regarding the concept of Electronic

Business (E-Business),279 best practiceswere included in the manufacturingprocess. This represented a continuationof the inclusion of best practices, whichwas introduced in Version 5.0. Duringthe examination of the effects of newtechnologies upon Supply Chainmonitoring, the SCC came to theconclusion that although the appliedtechnologies have altered, thefundamental processes associated with

the Supply Chain have remainedunchanged. The assigned best practiceshave, however, changed substantiallydue to the influence of new technologies.

In its overhauling of the bestpractices and relevant technologicaldescriptions, then, the updated versionof the SCOR model represented theformal recognition of tried E-Businessmethods and E-Business technologies bythe SCC.280

2.1.6 Changes in SCORVersion 7.0In SCOR Version 7.0, which has beenpublished by the Supply-Chain Councilin March 2005, changes were

congregated in two areas.281

Firstly, the application of the

performance indicators has beensimplified. For this purpose the firstlevel performance metrics were re-configured and their fundamentalstructure carved out more elaborately.The number of first level metrics wasreduced from thirteen to nine. This didnot, however, mean that the respectivemetrics have gone altogether. They have,rather, been allocated to the performancemeasure level below it. A consequenceof this restructuring was that theprocesses within the SCOR processelements Deliver had to be adjusted. Therespective processes on the third level

were therefore extended in order toguarantee a better alignment with thecycle time and the cost-specificperformance metrics.282

Beyond this, a new section added

to the performance indicators has beenattached as an additional appendix,which described the individual metricsand their calculation in detail.Additionally the explanation of themetric’s influence has been substantiallyexpanded. Only the metrics of the firstlevel are contained in the Appendix toVersion 7. The question of whether ornot future SCOR model versions wouldalso include similar detaileddescriptions for all performance

indicators was under construction at thistime.283

The second area in which changes

have been made was that of the bestpractices. Here a number of newprocedures were added. In particular,twelve new best practices wereincluded, of which four were alreadycontained within SCOR Version 6.1, butnot explained in detail. The newprocedures were listed in the Appendix,and discussed and dealt with in detailthere. Whilst the Appendix to SCORVersion 7 contained those best practiceswhich were newly included or changed,future versions of the model wouldlikely include the list (and associated

explanations) within the model itself.284

2.1.7 Changes in SCORVersion 8.0Version 8.0 represents the most up-to-date version of the SCOR model.285 Itfeatures a number of fundamentalrevisions, whereby the processes of thefirst, second and third level remainunchanged from the previous Version7.0. The main changes are in the areas ofperformance indicators, best practices,illustration of inputs and outputs,workflow diagrams and the SCORdatabase.286

With reference to the performance

indicators, an additional Level 1 Metric

– namely Return on Working Capital –can be found within the performancea t t r i b ut e Assets. Several furtherperformance indicators have beensimplified. The second level processesnow exclusively have performancemeasures of the first level assigned tothem. Apart from this an additional costmeasure for each individual process hasbeen integrated. The adoption of a cycletime measure for each process wassimilarly pursued in Version 7.0. Bythese means, it is possible to aggregatethose two measures to the first levelperformance metrics. In order toillustrate the aggregation possibilitiesand fundamental hierarchy, a completelynew appendix for the performance

indicators (Metrics Appendix) has beenincluded in the SCOR model’sdocumentation.287

The Best Practices Appendix was

also revised with the objective ofcreating a clear and consistent point ofreference. Changes to several definitionswere brought up to date, and theirassignment to the correspondingprocesses was changed accordingly. Thebest practices no longer contain thecolumn Feature, as this is a remnant ofthe time when the model description stillcontained associated softwarecharacteristics, which are no longeridentified by the Supply-Chain Council.The respective column has, where

necessary, been replaced by adefinition.288

The revised model description also

includes the new Inputs and Outputs,along with their definition. These werenot respectively present, described orreferred to in any of the previousversions of the SCOR model. Thiselement was created by a group withinthe SCC that assigned the ISA-95standard289 by the ISA organization290

in conjunction with SCOR. Thecorresponding definitions were insertedin order to fulfill the requirements on theSCOR side as well as those of ISA-95.291

Due to the first-time adjustment ofthe SCOR model to a form of illustrationthat is compatible with Business ProcessManagement (BPM),292 the WorkflowGraphics are markedly different fromthe earlier ones, which were bound tothe Microsoft Word format. They nowalso include the work or tasks to becarried out (deliverables), i.e., thoseelements that move from one process to,or respectively into, the next (input) andback out of this process (output). As aresult of these complex work proceduresand the fact that the illustration is in aspecial software program replacingMicrosoft Word, the font size in thework procedure diagrams is very small.The diagrams are therefore available on

the Supply-Chain Council’s internetpage293 in a Hyper Text MarkupLanguage (HTML)-format with anenlargement function.294

It is envisaged that the next SCOR

version – probably Version 8.1 – willillustrate and offer the data stored inBPM-compatible format within theSCOR database in a format that isindependent of the manufacturer. Atpresent the SCC is working on thissuggestion with the respective systemsuppliers. In addition to this, a softwarelicense program is being consideredwith regards to the release of futureSCOR model versions in an electronicformat.295

In this study references to the

SCOR Model are to Version 8.0. Theonly exception to this is the issue ofperformance indicators, where Version6.0 will be used. This is becauseVersion 6.0 was the up-to-date model atthe time of the theses model’scompilation and as a result of this, theresearch is based upon the performanceterms within Version 6.0. The validity ofthe evaluations is, however, notinfluenced by this because, as mentionedabove, no performance indicators wereremoved or added. The only change hasbeen in the position of the performanceindicators within the model’s hierarchy.

2.2 Limitations of thePractical Areas of the SCORModel’s Application asDescriptive Model for theAnalysis of Companies’Supply ChainsAs already noted, the SCOR model wasdeveloped to describe businessactivities relevant to the Supply Chain,which are linked to all phases that arerun through in order to satisfy customerrequirements. The model is characterized by five basicprocesses. By illustrating Supply Chainsusing these process building blocks and

generally valid definitions, the modelcan serve to describe Supply Chainsboth of a very simple or verycomplicated nature.296 In describing, asit were, the “depth” and “width” of anychosen Supply Chain, the model hasbeen able to contribute to delivering abasis for SC improvements for global aswell as location-specific projects.297

As shown in the next illustration,

the SCOR model represents a so-calledBusiness Process Reference Model .Reference models are considered to benormative models as represented inChapter 1.298 With SCOR, we aredealing with a special model, whichconnects process elements, performance

indicators, best practices and thespecialties relevant to theimplementation of Supply Chainactivities in a very distinctive way. Thesingularity and effectivity of the modeland its successful application are mainlybased upon the concentrated andregulated assignment of these fourelements.299

Reference models are principally

used to systematize business processesand to represent them in a unifiedmanner. The SCOR model builds uponthe input, throughput and output schemethat is used within the processmonitoring framework. The model isused to represent the processes on

various levels and to determine theirformulation in stages.301 The SCCdefines the term reference model asfollows:

“Process reference modelsintegrate the well-knownconcepts of business processreengineering, benchmarking,and process measurement intoa cross-functionalframework.”302

Reference models are based upon

workflows and the monitoring of theseworkflows (Workflow Management ).303

They identify the interfaces within thestructure of the work procedure which

enable products to interact on a varietyof levels. All systems for monitoringwork procedures contain a number ofuniversally valid building blocks, whichinfluence each other within a defined setof scenarios and work together. Variousproducts typically show a difference inlevels of performance within theuniversally valid building blocks.

Diag. 2-5: SCOR as a hierarchical model300

To achieve interoperability

between the various work procedures, itis necessary to determine a standardizednumber of interfaces and formats for theexchange of information. This can take

place by the assembly of unambiguousinteractive scenarios with reference tothese interfaces. The interactivescenarios in turn serve to identifyvarious levels with functionalconcurrences, which are in line with theproduct range found on the market.304

In addition to this, a reference

model represents a Supply Chain modelthat can support the introduction ofapplication systems.305 The advantagesof a reference model result, in thiscontext, from the ability to enable thedetailing of several observation levelsand methods of questioning. Firstly, thisincludes the description of processconditions and process results, i.e., the

answering of the questions as to whichdata, information and resources are usedand which objects are beingprocessed.306 Secondly, it contains thedescription of the associated procedurefrom a process point of view, i.e., theanswering of the questions as to whichpartial processes and results pilot theprocess and which organizational areasare involved.307

An important point in this context is

that the model explicitly describesprocesses and not functions. Expressedanother way, the model concentratesupon the activities involved rather thanthe people or organizational units thatcarry out these activities.308 The relevant

process is shown in the followingillustration.

Process Decomposition Models,whose intention deviates considerablyfrom the formerly mentioned processreference models, must be clearlydifferentiated. The SCOR modelprovides the service of a language forthe communication between SC partners.Process decomposition models are, onthe other hand, designed to observe aspecial configuration of processelements. Therefore they are missing theintegrative character – with regards tothe business-internal as well as thebusiness-integrated Supply Chain.310

Electronic Business (E-Business)has risen in importance as a newapplication domain in conjunction withreference models in recent years.311

According to Fettke and Loos, referencemodels for E-Business are those whichsupport the formation of E-Businesssystems.312 Following this, the SCORmodel can also be understood as an E-Business reference model, because itsapplication can determine acomprehensive application ofinformation technologies.

Diag. 2-6: SCOR as an activity-orientated referencemodel for business processes309

2.3 Strengths andWeaknesses of the SCORModel Based Upon thePresent Discussion2.3.1 Strengths andpotentials of the modelOne of the great strengths of the SCORmodel is its capacity to predict bothduration and costs, particularly when it

is implemented within the framework ofa Supply Chain analysis project(described in the following course asSCOR project in short). SCOR projectsare often formulated with the followingmeasures in mind:313

The improvement of a company’sstock market valueThe increase of profits and marginsThe increase of the availablefinancial means by implementationof investments (i.e., IT investments)The reduction of costsThe optimization of EnterpriseResource Planning (ERP).314

Handfield and Nichols provide a

good summary of the more qualitativeadvantages in connection with the use ofthe model:

“The major benefit of SCORis that it givesinterorganizational supplychain partners a basis forintegration by providing them,often for the first time, withsomething tangible to talkabout and work with.”315

In addition to qualitative

improvements, such as improvedcommunication between the operationalareas, the model can also be used to

achieve the following (exemplary)quantitative results:316

The improvement of operatingresults of an average of threepercent in the initial project phaseby means of cost reduction andimprovement in customer service.An increase (of between twofoldand sixfold) in profitability317 withregards to project investment costswithin the first twelve months. Thisis often in conjunction withimprovements that compensate forcosts inside the first six months.A reduction of expenses forinformation technology (IT) through

minimizing system customizationsand making better use of availablestandard functionality.

The continual actualization of theproject’s portfolio318 by continuousconversion of Supply Chainimprovements with the objective ofincreasing annual profits by one tothree percent.

Hughes et al. specify the following

typical and respective areas for potentialimprovement and optimization whenSCOR is applied within a framework ofinitiatives to improve the Supply

Chain’s performance (Supply Chainimprovement initiatives):319

Raw materials purchase costs: 25percentCost of distribution: 35 percentTotal resource deployed: 50percentManufacturing space: 50 percentInvestment in tooling: 50 percentOrder cycle time: 60 percentNew product development cycle:60 percentInventory: 70 percentPaperwork and documentation: 80percentQuality defects: 100 percent.320

Stephens points out the following

advantages may be achieved whenSCOR is applied to integration measures(those quantified benefits which may beattained by integrating the SupplyChain). He refers to these in the contextof a 1997 comparative study by theSCC:321

Delivery performanceimprovement: 16 to 28 percentInventory cost reduction: 25 to 60percentReduction in order fulfillment cycletime: 30 to 50 percentImprovement to forecast accuracy:25 to 80 percent

Increase in overall productivity: 10to 16 percentLower supply chain costs: 25 to 50percentImprovement of fill rates: 20 to 30percentImproved capacity realization: 10to 20 percent.

An additional, although not

immediately quantifiable, advantage tothe application of the SCOR model maybe found in its nature as somethingindependent of a particular industry.322

The freedom that this entails means that,amongst other things, it is possible toarrive at a comparison of processes in

companies from various industryaffiliations and formulate an optimizedprocess as a result.323

A report compiled by the company

Intel324 describes the advantages thatresulted from the implementation of aSCOR initiative. The advantagesportrayed are mainly qualitative innature. The project team originallyresponsible for the SCOR projectstrongly promoted the SCOR model’sdiffusion throughout all areas of Intel’sSupply Chain. The subsequent report isevidence that after reflecting on theexperiences recorded the team wasconvinced of the model’s performancecapabilities and advantages. An

additional advantage gained, althoughharder to quantify, was the increase inknowledge on the part of the projectteam’s members with reference tobusiness processes, Supply Chainprocesses, and relationships andassociations within the Supply Chain.

The application of the SCOR modelis also seen as a positive end in itself aspart of a process to internalize andcomprehend the fundamental connectionsin the chain in a generally valid languageand within a continual structure. Thereport stresses that the inclusion ofrepresentatives from different businessareas in the model was a greatadvantage. As a result of this approach,

the risk that a one-side point of viewwould result was reduced.325

In the report’s conclusion the

central knowledge database(repository), which originated out of theproject was identified as a significantadvantage for the business. Today thisrepresents a substantial component ofKnowledge Management326 within theframework of Intel’s Supply Chain. Inaddition to this, the part of the repositoryused for all SCOR projects was alsoapplied to overlapping projects – forexample, in the form of an initiative forbusiness process modeling within theframework of the Enterprise ResourcePlanning (ERP) system.327

The companies SAP328 and

PRTM329 began working together in theyear 2000 on a SCOR-based,standardized program. This program canbe assigned in order to compareorganizations’ Supply Chainperformance capability to thecompetition (standardizedBenchmarking Program based onSCOR). Participants were able tocompare their results to the results of thecompetition based upon the SCORmodel.330

The BASF Company331 also

participated in such a comparison studyas carried out by SAP and PRTM. The

corporation stated that the project hadcontributed extremely positively to theanalysis and definition of many areas forpossible improvements. The studydemonstrated that the application of theSCOR model holds the potential forsubstantial improvements in the fields ofSupply Chain solutions.332

The views of leading IT research

companies, like the Meta Group,333 pointin the same direction. SCOR-basedSupply Chain performance comparisonis considered to be a useful way ofsupplying businesses with valuableinformation in order to analyze andoptimize their processes. This isparticularly important when the metrics

allow the Supply Chain’s performancepotential to be compared to that of thecompany’s competitors. In associationwith this, the particular advantages ofthis model over the Supply ChainScorecard introduced in Chapter 1 areevident, as the latter is classified asmore one-dimensional and thusinsufficiently integrated. As a result, theadvantages and strengths of the metricsused within the SCOR model’sframework are clear.334

2.3.2 Weaknesses andlimitations of the modelThe SCOR model is still in anevolutionary condition, and remainssubject to changes. On the one hand, this

gives it a certain strength, because itguarantees the model’s continuousexpansion to include up-to-datethemes.335 This could involve a wholemonitoring process, such as theintroduction of the Return process asshown by Version 4.0 (even if not allactivities in this field are containedwithin Version 6.0). On the other hand,this is accompanied by a particulardegree of uncertainty as elements of themodel valid today may – under certaincircumstances – be changed in the futureand thereby may lose at least part oftheir present validity.

As in the earlier illustration 2-5,the SCOR model’s hierarchy presently

comprises three levels in order tosupport Supply Chains of varyingcomplexity throughout variousindustries. The SCC has clarified that itdoes not intend to extend the model intofurther levels and describe how a certainorganization should execute theirbusiness or adjust their present ITsystems and information flows to suitemarket requirements. Such a statementresults from the SCC’s conception ofSCOR as a descriptive and formativemodel. It does not, however, mean thatthe model’s application excludes thepossibility of a subsequent Supply Chainoptimization building on its findings.Indeed, it even makes explicitsuggestions in that direction, for

example, in the form of best practices.

The model’s status does, however,mean that each organization that appliesit with a view to securing improvementsin the Supply Chain must expand themodel – namely by the inclusion of afourth level which illustrates the tasks.This necessitates the inclusion oforganization-specific processes, systemsand practices. This organization-specificextension is not supported by the SCORmodel, at least not in the present version.There are, however, approachesregarding the improvement of themodel’s assignment possibilities in thisfield, and these will be dealt with inChapter 5.336

Furthermore, the model does not

attempt to describe every businessprocess or every activity within theSupply Chain. These deliberatelyexcluded components are: Marketing andsales (i.e., creation of demand), researchand technology development, productdevelopment and some areas of post-delivery customer service. The modelalso includes several functional areas aspreconditions without addressing themspecifically: Human Resources,Training, Quality Assurance, InformationTechnology, and Administration (as longas the latter does not refer to the SupplyChain’s monitoring). The present SCCposition is that the respective horizontal

activities are implicitly contained withinthe model, and that there are particularorganizations that specialize in theseareas. The SCC leaves it to suchorganizations to offer qualified supportin these fields.337

2.3.3 Critical success factorsduring application of theSCOR modelThe critical factors for success duringthe SCOR model’s assignment arisefrom the model’s possibilities andlimitations, and the interdependenciesbetween them. In this way, the modelundoubtedly serves to standardize theSupply Chain’s procedures in an manner

that can span different industries. Theparticipating companies andorganizations speak, as it were, a singlelanguage in which they define theirmetrics identically.338

If an organization chooses to follow

the SCOR model, it is important that ittransposes the universally valid andformulated concept upon the specificcompetitive situation. As a result, it isobliged to openly address the actualwork processes within the organization,and this presupposes a good knowledgeof these processes. Harmoncharacterizes the conditions as follows:

“The use of a framework-

based business processmethodology is only possiblein cases where a high levelanalysis of the processes to beanalyzed already exists, andwhere measures of processsuccess have already beenstandardized. Obviously, itwill help if the standardizationis done by a large, neutralstandards group, like theSupply-Chain Council, sincethat will assure that theprocesses and measures arereally well thought out and thatindividual practitioners willmore readily buy into thecommon framework.”339

The parties involved in the Supply

Chain can benefit from best practiceswith regards to the Supply Chain’smonitoring, and as a result of thesepractices the compatibility within thecompany-spanning Supply Chain (i.e.,with the inclusion of all parties)increases. This also applies to thesynchronization of the respectivehardware (HW) and software (SW)solutions – a criterion not to beunderestimated nowadays as these canrepresent a substantial cost factor andmust be seen unconditionally in thecontext of their related advantages.340

Due to the greater complexity thisentails, the synchronization is more

difficult than if companies were toconcentrate purely upon their own areas.To carry this forward successfully,companies must have sufficient funds ata relatively early phase of projectimplementation. On the positive side,however, synergies arising from thisprocess can yield advantages from thevery beginning of the implementationphase.

The SCOR model has a high degreeof abstraction due to its comprehensiveapproach.341 It is therefore almostimpossible to apply with an unstablebasis of cooperation between theparticipating Supply Chain parties as itpresupposes a certain degree of

continuity. If the approach is persistentlyapplied, the dependency between theunified partners increases, and as aresult the companies involved losesovereignty. Whether that represents anadvantage or disadvantage dependsstrongly upon the respective businessstrategy. In addition to this, the narrowsupplier-customer connection finally andinevitably leads to the release ofsensitive information at the interfaces,whereby critical knowledge can alsoflow.342 The business must decide if thebenefits of this outweigh potentialdisadvantages.

It is the author’s view that thecritical success factors named do not in

themselves primarily represent a SCORmodel problem, but moreover a problemof Supply Chain Management inprinciple. The reason for this can beseen in the fact that SCOR illustrates anorganization’s Supply Chain, thereforerepresenting a descriptive model. Itdoesn’t lay claim to immediatelystructuring the Supply Chain, i.e., tobeing a formative model.343

Nevertheless, it should contribute byrespective recommendations to SupplyChain improvements, but this takes placedue to the description and consequentextraction of action points.

Brought to a precise denominator:The SCOR model itself does not form,

but contributes to a (better) formation orstructure of the Supply Chain. Uponcloser examination, this train of thoughtdoes not represent a disadvantage, butmoreover an advantage: It is the highdegree of abstraction that in fact allowsthe model to fulfil the requirements of anormative model or reference modelrespectively, as stated above. It is,however, important to take intoconsideration the limitations to themodel’s successful assignment. 2.4 Practical areas ofapplication of the SCORmodelThis section will examine the assignment

and application of the SCOR model.During this a differentiation will bemade between the two following cases:

Companies that have appliedSCOR within the framework of abusiness initiative.External consultancies, which enlistthe SCOR model with customersfor the purpose of analysis andnecessary building upon it forSupply Chain optimization.

2.4.1 Examples for theapplication of SCOR ?in theframework of a business

initiativeIn conjunction with this, the author cameupon two present examples from theHigh Tech industry, Hewlett-Packard(HP) and Intel. In addition to this, SCORis presently an intensely discussed topicwithin the logistics field of theDepartment of Defense (DoD) in theUnited States. This last case study isparticularly noteworthy on account of thescope and the complexity of therespective Supply Chain. 2.4.1.1 Application of SCOR atHewlett-Packard (HP)Hewlett-Packard’s344 Business ProcessManagement Group (BPM) hasdeveloped a reference system for

product design and for the businessareas responsible for customer relationswhich is built-on the SCOR model. The group has also added a referencesystem for demand generation(Marketing). These reference systemshave been successfully used in numerousprojects, and HP has handed them overto the SCC’s newly formed SpecialInterest Groups (SIGs) – the Design-Chain Council (DCC) and Customer-Chain Council (CCC),345 so that theycan be adopted as open industry-standards for business processmonitoring and improvement.346

A further example of the SCOR

model’s application took place withinthe framework of the merger347 of HPwith Compaq in 2002.348 HP considersthis application to be a good example ofa so-called approach to a SecondGeneration Business Process Change.When the merger was first announced,teams were formed by HP and Compaqto plan how Supply Chain combinationcould be carried out. At this time, bothCompaq and HP had dozens of SupplyChains distributed all around the world.The Supply Chains had been developedat different times and were based upondiffering software systems. Next to theproject team responsible for the SupplyChains, there were also teams that wereresponsible for analyzing the software

systems which were to be applied forthe new, combined functional areas ofsales, marketing and development ofnew products, as well as the supportingfunctions of a financial, accounting,personnel and IT nature.349

Most of the teams began with an

inventory of the systems already present.Upon completion of this they moved onto a discussion of the advantages of thevarious applications with a view toreaching an informed decision as towhich ones were the best suited for thefuture. At that time it was known thatcomplex Supply Chains can becharacterized by means of SCORdiagrams of the second level (Level 2

Thread Diagrams)350 and that SCORprovides precise formulas for businessmetrics, which can be used upon datafrom the past in order to measure theachievement of success in every processon the second level.

Based upon previous examples of aso-called First Generation BusinessProcess Change,351 there waswidespread skepticism as to whether theproject’s implementation – the analysisof HP–s and Compaq–s main processes,the optimization of the communal futureprocesses and the allocation of metrics –was realistic within the given timeframe. Firstly, SCOR processes of thesecond level, and then of the third level

were analyzed in order to determinemutuality between the present SupplyChains. Data from earlier studies wasalso used to identify the success of everyprocess. In several cases two differingprocesses of HP and Compaq werefunctionally similar, but there wasconsiderable variation in theirperformance potential based upon therelevant SCOR metrics.352

In other cases the processes were

compatible as far as their performancepotential was concerned, but one of thetwo showed a better functionality, whichwas assessed by means of the third levelprocesses. In addition to this, the SCORmodel’s application enabled the team to

identify the Supply Chain processes withthe greatest efficiency and, building uponthis, to choose the most suitablesoftware applications for processsupport. The project’s success restedmainly upon the presence of a referencesystem which allowed it, in a relativelyshort time and in a consistent way, toanalyze and optimize the Supply Chainprocesses, as well as to apply metrics inorder to assess the effectivity andefficiency of every process (andassociated sub-processes). In retrospect,then, HP was able to state that thesuccess of the project was primarilyattributable to the SCOR model’sassignment.

In the months following the merger,HP’s IT BPM Team concentrated uponconsidering how SCOR’s referencesystem could be expanded to includeareas within the business that were notcontained within the present version.353

Within the framework of severaldevelopment and refinement cycles(iterations) and in conjunction withbusiness partners, the model wasvalidated on the basis of regularoperational occurrences. The referencesystem resulting from this was an exactreflection of several of HP’s businessareas and proved itself to be adequatelyand universally valid in its ability toanalyze and describe the businessprocesses in any number of HP’s

business areas.354 Welke summarizessuch a means of approach as anexpansion of the normative model’spossibilities (broadening the normativemodel set)355 and goes on as follows:

“The full-enabling process sethas been used by HP tomanage the Merger andAcquisition process withCompaq. It has been the basisfor the extension to an openstandard, SCOR-style,normative model.”356

With this process the conversion

from a universal, normative model into aSCOR-based model was effectively

completed. 2.4.1.2 Application of SCOR at IntelIntel357 describes the method and thesuccess of the SCOR model’sapplication under the headingExperience with SCOR at Intel. It isthereby particularly remarkable that apersonal method was developed, the so-called Intel SCOR Best Known Method(BKM). The BKM Project for SupplyChain analysis and optimization at Intelbegan with focused preparations. Beforea team of employees from variousfunctional areas was formed, thebusiness information necessary to definea clear problem statement was collectedby a core team. After the function-

spanning team had been formed, thewhole team participated in a number ofso-called Face-to-Face (FTF) workmeetings to define the project milestonesaccording to the SCOR model.358

The Intel-specific method

developed from this means ofimplementation, BKM, recommended ineffect that the core team completed theassigned tasks and collected detailedinformation in small workgroups (forexample, based on metrics, benchmarksand financial information) and presentedthem during the next FTF work meeting.Additionally, SCOR-based simulationswere used early on in the course of theproject to analyze alternative

configurations and to later confirm theeffects of the suggested changes.

In some cases Intel’s Supply Chainwas not as effective or reactive aswould have been necessary for agrowing business with a high businessvolume. It became evident that thecompany’s traditional business modelwas often unable to supportrequirements that arose in particularbusiness areas. Furthermore, some of theestablished “workarounds” were notefficient or robust enough. Intel hadstarted the first SCOR project with theobjective of identifying improvementpotential in the customer service andefficiency areas, purely with reference

to its own Supply Chain. The projectwas intended to test the SCOR model’susage and supporting tools within theorganization, and compile and establishguidelines for its application.359 Thedesired results of the project lay in thefollowing areas:

Documentation of the Supply Chainand the measures for improving theSupply Chain’s processes.

Identification of short-termimprovements.Ensuring support on the part of thebusiness areas and executives, and

identification of the personsresponsible for long-termimprovements.360

Additional results contained a

summary of project results and thelearning progress achieved by the SCORmodel’s usage as well as the principlescompiled for SCOR application in futureinitiatives. Beyond this, a method wasdeveloped, due to the adoption ofSCOR, which should, in the futureenable the comparison of SCOR-basedmetrics to those of competitors, i.e., aSCOR-based performance comparison(benchmarking).361

The company-wide application ofSCOR BKM within all business areaswas supported and promoted by the IntelSupply Network Group (ISNG). Thegreatest challenge in conjunction withthis resides in the requirement forcircumferential training duringsimultaneous acceleration of themethod’s further development. In orderto support the latter requirement, theIntel IT research group cooperated witht h e Network Decision SupportTechnology (NDST) group – a groupwithin ISNG that was responsible forsimulations. The objective was todevelop a SCOR-based Supply Chainsimulation, and examine alternativedesigns of Supply Chain networks (so-

called Supply Networks, SN), as well asthe effects of high-priority systemsolutions.

BKM has meanwhile beenexpanded by the addition of a SCOR-based planning process with regards tothe Supply Network. This procedurewas checked within an extended SupplyChain, which illustrated the electronicdevices and component group’s SupplyChain model along with a EuropeanOriginal Equipment Manufacturer(OEM). The focus of this study was thepossible replanning of demand response.During this, it was possible to study theeffects of the balancing of demandrequirements forward-facing, as it were,

within the Supply Chain (i.e., fromdelivery process up to procurementprocess), and to study effects of thereplanning process upon the SupplyChain performance and costs.362

In addition to this, increased focus

on the planning process also made itpossible to effectively make inventoryitems available for order fulfilment.Within BKM, SCOR’s planning processhas been specially adapted to Intel’sbusiness requirements and its marketingpartners’ companies within the expandedSupply Chain. New possibilities forcollaborative planning with suppliers, aswell as the sharing of demandinformation, have resulted from this.363

Finally, the SCOR-based

simulation was integrated into tools forthe application of diverse forecastingmethods and the modelling of demandcreation. In this context the so-calledPostponement Strategies364 could bemodelled, which enables examination ofthe effectivity of a product’s finalassembly at various locations.365

In the recent past Intel has gone

beyond the measures for the applicationand enhancements of SCOR spoken ofabove and has engaged in a SCOR-based model that exists under the nameValue Chain Operations ReferenceModel (VCOR).366 The model’s

promotion takes place under theauspices of an organization called theValue Chain Group (VCG) .367 VCORbuilds, as it were, upon the SCORmodel, but it is characterized by twomain differences or variations:368

Firstly, the plan process isextended by two further so-calledMacro Processes: Govern andExecute. All further processescome below these three macroprocesses.Secondly, the model is constitutedby the following eight ProcessClassifications instead of the fiveclassical SCOR main processes:

Market, Research, Develop,Acquire, Build, Sell, Fulfill andSupport.

Intel provides the following

description for the usage and assignmentof VCOR sought, and the commitmentassociated with it:

“A general consensus hasdeveloped among partnersdeveloping essentialcollaboration models forproduct design for supplychain that the long-term valueproposition is to focus on aValue Chain OperationsReference model (VCOR).

Defining business semantics interms of the commonvocabulary of VCORaggregates businessapplications and businessprocesses to a higher level ofabstraction. In this way, valuechain integration enablescoordination acrossdepartmental, organizational,and enterprise boundariesfrom an overall business levelperspective. The benefit is thatit facilitates service-composed processes and,thereby, brings service-oriented relevance to acomplex IT landscape in

which ongoing, flexibleadaptation is necessary.”369

The further development of VCOR

on the basis of SCOR can therefore beseen as an attempt to move still furtherfrom normative models towards thoseenriched with Value Chain-specificaspects.370

2.4.1.3 Application of SCOR by the USDepartment of Defense (DoD)The US Department of Defense (DoD)maintains, according to value, the largestworldwide Supply Chain. Within theorganization, the Supply ChainIntegration Office of the Secretary ofDefense (OSD) – Logistics and

Material Readiness is responsible forSupply Chain operation.371 The annuallogistic expenses in 2004 were morethan $80 billion (US),372 which wereadministered by more than a millionlogistics employees. Several years ago,the DoD began with the SCOR model’sintroduction in order to lower SupplyChain cost and improve customersatisfaction. The ultimate objective wasto implement an integrated SupplyChain.373

The DoD assumes that SCOR is a

universally valid platform and languagefor cooperation with private firms andthe respective defense organizations,used in order to mutually develop and

assign best practices and evaluateSupply Chain efficiency. At the end of2004, the DoD had introduced the SCORmodel throughout many areas of itsbusiness. The model therefore representsan immanent component of the strategyfor Supply Chain monitoring, and itsleadership of the logistic area drivesfuture SCOR developments onwardswithin the Supply-Chain Council.374

The Supply Chain model developed

by the DoD has the purpose oforganizing relevant information andmaking it available (KnowledgeExchange). The SCOR model versiondeveloped by the SCC and widelydiffused throughout industries thereby

developed itself into an analytic tool forSupply Chain monitoring within thepublic sector.375 This refers back to thepreviously mentioned expansion of itsspectrum of usage to cover E-Businessareas Government-to-Government(G2G) and Government-to-Business(G2B).376

For the purpose of knowledge

exchange, the terminology was adaptedto the DoD’s SCOR model defined bythe OSD, which is distinguished by thefollowing monitoring processes: Source,Make/Repair, Deliver, Reutilize/Dispose. Under the monitoringprocesses lie functional areas, whichserve to categorize Supply Chain

information for the knowledge exchange.

In the case of the functional areaswe are dealing with MaterialRequirements Determination, i.e.,Purchasing, Material Management,Repair/Maintenance, MaterialDistribution, Transportation andMaterial Disposition. The result is aSCOR-based Supply Chain, which takesthe special requirements of the DoD intoconsideration.377 The followingillustration summarizes the explanationsin graphical form.

The above Supply Chain model forknowledge exchange takes intoconsideration the two fundamental

perspectives from which a Supply Chaincan be observed—on the one side, aninternal Supply Chain, which subsumesall the monitoring processes andfunctional areas of an organization inorder to fulfil customer orders; on theother side, an external Supply Chain,which is defined as a group ofindependent organizations cooperatingwithin special channels in order todeliver a product or a service.379

The monitoring of the DoD’s

Supply Chain (DoD Supply ChainManagement) is based upon anintegrated procedure that begins with theplanning of the requirements on thecustomer-side with reference to material

and services and ends with the deliveryof material to the customer. Included inthis process are the return of materialand the bidirectional information flowsthroughout suppliers, logistic functionsand customers.380

Diag. 2-7: DoD model of Supply Chain

Management378

Beyond this, Supply Chain monitoringrequires a complete set of relatedprocess cycles (including planning,

procurement, repair and delivery),which are communally optimized toensure that material and servicerequirements are efficiently planned andexecuted in order to satisfy customerneeds. The monitoring of the DoD’sSupply Chain focuses firstly uponfulfilling customer requirements andonly secondly upon doing this at thelowest process costs.381

2.4.2 Examples for theapplication of the SCORmodel by externalconsultanciesResearch with regards to application ofthe SCOR model by external

consultancies has produced thefollowing results:

There are a number of smallerbusiness consultancies, for exampleSCE Limited based at Stillwater,Minnesota or mi services groupwith its headquarters in Wayne,Philadelphia, which have chosen tospecialize in the application of theSCOR model. SCE Limited roughlydescribes itself as a “center ofexcellence in SCORapplication.”382 Companies of thistype usually have less than 500employees.There are also medium-sized

business consultancies, usuallywith around between 500 and 1,000employees, that see SCOR as asubstantial component of theiradvisory portfolio. The best knownexample is the consultancyPittiglio, Rabin, Todd & McGrath(PRTM), which was immediatelyincluded in the creation of theSCOR model and is still a leader inthe field today. It also formed abranch which specializes in SupplyChain Benchmarking, thePerformance Measurement Group(PMG).There are then businessconsultancies with more than 1,000employees, that see SCOR as an

immanent component of theiradvisory portfolio in the field ofSupply Chain Management. In thiscase, we are dealing primarily withbusiness consultancies that wereinitially offshoots of the so-calledBig Four (or respectively theformer Big Five) certified publicaccountants.383 According to theauthor’s knowledge, the firmBearingPoint (formerly KPMGConsulting) with its headquarter inMcLean, Virginia, was the onlyexample of a business of thiscategory at the time of the submittedwork’s origin, which specificallyassigns the SCOR model within theframework of its consultancy

activities.

There are also software supplierswith their own advisory field inwhich SCOR becomes assigned,either in the field of SCM solutionsor in the field of reporting.Examples of these are the firmsBusinessObjects headquartered inSan Jose, California,384 and SAPwith its headquarter in Walldorf.Finally, there are institutes, themajority of which are associatedwith universities, which have setthemselves the target of the furtherdevelopment and application ofSCOR. One example of this is the

Supply Chain Management Centreof the Singapore Institute ofManufacturing Technology(SIMTech) (formerly Supply ChainManagement Centre of the GinticInstitute of ManufacturingTechnology).

For each of the five cases listed,

one of the named companies will bemore closely examined in the followingpages. 2.4.2.1 mi services groupThe tool developed by mi servicesgroup385 and based upon the SCORmodel, the so-called SCORWizard,serves the purpose of automating the

SCOR model’s application. Such a toolmust be clearly differentiated from thoseapplications which have, amongst otherthings, the Supply Chain’s design orformation respectively as theirobjective.386 In the present case we aredealing purely with a tool to supportSCOR’s application as a descriptivemodel – not a forming model – of theSupply Chain.387 The SCORWizardconsists of two components: 1. Balanced Strategic Measurement:

• Adjustment of strategic objectives toSupply Chain objectives

• Creation of a Balanced SCOR e-Card388

• Comparison of performance with

competitors (Benchmarking)• Determination of performance

objectives. 2. End-to-End Visualization:

• Clear allocation of the extendedSupply Chain’s physical scope

• Configuration of all processelements, determination of rolesand relationships389

• Compilation of a reference systemfor a detailed analysis.

Whilst the SCOR model deliversthe general reference system, theSCORWizard automates several stages

and should add a further degree of detailwithout negatively influencing SCOR’sstrengths. We are therefore dealing withthe assignment of one of the toolsoriginally supporting the SCOR model.The advantages named are to becharacterized as follows:

Simple addition, removal or changeof best practices and metrics withinthe SCOR reference system.Creation of a knowledge base forSCOR processesSimple compilation of examples forSupply Chain illustration which areconsistent throughout businessareasSimple determination of the

optimized business processes andthe supporting system technology.

By these means, the redefinition of

the Supply Chain Strategy is supportedand a reference system created for theimprovements along the Supply Chain.The application is thereforerecommended for executives as well asemployees within the operationalbusiness.390

2.4.2.2 PRTMPRTM391 was one of the first businessconsultancies that occupied itself withthe comparison of a Supply Chain’sperformance potential (Supply Chain

Benchmarking). As a result of this, acomprehensive database of SupplyChain benchmarking information wascollected, whereby the fundamentalmetrics are based upon the SCORmodel. The PRTM’s own PerformanceMeasurement Group (PMG),392 whichwas founded for this specific purpose,describes itself as a leading supplier ofdata for Supply Chain performancecomparison.

The spectrum of services offeredstretches from individually tailoredperformance comparisons up to fast,efficient diagnoses on the basis of thePMG’s Supply Chain database. In allcases, the performance comparison is

seen as a tool to provide the initialincentive and focus for Supply Chainimprovements. During this, it is assumedthat in the course of projectimplementation not only possibleimprovements, but also necessaryinitiatives for improvement can bedefined. The services offeredindividually cover:393

Data collection, identification ofgaps and implementation planningAccess to the Supply Chain’smetric databaseA circumferential set of data for thecomparison of Supply Chains(benchmarking) which are

completely consistent with theSCOR modelA specially developed model, theso-called Supply Chain MaturityModel that contains a multitude ofprocedures in order to estimate thecompany’s development condition.Highly-automated analysis tools.

We are also dealing here with an

automation of the basic SCOR model’sstages, as illustrated by the firstexample, with sporadic enhancements inselected areas. 2.4.2.3 BearingPointBearingPoint (formerly KPMGConsulting)394 assigned the SCOR model

in the area of Supply Chain Strategy as areliable basis for Supply Chainillustration. The methodology usedwithin the framework of advisoryprojects for Supply Chain transformationand the supporting tools werecompletely adapted to the SCORmodel.395 The five main processes of theSCOR model were therefore analyzedand evaluated by means of four keydimensions:396

Market/value chain integrationProcess design?Organizational designTechnology design andinfrastructure.

In comparison to the previously

cited examples, the consultancy did notattempt to automate or extend the SCORmodel. Moreover, it assigned the modelto consequently develop consistentprocedures.397 Additionally,BearingPoint has, in association withthis, developed a personal tool (KPIBenchmarking Questionnaire) basedupon quantitative questionnaires, whichwill still be dealt with in detail inChapter 3, as it represents the basis ofthe empirical examination.398

2.4.2.4 SAPSAP399 in conjunction with PRTMconducted a SCOR-based Supply Chain

study in the years 2002 and 2003. Thestudy evaluated and followed the SupplyChain performance of more than 100global SAP customers in order to seewhat influence the processes andsystems for Supply Chain planning haveupon companies’ performancecapability. During this, two types ofmetrics were differentiated:400

Internal-facing indicators:Inventory days of supply, inventorycosts and cash-to-cash cycletime,401 etc.Customer-facing indicators: Orderfulfillment according to customerrequest, on-time delivery, order

cycle times, etc.

In addition to this, the study

occupied itself with the degree ofmaturity of the planning procedure’sdevelopment position with respect to theSupply Chain, as well as that of thesupporting systems (maturity of SupplyChain planning practices). It waspresumed that “mature” planningprocedures and systems arecharacterized not only by the enabling ofintegration within the expandedbusiness, but also with external businesspartners. The study arrived at severalrevealing insights:402

The most significant cost reductionswere identified in the area ofinventory days of supply.Companies could therefore save 63percent costs, or respectively gaina 1.7 percent improvement inprofitability, with a high degree ofmaturity.403

In association with this, theobsolescence of stock was a highlyimportant factor. The companieswith the highest degree of maturitywere able to lower the drop ininventory value by up to 84 percent.In addition, the inventoryobsolescence could be reducedfrom 0.9 percent of the profit downto 0.3 percent by the assignment of

leading planning procedures andsystems for the development of newproducts.It became apparent that companieswith a high degree of maturityshowed a 17 percent improvementin on-time order fulfillment rate anda 7 percent improvement indeliveries. Based upon the value ofexperience it was assumed that a 17percent improvement in ?on-timeorder fulfillment rate representedan increase of 3.4 percent in profit.

Beyond this it became apparent thatthese firms showed a 45 percentshorter order cycle time. A

decrease of 45 percent can,according to experience, lead to a45 percent reduction in inventorystock.

Apart from the aforementioned

cases, conclusions were also reachedwhich do not allow themselves to beextracted from the collective results bymeans of scientific methods, but wouldrather seem to have a qualitativecharacter. It was therefore stated thatcompanies which use the SAP planningsystem had shown particularly positiveresults with regards to internal andexternal metrics.404 In addition to thisthere were indications that customers

who use the SAP application programachieved a net profit roughly threequarters higher than other companiesachieved, an average margin of 14percent as opposed to 8 percent.405

2.4.2.5 Singapore Institute ofManufacturing Technology (SIMTech)The Supply Chain Management Centre ofthe Singapore Institute of ManufacturingTechnology (SIMTech) 406 carries out anannual Supply Chain BenchmarkingStudy, based upon the SCOR model, forthe Southeast-Asian region.407 Theparticipating companies come fromIndonesia, Malaysia, the Philippines,Singapore and Thailand. The projectwas started in the year 2000, and six

reports have been submitted to date.

The study is based completely uponthe SCOR model and its associatedmetrics. The objective is to give theparticipating firms a clear picture to asto their position with regards to theircompetitors, and to what extent they mustimprove themselves in order to belongto the best in their peer group (Best inclass). The targeted group is restrictedin this case to the industrial sector.Furthermore, a better data collection issought in order to compare theperformance potential of companies andtheir Supply Chains in the Asian region.Given the various business environmentsand regional features, the collection of

such comparable data is seen as aparticularly important requirement.

In order to guarantee thecomparability of the collected data in theAsian region, as well as withcompetitors in North America andEurope, it was decided to use the SCORmodel in this respect. This was reached,on the one hand, because the SCORmodel is seen as a comprehensivemethodology for Supply Chain analysisthat has been successfully able to proveits application ability and the advantagesthereof. One the other hand, the SCORmodel’s wide diffusion and theaccessibility of comparable dataassociated with it, primarily in North

America, were also decisive. Theinitiators of the study believe that it will,over an increasing period and with theassociated growth in collected data,develop into an increasingly importantsource of information for the respectivecompanies within the region.408

Chapter Three

Empirical study basedupon a quantitative

questionnaire This chapter addresses the context ofjustification within the framework of theresearch-logical course. According toFriedrichs, this context may becharacterized as follows:

Under context of justification,the methodological steps mustbe understood with whose aid

the problem is to be examined.It is a methodologicalprocedure by which theindividual stages areinterdependent. The goal is anexact as is possible,verifiable, and objectiveexamination of thehypotheses.409

A hypothesis or thesis410 in this

context refers to a presumption of theconnection between at least twovariables. An empirical theory is asystem of logical, contradiction-freestatements in the form of hypotheses withrespect to the object to be examined,along with the associated definitions of

the terms used. Therefore, severalhypotheses (or a system of hypotheses)must accompany a theory.411 This studyseeks to make an initial contribution tosuch a field. It has adopted anexploratory approach412 and reachedsome initial findings. If these are to beuseful in the broader context, however,further research is needed to exploreother aspects of the field.413

The fundamental central

presumption of this study, which stillremains to be tested, is as follows:414

The SCOR model is groupedaround a main axis with a

customer-facing competence onthe one side, and an internal-facingcompetence on the other.415

Both the SC competences are eachalso assigned PerformanceA t t r i b u t e s : Reliability andResponsiveness as well asFlexibility on the customer-orientated side, Cost and Assets onthe business-internal side.It is presumed that the PerformanceMetrics assigned to thePerformance Attributes within oneof the two SC competences areconsistent with one another, i.e.,point in the same direction. The

performance metrics assigned to theperformance attributes between thetwo competences mutuallycomplete each other, i.e., theyguarantee a balance between thevarious objectives.

It must be noted that besides the

model’s depiction, which wasdeveloped for the purpose ofexamination within the framework of thework at hand and is reflected in theaforesaid central presumption, amultitude of further illustrationalalternatives theoretically exist.Reference to the SCOR model in ageneral sense must really be seen against

this background.416 In order to examinethe central presumption, hypotheses – ormore exactly: a system of hypotheses –are formed and examined with the aid ofstatistical procedures. During thisprocess, a hypothesis-investigativeapproach is adopted.417 Some of theterms necessary for this have beenexplained in Chapters 1 and 2, andothers will be introduced before thedescription of the empiricalexamination. 3.1 Objectives of theEmpirical ExaminationAt the close of Chapter 2 it was notedthat the SCOR model’s diffusion andapplication has increased considerably

in the past three to four years, mainly inthe American and Asian regions. In spiteof this increased usage, however, therehas not been a detailed study that hassubjected the SCOR model’s basicstructure or its fundamental assumptionsto a scientific examination. For the mostpart, only generally held indicationsexist, and these are based upon the valueof experience. This may be due to thefact that, despite constantly increasingmembership numbers and enhancements,the SCOR model is not yet fullyaccepted in practice.418

The examination at the center of

this study is therefore intended to serveas a scientific contribution to research

into the structure of the SCOR model.419

The data or correlation of the individualvariables (in accordance with thetheses) will be primarily examined on anempirical basis within the findings. It isnot envisaged that interpretations –unless referring to the statisticaldurability of the theses – or appraisalsregarding concrete recommendations foraction will feature in the findings.420 Therespective conclusions are moreover tobe reached in the further course of thework within the context of realization, inorder to accommodate for the Freedomof Value Judgment Posit(Werturteilsfreiheits-Postulat)contained within research science (seechap. 5).421

A Balanced SCOR-based Supply

Chain Scorecard serves as aninstrument for the examination, for whichthe term SCORcard will hereafter beused. Bolstorff and Rosenbaumintroduced the term SCORcard asFowler’s SCORcard Matrix inconnection with a practical examplewithin an American corporation calledFowlers Inc.422 The construction of thisSCORcard will be explained in the nextsection. 3.1.1 Concretizing of aspectsand formation of thesesThe connection between the SCORmodel’s fundamental performance-

respective terms needs to be explored inorder to arrive at a form of SCORcardthat can function as a basis for empiricalexamination.423 For this purpose, therelevant forms of data and theirconnection must be defined beforehand. 3.1.1.1 Overview of the performanceterms relevant to the examinationThe following illustration represents thefive Performance Attributes used withinthe SCOR model in addition to theirdefinition and the associated thirteenLevel 1 Metrics.

The Level 1 Metrics illustrated indiag. 3-1 are in turn defined asfollows:425

To (1): Delivery Performance [to

commit date]:P.rcentage of orders delivered

upon or before the agreed date. To (2): Fill Rate:

P.rcentage of deliveries from thewarehouse within 24 hours of receiptof order. In the case of services, thepercentages of services are meantwhich were completed within 24hours.

Diag. 3-1: Assignment of the SCOR performanceattributes to the associated level 1 metrics 424

To (3): Perfect Order Fulfillment:

A perfect order is defined as anorder which fulfills all the followingrequirements: Delivered completewith all order lines included.Delivered to the point in timerequested by the customer wherebythe customer’s definition of on-time

delivery is applied.426 The orderdocuments (inventory list, bill oflading, invoice, etc.) are complete andaccurate. The delivery is in perfectand damage-free condition. Theinstallation, as far as applicable, hasbeen faultlessly performed, is inaccordance with the configurationrequirements and ready to be takenover.

To (4): Order Fulfillment Lead Times:

The average actual order cycletime which is consistently andreproductively achieved. The processcomprises the following stages:Respective customer signature orapproval up to order confirmation,

from there to the completion of orderreceipt and entry, from there to thebeginning of production and/or issueof purchase orders, then on to theshipment-readiness of the order, fromthere to product release, and finally toinstallation, if necessary.

To (5): Supply Chain Response Time:

Indicates how fast a business canadjust itself to changes to the market.

To (6): Production Flexibility:

Production flexibility becomesapparent in two variations:

• Upside flexibility: The number of

days necessary in order to ensure

an unplanned, permanent 20 percentincrease in production.

• Downside flexibility: The number ofdays required in order to react to anx-percent enduring reduction inorder volume, which takes place 30days prior to the planned deliverydate without building up stock ofmaterial or raising a contractualpenalty.

To (7): Total SCM Cost:

The sum of all costs incurred bya business due to the development ofan integrated Supply Chain.Comprises all Supply Chain relatedcosts for the Management InformationSystem (MIS),427 finances, planning,

stock of material, materialprocurement and order management.

To (8): Cost of Goods Sold, COGS:

Direct as well as indirect costsincurred by a business in order tomanufacture finished goods.428

Represents the margin as a percentageof total income.

To (9): Value-Added Productivity:

Calculated as total product salesminus total material purchases.

To (10): Warranty Cost or Returns

Processing Cost:The number of return deliveries

within the guarantee period.Guarantee is an (explicit or implicit)

insurance that a particular event withreference to a component of a contractis actually correct or adjustedproperly. Guarantee costs comprisematerial and labor costs plus the costsfor the examination of a defect.

To (11): Cash-to-Cash Cycle Time:

The time necessary for a certainamount of money to flow back into thebusiness after being spent uponmaterial procurement. The valuerepresents one of the main metricsused to identify how efficient abusiness monitors the financial flowbetween customers and suppliers.

To (12): Inventory Days of Supply:

The number of days necessary inorder to manufacture and sell goods.Hence, represents the time necessaryto convert an investment in stock ofinventory into goods sold.

To (13): Asset Turns:

The relationship between annualsales and total asset value.

Diag. 3-1 served as an illustrationof the relationship between thePerformance Attributes and the Level 1Metrics. Following on from that, it isnecessary to highlight the associationwith the previously represented SupplyChain competences – the customer-centric SC Capability on the one hand

and the business-related Efficiency onthe other.429 This correlation, forexample, corresponds to the opinionheld by Geimer and Becker, whereby theSCOR model’s measures are groupedaround four main performance attributes:customer service, flexibility, costs, andassets. Whilst the first two areas arecustomer-orientated, the other two putinternal business priorities into theforeground.430

Sürie and Wagner go a stage further

and describe the given performanceattributes as universally valid,applicable and relevant for every SupplyChain within the framework of a SupplyChain Scorecard – regardless of the

fundamental model. They assume thatalthough every Supply Chain is uniqueand requires a special approach, thereare still several characteristics whichare applicable in the majority of cases.Because they focus upon differingaspects of the Supply Chain, they canbasically be assigned to the categoriesalready named in accordance with theperformance attributes (reliability,responsiveness, flexibility, costs andassets).431

This approach is to be followed in

the further course, whereby both theperformance attributes Reliability andResponsiveness are sensibly combinedwithin the performance attribute

Customer service. Bovet and Marthahold a similar viewpoint whereby – asalready more closely explained – theSupply Chain’s Capability is comparedwith Efficiency.432 In other SCOR modelillustrations, the SC performancecapability is also described as acustomer-orientated component and theefficiency as a business-relatedcomponent.433

The Level 1 Metrics illustrated in

diag. 3-1 must, in the next stage, beassigned to Performance Measures,which are associated with a morenarrowly arranged spectrum of sub-processes.434 Of the two hundred or soPerformance Metrics referred to in

SCOR Version 6, this study focuses onabout sixty.435 The basis for this was thequantitative survey (KPI Benchmarkingquestionnaire)436 used within theframework of the empirical examination,which was developed in conjunctionwith an internal business consultancyproject.437 The influx of experience intothis resulted in the focus upon roughly 60primary performance metrics, whichwere enlisted again for use within theframework of the empirical examination.The main intention during this was toavoid redundancies and enableconcentration upon the most relevantmetrics. At the same time, such anapproach ensured the representativecoverage of the most decisive

influencing factors (cost, quality, timeand productivity).438

As the performance metrics can be

used to formulate and design measuresfor improvement, they may conceivablybe referred to as tools forDiagnostics.439 These diagnosticinstruments are, for instance, supposedto help to diagnose the deviation of thedelivered orders from the originalplan.440

3.1.1.2 Clarification of theperformance termsThere are significant gaps in the existingliterature as regards the usage of certainwords in reference to the process.441 In

response to this, a suggestion by Seibtwas adopted and a valid nomenclaturewas determined for the further course ofthis study.442 This is constitutedhierarchically as listed below, wherebyOssola-Haring’s recommendations werefollowed with reference to metrics usedfor business management:443

The two SC competences, performance

capability and effi ciency, each havefour Performance Attributes assignedto them:• Customer service• Flexibility• Cost• Assets.

The performance attributes represent

aggregated constructions and thereforedo not allow themselves to be directlymeasured or calculated.444

The thirteen Level 1 Metrics:• Delivery performance [to commit

date]• Fill rate• and so on.

The performance metrics

represent so-called relative figures orratios, i.e., they relate two (or more)absolute figures to each other.445

3.Performance Measures are assigned to

the Level 1 Metrics. Each Level 1

Metric has several performancemeasures assigned to it. Theperformance measures represent so-called absolute figures, i.e., they stemfrom individual numbers, sums ordifferences.446

4.The collective term >KeyPerformance Indicator (KPI)> isused for the aforementioned termswhich were introduced in Chapter1.447 The application of theperformance terms subsumed beneaththese KPIs within the framework of aSCOR-based quantitative analysis ofthe Supply Chain (KPI Benchmarking)is at the heart of further observation,the focus of which is mainly upon aprocess performance comparison – or

more precisely: a comparison ofSupply Chain processes – in the senseof process benchmarking.448

The exact means by which the

respective performance indicators relateto each other can be taken from section 3of the Appendix, whereby the hierarchyruns from left to right – originating fromthe performance attributes on the left ofthe table, through the performancemetrics, right up to the performancemeasures. 3.1.2 Establishment ofhypotheses and SCORmodel groups

The theses to be developed within thestudy refer, as a rule, to the correlationof interval-scaled SCOR modelparameters. The theses may usefully bedivided into three SCOR model groupsin keeping with the central presumptionformulated at the beginning of thechapter: Within a SCOR Performance Attribute:

Metrics and associated PerformanceMeasures are being examined, whichare allocated to a particularPerformance Attribute. For thispurpose, the respective term ora b b r e v i a t i o n Intra-PerformanceAttribute (I-P) will be used.

Between SCOR Performance Attributes,

“on one side of the equation ”:The focus here is upon metrics andassociated Performance Measureswithin one of the two SCcompetences, i.e., only one side of theequation will be observed at a time.For this purpose, the respective termor abbreviation Intra-Competence (I-C) will be used.449

Between SCOR Performance Attributes,

however “on different sides of theequation”:Metrics and associated PerformanceMeasures between the two SCcompetences, i.e., on both sides of theequation, will be observed (and withthat inevitably between various

Performance Attributes). For thispurpose, the respective term ora b b r e v i a t i o n Inter-Competence/Performance Attribute(I-CP) will be used.

As a matter of priority, anargumentative establishment of the threeSCOR model groups will be undertaken.The theses that will be examined laterwill be deduced from those definitions. 3.1.2.1 Intra-Performance Attribute (I-P)The associated work theses referexclusively to indicators within oneperformance attribute, for examplewithin flexibility or costs. In this case,

the theses must be positively correlatedand “synchronous.” For example, a highor low value for one variable means asimultaneous high or low value for theother variable. The interdependencybetween delivery performance and fillrate is an example of this.

The circumstances in questionbecome more apparent by use of anexample from the computer industry,which shows the connection betweeninventory and operating margin. In thisway, a rough estimate of the annualinventory costs for the PC business canbe reached through the aggregation ofinvestment cost (10 percent) and theprice erosion (25 percent), which

together represent a total of 35 percentof the inventory value. A good approachis to calculate the inventory managementcosts for a period of ten days: 10 daysmultiplied by 35 percent and divided by365 days makes 1 percent. This meansthat the reduction or increase of 10 daysof inventory (days on stock, DOS)influences the profitability by onepercent.450

One of the Level 1 metrics

allocated to the performance attributecosts is that of total Supply ChainManagement costs, and it isimmediately affected by the processdescribed above. The inventorymanagement cost as a percentage of

revenue is subsumed into this metric,reflecting the aforesaid connectionbetween inventory and operating margin. 3.1.2.2 Intra-Competence (I-C)In this case the theses include indicatorsbelonging to various performanceattributes. The performance attributes,however, lie on one side of the equation,i.e., they are without exception either ofa customer-orientated or businessinternal nature. Examples of these arefill rate and response time.

Geimer and Becker assume that theindicator within a competence (customercentric vs. business internal) representsthe variety of performance perspectives,which guarantee the balance between the

various objectives. This balance isimportant for the company’s overallsuccess. It would not be purposeful toshorten order fulfillment lead time, forexample, without taking intoconsideration the effect upon productionflexibility.451

A further example can be found

within the business-related competence.The Supply-Chain Council deliberatelydifferentiated between the terms leadtime and cash-to-cash cycle time withinthe SCOR model. Lead time precipitatesitself not only in the form of the totalSupply Chain Management cost, but alsoin the form of inventory days of supply.This differentiation between the terms is

relevant for a company’s competitiveposition and can be made as follows: T h e lead time is associated with a

product or service provided by theSupply Chain. Lead time is therefore“imposed upon” the Supply Chain andis dependent upon customerexpectations, Supply Chaininnovations, and competitivepressure.

The cycle time is based directly uponthe Supply Chain processes. Thelowest possible cycle time for aproduct’s Supply Chain is representedby the sum of all single cycle times,i.e., the sub-processes’ cycle times.

The main reason for carrying out an

inventory and incurring the associatedcost lies in the inequality between leadtime and cycle time. If the lead timedemanded by the market is lower thanthe respective cycle time, inventory ofstock is required. It is also valid to saythat the larger the inequality, the greaterthe required stock level. For this reason,leading companies are constantlystriving for synchronization of theoperating procedures and efficientdesign of the procurement andproduction processes with the aim ofreducing the difference.452

The company Dell453 has, for

example, reduced the lead times forcustomer-specific computers so far that

it possesses a positive cash-to-cashcycle time.454 Dell therefore receivespayments from customers even beforethe suppliers’ invoices have to be paid.However, this must not be taken as proofthat inventory stock is no longernecessary: it means rather that Dell haspurely reduced its own stock holdings,and the burden of this has in turn to beborne by its suppliers.455

3.1.2.3 Inter-Competence/PerformanceAttribute (I-CP)In this case the work theses refer toindicators, which belong to variousperformance attributes and additionallylie on differing sides of the equation(i.e., customer-orientated as well as

business-related). An example here isthe case of fill rate and total SC cost.

The SC competences –performance capability and efficiency –have already been addressed in moredetail elsewhere.456 The connectionbetween the two terms can be illustratedby an example of the fundamentalconflicting objectives: It makes no sensefor a supplier to invest intensively in theconstruction of production capacitiesand inventory in order to build up apotential loss of sales in products withlow margins. On the other hand, it couldbe thoroughly purposeful to do this in thecase of products with high profitmargin.457 A high value of the one

variable is linked to a low value of theother variable, and vice versa.

This connection was alreadyillustrated in the so-called SC driver/SCcompetence approach according toHugos. In this approach high productioncapacities determine a high SupplyChain performance capability(responsiveness), but on the other hand asimultaneously low level of efficiency.In opposition to this, low productioncapacities determine a high level ofefficiency, but on the other hand a lowlevel of responsiveness. The followingapplies to the respective products: Highstock levels determine a high level ofresponsiveness, but lead on the other

hand to low level of efficiency. Contraryto this, low inventory stocks maydetermine a high level of efficiency butdetermine on the other hand asimultaneously low level ofresponsiveness.458 Meyr et al describethe connection as a trade-off betweencustomer service and inventoryinvestments.459

A further example of this dynamic

is the connection between customerservice and investment in assets, i.e.,capital expenditures. The cash-to-cashcycle time depends amongst other thingsupon the industry within which thebusiness finds itself. For example, in theFood and Beverage industry relatively

short payment and cycle times arenormal. However, a tendency can berecognized whereby leading companieshave a cash-to-cash cycle time half aslong as that of their competitors.Alternatively formulated, thesecompanies receive their payments withinhalf the time, which creates an obviouscompetitive advantage for them, becausethey have lower for working capitalexpenses.460 Typically, the associatedperformance indicators correspond tothe delivery performance: Companiesshowing a high delivery performanceoften have fewer customer problemswith regards to wrong or belateddeliveries, incorrect delivery documentsor incorrect invoices. This in turn results

in shorter cash-to-cash cycle times forthe firms in question, because, forinstance, customers have less reason todefer a payment.461

There are a number of further

reasons why a higher level of deliveryperformance has financial advantagesand connects customer service on theone hand and costs with investments inassets on the other. Companies withshorter lead times and a high degree ofcustomer service can significantlyincrease their operating procedures. Bythese means a business may undertake astrategic adjustment of customer groupsand financial and business objectives. Inaddition to this, the development of

better customer relations is madepossible, and this has a direct influenceupon the customer retention rate and thecosts associated with it.462 Schary andSkjott-Larsen combine the facts of theillustrated case as follows:

“The objectives of the supplychain become a difficultbalance. (_) The dominantpurpose is to provide serviceto final customers, to deliverproducts reliably, as rapidlyand with as much variety aspossible. Service, however,commits resources and incurscosts. Supply chainmanagement must seek to

control assets and cost toobtain profit as a return on theassets employed.”463

3.1.2.4 Formulation of hypotheses andtheses modelIn this case we are dealing primarilywith bivariate suppositions ofcorrelation which – in the face ofcontinual variables (level of interval-scale) – can be investigated by means ofcorrelative procedures with the aid ofthe usual Product-Moment Correlationaccording to Pearson.464

The respective theses or hypotheses

contain relationships between variables,whereby the variables represent SCOR

performance measures. These types ofrelationship may be defined moreexactly using several criteria, whereby adifferentiation can be made betweenvarious fundamental instances, as is thecase with deterministic or statistically-variable relationships.465 Several ofthese relationships are of particularrelevance within the framework oftheses formulation and they are appliedin conjunction with this.

The theses model is built uphierarchically as already explained, onthe levels Performance Attributes –Level 1 Metrics and PerformanceMeasures. The theses formulationfollows this hierarchical structure,

whereby the starting point is the lowestlevel, i.e., the performance measures.The respective theses foundations ormodel assumptions, on the other hand,start at the base of the highest level, i.e.,the performance attributes. The thesesinvestigation therefore takes place uponthe performance measures level and theconclusion of model assumptioninvestigation is carried out by means oftheses aggregation in the form of Metatheses based upon the performanceattributes.

The allocation of each SCORmonitoring process to each performancemeasure is possible, but has only apredetermined influence upon the theses

formulation. The reason for this is that acomparison of the performance measuresallocated to the various monitoringprocesses can occasionally make sense.This may be seen in the performancemeasure Percentage of purchase ordersreceived on time and completeallocated to the procurement process,and the performance measure Backordervalue allocated to the delivery process.The investigation of a potentialconnection between these two quantitiesis of great interest.

The following illustrations showthe theses model in graphical form, anddifferentiate between the derived SCORmodel groups. In this case, the cross-

hatched areas purely represent examplesfor the actual SCOR model group.

Diag. 3-2: Supply Chain competence and keyperformance indicators as building blocks of a SCOR-based theses model: Intra-Performance Attribute (I-P)

Diag. 3-3: Supply Chain competence and key

performance indicators as building blocks of a SCOR-based theses model: Intra-competence (I-C)

Diag. 3-4: Supply Chain competence and key

performance indicators as building blocks of a SCOR-based theses model: Inter-Competence/Performance

Attribute (I-CP)

In this way, and with regards to themodel group Intra-performance attribute(I-P), the other three performanceattributes (customer service, costs,assets) are investigated in addition to theillustrated Flexibility. This also appliesto the other SCOR model groups andtheir associated performance indicators.

Before the work theses due to be

examined are extracted on the basis ofthe developed theses model, we mustconsider possible variants. As the thesesmodel to be investigated is concretelydesigned around Supply Chain specificperformance indicators, particularattention must be paid to alternativeapproaches for the evaluation of theSupply Chain’s performancepotential.466

3.1.2.5 Variations in approach andmodels for the illustration andmeasurement of the Supply ChainperformanceRummler and Brache identify three

fundamental performance dimensions intheir approach to the measurement ofSupply Chain performance. Theydifferentiate between quality,productivity and cost-orientatedperformance indicators. Furthermore,they differentiate between threeperformance levels: the organizational,process, and workplace or employeelevel.467 The first level performanceindicators are characterized by theindividual market requirements andsuccess-determined functions of theorganization. Additionally, the type andscope of the performance indicatorsbeing observed are orientated upon theorganizational strategy, the organization-wide objectives, and the organizational

structure.

Neuhäuser-Metternich and Wittdefine four performance areas anddivide them into time, cost, quality, andperformance-related measures.468

Sellenheim differentiates five varyingdimensions and uses flexibility andpreparation-orientated indicators, inaddition to cost, quality and time-relatedperformance indicators.469 Beischel andSmith similarly identify five dimensionscritical to success and use cost, qualityand flexibility, as well as service andresource-related performanceindicators.470

The aforementioned approaches are

united in their premise that non-monetaryperformance dimensions (i.e., quality,time, flexibility and productivity) are ofevidently higher importance and thatmonetary performance dimensionsshould only receive marginalconsideration.471 One reason for this canbe seen in the fact that non-financialperformance indicators can beaccommodated in a process model likeSCOR,472 because the causes ofdeviation in performance are madeapparent and targeted correctivemeasures are made possible. If a fall inquality (i.e., increase in reject rate orincrease in repair quota) is noted,respective measures can be takenimmediately.473

However, the use of non-monetary

performance indicators carries with itinevitable respective disadvantages orrestrictions, e.g., that non-monetaryindicators cannot be aggregated soeasily.474 Furthermore, an associationbetween those improvements determinedby non-monetary indicators and theachieved profit is difficult to establish.Therefore, it cannot be basicallyassumed that the improvementsmeasured upon the basis of non-monetary quantities really have an effectupon financial results. In this way,seeking a monetary quantification offalling production cycle times canbecome a difficult venture. On the other

hand, the occurrence of a slump in salesdue to falling degree of delivery abilityand the associated effects upon profitscan be easily estimated.475

It was for this reason that expanded

models or respective approaches weredeveloped, and these explicitly includemonetary in addition to non-monetaryindicators.476 Greene and Flentov setdown three performance levels as abasis for this: The business or factorylevel, the functional level, and the workplace level. The first level performanceindicators measure the respectivecompany’s or factory’s performancewith regards to the achievement ofcritical market and competition-related

success factors. The performancecapability of functionally overlappingprocesses is measured on this level. Thefunctional level’s indicators are used inorder to measure the contribution of eachfunction towards the achievement of thecompany’s strategic objectives. Theymeasure how effectively the resourcesare used to fulfill the given strategic andtactical objectives. Finally, theperformance indicators of the third levelmeasure the production performance atworkplace level. Their main task is theearly highlighting of deviations, so thatcorrective actions can be taken at anearly stage.477

Utzig differentiates four

performance levels: Market level,corporate level, factory level, andworkplace level. The market levelindicators are to measure the overallbusiness compared to the competition, inorder to determine the company’scompetitive position. The performanceindicators applied here can refer toquality, service, costs, and market share.On the corporate level we are dealingwith assessment of the actual revenuesituation, as well as the assessment ofrevenue potential. The indicators are forinstance the annual surplus, Return onAssets (ROA) or Return on Sales(ROS),478 and the market share. On thefactory level, financial and non-financialindicators are applied; for example cost,

productivity and delivery times. On thelowest level, workplace performance isassessed and indicators such as stocklevels and cycle times play a supportiverole.479

Apart from scientific approaches

there are also a number of models frompractical business that contain monetaryas well as non-monetary performanceindicators, as for example the Tableaude Bord. This mainly integratesquantitative performance indicators;qualitative indicators are merelyallocated an inferior importance. Theindividual indicators are extracted top-down from the business strategy in orderto enable an association between the

operational activities and the strategicbusiness objectives. The performanceindicators contained therein areorientated towards both the long-termand the short-term.480

The J. I. Case approach comprises

a multitude of non-financial andfinancial performance indicators, andassesses not only quantitative but alsoqualitative measures (i.e., for thepurpose of estimation of customersatisfaction).481 With the Harmanapproach, monetary and non-monetaryperformance indicators are alsointegrated. Qualitative measures are, onthe other hand, not included. Theindicators are extracted top-down from

the critical success factors, whereby aconsistent targeting of all business levelsis sought.482 The Caterpillar approachintegrates monetary and non-monetary,as well as hard and soft performanceindicators (for example customer andemployee satisfaction).483 On the otherhand, the Skandia-Navigator contains amultitude of non-financial indicators andthereby integrates quantitative andqualitative performance indicators.Apart from internal stakeholders (i.e.,employees), it also includes externalstakeholders (i.e., customers) in theconcept.484

T h e Data Envelopment Analysis

provides an ultimate indicator, the so-

called efficiency value. However, it isstill possible to consider several input-and output-related performanceindicators. In this case the approach issufficiently flexible enough to includethe time, adjustment and monitoringobjective and the dimensional, format-and output-related dimension. However,the performance data is so stronglyaggregated that it is no longertransparent enough, especially in thecase of operational areas.485 Theperformance measurement matrixcontains both monetary and non-monetary, in addition to internally- andexternally-orientated performanceindicators. The approach necessitatesthe development of strategy-conformant

figures as well as their level-specificadjustment.486

T h e Performance Pyramid

combines monetary and non-monetary aswell as internally- and externally-orientated performance indicators.These indicators are extracted top-downfrom the business strategy. There isdifferentiation between variousperformance levels, and highlyaggregated financial information isprepared for the higher business levels,in addition to transparent performancedata prepared for the operationalbusiness areas. During this, primarilyquantitative performance indicators areapplied. Qualitative indicators, on the

other hand, are only considered within alimited scope.487

Kaplan and Norton’s Balanced

Scorecard (BSC), which has alreadybeen addressed488 uses the categories offinances, customers, and processes, inaddition to those of innovation, andgrowth. The applied performanceindicators refer in this instance to thedimensions of cost, quality, time andproductivity.489 The BSC hasincreasingly developed itself into arespective de facto-standard orreference model for performancemeasurement.490 The QuantumPerformance Measurement Approachalso integrates costs (monetary) in

addition to quality- and time-related(non-monetary) performance indicators.The dimensions represented aretherefore cost, quality and time. Inaddition to quantitative data, qualitativeperformance data is taken intoconsideration here. The approachdifferentiates between three performancelevels: organizational (more long-termorientated), process, and employeelevels (set up for more middle- or short-term).491

Of the respective approaches or

models represented, only thePerformance Pyramid, BalancedScorecard and Quantum PerformanceMeasurement Approach show a

relatively strong process orientation. Asa process reference model, then, they aredecidedly compatible with the SCORmodel. The other approaches andmodels are only weakly or moderatelyhighlighted in this aspect. In turn, of thethree approaches the BalancedScorecard alone shows an individualperspective for process-relatedperformance indicators. It is therefore,as far as the model structure isconcerned, the most closely related tothe concrete model due to be examined,i.e., a performance indicator-specificdepiction of the SCOR model. It must,however, be borne in mind that the BSCin its original condition is not explicitlydirected at Supply Chain processes. The

special requirements to be considered inorder to focus upon Supply Chainprocesses have already been discussedin Chapter 1.492

As the associated effects

fundamental to the BSC and the SCORmodel are consequently founded upon avariety of performance indicators, noapplicable examples can be found forcomparison with the association effectscontained within the theses introduced inthe study. The study is exploring newterritory, as it were. Furthermore, it mustbe borne in mind that the SCOR modelfalls under the above category of amodel that explicitly focuses upon non-monetary performance indicators. The

restriction inevitably resulting from thisis that financial quantities are notexpressly included.493 Within theframework of the questionnaire chosenfor the implementation of the empiricalexamination, however, financially-related data was partially collected, asfor example in the case of revenue orReturn on Assets (ROA). During theevaluation, this information (if it wasconsidered worthwhile) was used foradditional discriminatory evaluations inorder to identify whether this would leadto relevant changes in the respectiveresults extracted from them. This will bedealt with more closely later.494

Although efforts do exist to arrive

at a standardized, SCOR-based SupplyChain Scorecard (a quasi ReferenceSCORCard),495 these efforts have, upuntil now, remained of a project-specificnature.496 To date, no empiricalexamination of these approaches from ascientific viewpoint has, in the author’sknowledge, taken place.497 This pointwill be taken up again in connectionwith suggestions for further research atthe end of the work.498

3.2 Derivation of the CentralWork ThesesBuilding upon the theses model and itsfoundations, as discussed above, thehypotheses to be examined must be

extracted and formulated.499 Here theavailable and applied data was of greatimportance and had an immediate effectupon the derivation of the hypotheses.The basic variables that were assignedduring this can be taken from theAppendix: Section 3 of the Appendix isan overview of the applied performanceindicators and section 4 contains theexact definitions and calculativeformulas, along with information on eachof the sixty or so performance measuresadopted.

The theses that follow refer directlyto the performance measures or, moreexactly, to a combination of twoperformance measures (the bivariate

assumption of correlation). The Metatheses that are outlined at the beginningof every paragraph are situated upon ahigher level, namely that of theperformance attributes. It must be bornein mind that not every single thesisallocated to a Meta thesis correlates asstrongly with the respective Meta thesis.It is also necessary to take intoconsideration the indicator-specificdifferences in performance termsexplained at the end of paragraph 3.1.1.

For this reason, an operationalexploration of the single theses isessentially more precisely andimmediately possible than in the case ofthe Meta theses. Thus, the Meta theses

serve purely as verification upon anaggregated level, whereas the detailedevaluation takes place upon the singletheses, and therefore upon theperformance measures level. 3.2.1 Theses of the SCORmodel Groups Intra-Performance Attribute (I-P)

META THESIS I:

The Performance Measures withinone Performance Attribute conform toone another (Performance MeasuresConsistency Criteria). 3.2.1.1 Performance AttributeCustomer Service (reliability and

responsiveness)500a. Delivery performance and fill rate:

Thesis 1: A high on-timedelivery percentage – inbound andoutbound leads to a high customerretention rate.

b. Delivery performance and perfect

order fulfillment:Thesis 2: A high perfect orders

rate leads to a high customerretention rate.

Thesis 3: A low on-time

delivery percentage – inbound andoutbound correlates with a lowperfect orders rate.

c.Delivery performance and orderfulfillment lead time:

Thesis 4: A high averagemanufacturing cycle time correlateswith a low on-time deliverypercentage – inbound and outbound.

d. Fill rate and perfect order fulfillment:

Thesis 5: If a high percentage ofpurchased orders received on timeand complete is present, there issimultaneously also a high perfectorders rate.

e.Fill rate and order fulfillment lead

time:Thesis 6: A high average MPS

plant delivery performance – work

orders leads to a short averagemanufacturing cycle time.

f. Perfect order fulfillment and order

fulfillment lead time:Thesis 7: A short average

purchase requisition to deliverycycle time determines a high lineson-time fill rate.

3.2.1.2 Performance AttributeFlexibility501a. Supply chain response time and

production flexibility:Thesis 8: A high inventory

stockout percentage leads to a highbackorders value.

3.2.1.3 Performance Attribute Cost502a. Total supply chain cost and cost of

goods sold:Thesis 9: High purchasing cost

as a percentage of revenue correlateswith high inventory management costas a percentage of revenue.

b. Total supply chain cost and value

added productivity:Thesis 10: High inventory

management costs as a percentage ofrevenue accompany a high inventorymanagement cost per FTE.

Thesis 11: High transportation

cost as a percentage of revenue

correlates with high transportationcost per FTE.

c. Total supply chain cost and warranty

cost or returns processing cost:Thesis 12: A high

transportation cost as a percentageof revenue correlates with a lowamount of damaged shipments.

d. Cost of goods sold and value added

productivity:Thesis 13: High purchasing cost

as a percentage of revenueaccompanies a high purchasing costper FTE.

e. Value added productivity and

warranty cost or returns processing

cost:Thesis 14: High customer

service cost per FTE correlates witha low amount of customer disputes.

Thesis 15: High transportation

cost per FTE correlates with a lowamount of damaged shipments.

3.2.1.4 Performance Attribute Assets503a. Cash-to-cash cycle time and inventory

days of supply:Thesis 16: A high inactive

inventory percentage accompanies alow average inventory turnover.

b. Cash-to-cash cycle time and asset

turns:

Thesis 17: A high inactiveinventory percentage accompanies ah i g h average warehousing spaceutilization.

c. Inventory days of supply and asset

turns:Thesis 18: A high average

inventory turnover correlates with al o w average warehousing spaceutilization.

3.2.2 Theses of the SCORmodel Groups Intra-Competence (I-C)3.2.2.1 Customer-facing indicators504

Customer service (reliability andresponsiveness) vs. flexibility:

META THESIS II:

A high (low) customer servicecorrelates with a high (low) flexibility. a. Delivery performance and supply

chain response time:Thesis 19: A high on-time

delivery percentage – inbound andoutbound accompanies a lowbackorders value.

b. Delivery performance and production

flexibility:Thesis 20: A low inventory

stockout percentage correlates with

a high on-time delivery percentage –inbound and outbound.

c. Fill rate and supply chain response

time:Thesis 21: A high percentage of

purchased orders received on timeand complete contributes to a lowbackorders value.

Thesis 22:A high percentage of

purchased lines received on time andcomplete contributes to a lowbackorders value.

d. Fill rate and production flexibility:

Thesis 23: A high inventorystockout percentage correlates with a

l o w average MPS plant deliveryperformance – work orders.

e. Perfect order fulfillment and supply

chain response time:Thesis 24: A high lines on-time

fill rate contributes to a lowbackorders value.

f. Perfect order fulfillment and

production flexibility:Thesis 25: A low inventory

stockout percentage correlates with ahigh perfect orders rate.

Thesis 26: A low inventory

stockout percentage contributes to ahigh lines on-time fill rate.

g. Order fulfillment lead time and supplychain response time:

Thesis 27: A short averagemanufacturing cycle time contributesto a low backorders value.

h. Order fulfillment lead time and

production flexibility:Thesis 28: A high inventory

stockout percentage correlates with ahi gh average manufacturing cycletime.

3.2.2.2 Internal-facing indicators505Cost vs. assets:

META THESIS III:

High (low) costs correlate withhigh (low) asset investment. a. Total SCM cost and cash-to-cash

cycle time:Thesis 29: High inventory

management cost as a percentage ofrevenue correlates with a highaverage received finished goodsturnaround time.

Thesis 30: High inventory

obsolescence cost as a percentage ofrevenue accompanies a high inactiveinventory percentage.

b. Total SCM cost and inventory days of

supply:Thesis 31: High inventory

obsolescence cost as a percentage ofrevenue accompanies a low averageinventory turnover.

c. Total SCM cost and asset turns:

Thesis 32: High inventorymanagement cost per customer ordercorrelates with a low averagewarehousing space utilization.

d. Value added productivity and cash-to-

cash cycle time:Thesis 33: High inventory

management cost per FTE is found inconjunction with a high averagereceived finished goods turnaroundtime.

e. Value added productivity andinventory days of supply:

Thesis 34: High inventorymanagement cost per FTE is found inconjunction with a low averageinventory turnover.

f. Value added productivity and asset

turns:Thesis 35: A high average

throughput per FTE correlates with ahigh average plant capacity utilizationfor finished products.

Thesis 36: High inventory

management costs per FTEaccompany a low averagewarehousing space utilization.

g. Warranty cost or return processing

cost and cash-to-cash cycle time:Not applicable.506

h. Warranty cost or return processing

cost and inventory days of supply:Thesis 37: A low average order-

to-shipment lead time correlates witha low amount of customer disputes.

i. Warranty cost or return processing

cost and asset turns:Not applicable.507

3.2.3 Theses of the SCORmodel Group?Inter-

Competence/PerformanceIndicator (I-CP)3.2.3.1 Customer Service (reliabilityand responsiveness) vs. cost508

META THESIS IV:

A high (low) customer servicecorrelates with high (low) costs. a. Delivery performance and total SCM

cost:Thesis 38: High inventory

management costs as a percentage ofrevenue correlate with a lowbackorders value.

b. Delivery performance and cost of

goods sold:

Thesis 39: High customerservice cost as a percentage ofrevenue accompanies a high on-timedelivery percentage – inbound andoutbound.

c. Delivery performance and value

added productivity:Thesis 40: High customer

service cost per FTE correlates witha high on-time delivery percentage –inbound and outbound.

d. Delivery performance and warranty

cost or returns processing cost:Thesis 41: A low number of

customer disputes accompanies a highcustomer retention rate.

e. Fill rate and total SCM cost:

Thesis 42: A high cycle countaccuracy percentage correlates with ahigh inventory management cost as apercentage of inventory value.

f. Fill rate and cost of goods sold:

Thesis 43: A high percentage ofpurchased orders received on timeand complete corresponds to a highpurchasing cost as a percentage ofrevenue.

g. Fill rate and value added

productivity:Thesis 44: A high percentage of

purchased orders received on timeand complete, accompanies a high

purchasing cost per FTE.

Thesis 45: A high manufacturingcost per FTE correlates with a highaverage MPS plant deliveryperformance – work orders.

h. Fill rate and warranty cost or returns

processing cost:Thesis 46: A high percentage of

purchased orders received on timeand complete correlates with a lowamount of damaged shipments.

Thesis 47: A high average MPS

plant delivery performanceaccompanies a low amount ofcustomer disputes.

i. Perfect order fulfillment and total

SCM cost:Thesis 48: High inventory

management costs per customer ordercorrelate with a high perfect ordersrate.

Thesis 49: A high inventory

management cost per customer orderaccompanies a high lines on-time fillrate.

j. Perfect order fulfillment and cost of

goods sold:Thesis 50: A high customer

service cost as a percentage ofrevenue correlates with a high perfect

orders rate. k. Perfect order fulfillment and value

added productivity:Thesis 51: A high customer

service cost per FTE contributes to ahigh lines on-time fill rate.

l. Perfect order fulfillment and warranty

cost or returns processing cost:Thesis 52: A low perfect orders

rate correlates with a high amount ofcustomer disputes.

m. Order fulfillment lead time and total

SCM cost:Thesis 53: A low average

purchase requisition to delivery cycletime accompanies a high inventory

management cost as a percentage ofrevenue.

n. Order fulfillment lead time and cost of

goods sold:Thesis 54: A high average

purchase requisition to delivery cycletime correlates with a low purchasingcost per purchase order.

o. Order fulfillment lead time and value

added productivity:Thesis 55: A high purchasing

cost per FTE correlates with a highaverage purchase requisition todelivery cycle time.

Thesis 56: A high manufacturing

cost per FTE stands opposite to a lowaverage manufacturing cycle time.

p. Order fulfillment lead time and

warranty cost or returns processingcost:

Thesis 57: A low averagepurchase requisition to delivery cycletime accompanies a low amount ofcustomer disputes.

3.2.3.2 Flexibility vs. cost509

META THESIS V:

A high (low) Supply Chain-flexibility correlates with high (low)costs.

a. Supply chain response time and totalSCM cost:

Thesis 58: A high inventorymanagement cost as a percentage ofinventory value accompanies a lowbackorders value.

b. Supply chain response time and cost

of goods sold:Thesis 59: A high manufacturing

cost as a percentage of revenuecorrelates with a low backordersvalue.

c. Supply chain response time and value

added productivity:Thesis 60: Low customer

service costs per FTE take place in

conjunction with a high backordersvalue.

d. Supply chain response time and

warranty cost or returns processingcost:

Thesis 61: A low backordersvalue correlates with a low amount ofcustomer disputes.

e. Production flexibility and total SCM

cost:Thesis 62: A high inventory

stockout percentage correlates with ahigh inventory obsolescence cost as apercentage of revenue.

f. Production flexibility and cost of

goods sold:

Thesis 63: A low inventorystockout percentage accompanies ahigh manufacturing cost as apercentage of revenue.

g. Production flexibility and value added

productivity:Thesis 64: A high manufacturing

cost per FTE correlates with a lowinventory stockout percentage.

Thesis 65: A high customer

service cost per FTE takes place inconjunction with a low inventorystockout percentage.

3.2.3.3 Customer Service (reliabilityand responsiveness) vs. assets510

META THESIS VI:

A high (low) customer servicecorrelates to high (low) assets. a. Delivery performance and cash-to-

cash cycle time:Thesis 66: A high on-time

delivery percentage – inbound andoutbound correlates with a lowinactive inventory percentage.

b. Delivery performance and inventory

days of supply:Thesis 67: A high average

inventory turnover is simultaneous toa low backorders value.

Thesis 68: A low average order-

to-shipment lead time accompanies ahigh on-time delivery percentage –inbound and outbound.

c. Delivery performance and asset turns:

Not applicable.511

d. Fill rate and cash-to-cash cycle time:

Thesis 69: A high cycle countaccuracy percentage correlates with alow inactive inventory percentage.

e. Fill rate and inventory days of supply:

Thesis 70: A high percentage ofpurchased lines received on time andcomplete accompanies a low averageorder-to-shipment lead time.

f. Fill rate and asset turns:

Not applicable.512

g. Perfect order fulfillment and cash-to-

cash cycle time:Thesis 71: A high inactive

inventory percentage takes placemutually with a high lines on-time fillrate.

h. Perfect order fulfillment and inventory

days of supply:Thesis 72: A high average

order-to-shipment lead timecorrelates with a high lines on-timefill rate.

i. Perfect order fulfillment and asset

turns:

Not applicable.513

j. Order fulfillment lead time and cash-

to-cash cycle time:Thesis 73: A high amount of

transactions processed via web/EDIaccompanies a low average receivedfinished goods turnaround time.

k. Order fulfillment lead time and

inventory days of supply:Thesis 74: A high percentage of

sales via web/EDI correlates with alow average order-to-shipment leadtime.

l. Order fulfillment lead time and asset

turns.

Not applicable.514

3.2.3.4 Flexibility vs. assets515

META THESIS VII:

A high (low) flexibility correlateswith high (low) assets. a. Supply chain response time and cash-

to-cash cycle time:Thesis 75: A low average

received finished goods turnaroundtime correlates with a low backordersvalue.

b. Supply chain response time and

inventory days of supply:Thesis 76: A high average

inventory turnover occurssimultaneously with a low backordersvalue.

c. Supply chain response time and asset

turns:Not applicable.516

d. Production flexibility and cash-to-

cash cycle time:Thesis 77: A low inventory

stockout percentage correlates with alow average received finished goodsturnaround time.

e. Production flexibility and inventory

days of supply:Thesis 78: A low inventory

stockout percentage accompanies alow average order-to-shipment leadtime.

Thesis 79: A high inventory

stockout percentage accompanies alow average inventory turnover.

f. Production flexibility and asset turns:

Thesis 80: A low inventorystockout percentage correlates with ahigh average operating-equipmentefficiency rate – OEE for finishedproducts.

3.3 Planning and Design ofthe Empirical Examination

3.3.1 Sources of information(types of method forinformation retrieval)There are two types of informationretrieval: primary and secondaryresearch. Primary research, alsoreferred to as field research, is the caseif one conducts one’s own investigationsin order to receive information. In thiscase one is therefore working with dataunknown prior to commencing one’sown research.517

Secondary research occurs when,

during the acquisition of data, materialalready available, which was collectedby other institutions for other purposes,

is used. In this case, the object of thesecondary research is the collection andevaluation of data that was identifiedand retrieved at an earlier point in timeand for other purposes.518 Data istherefore submitted to a second or thirdevaluation, which is why we speak ofsecondary research as opposed toprimary research, i.e., the first survey ofdata referring to a concretely describedobjective. Since the renewed processingof the data can take place mainly at adesk or in an office, the term deskresearch519 may also be found todescribe this procedure.

This study employed a two-stageapproach with regards to the type of

information retrieved. In the first stageduring the primary research, data wascollected within industrial companies. Inthe second stage this data was used assecondary research for a purpose otherthan the original, within the frameworkof the examination carried out. 3.3.2 Data collection andsampling methodsThe relevance of any observational orinformative source in a primary surveyis governed above all else by nature ofthe information required. During thecourse of a survey the range of elementsused to reach specific conclusions isdescribed as the total mass or universe.A full survey (or total survey) is one in

which every individual element isexamined for characteristics of interest.In most cases, however, such a totalsurvey proves to be practicallyimpossible within a large scope ofpopulation, often for financial, time ororganizational reasons. For reasons ofresearch economy, the examination islimited to a part of the population, theso-called partial mass or sample.520

The selection procedures or spot

check techniques used today may bejudged according to two criteria whichpartially overlap each other: the resultvalidity or obligation, and theapplication of selection criteria:521

1. Result validity or obligation:In accordance with the validity of the

results, a differentiation can be madebetween those procedures that lead torepresentative results and those thatdo not. Representative procedures canbe further divided into simple andcomplex random sampling.522

2. Application of selection criteria:The selection criteria to be applied are

then re-classified as random samplingand non-probability sampling. Thoseselection procedures considered asnon-probability sampling include theaccidental sampling or sampling ofavailable subjects on the one side,and quota sampling, sampling oftypical cases, snowball sampling and

quota sampling on the other side. Inthose cases, subjective decisions arerequired at some phase of theselection.523

A similar division can be found in

the work of Hammann and Erichson,who differentiate between randomsampling and non-probabilitysampling.524 Therefore, whilst randomsampling is based upon randomselection mechanisms, the procedure ofpurposive sampling comprises aselected sub-set. The selection takesplace in a targeted and considered wayand according to factually relevantcharacteristics.525

Sampling of typical cases isespecially suitable forexaminations that testhypotheses. The premise ofthis method lies in itsrestriction of the analysis to arelatively low number ofpopulation elements, whichare considered to becharacteristic or especiallytypical. Each individuallyselected case should thereforerepresent a larger number.526

As this study is concerned with the

examination of the SCOR model’sdeveloped depiction, the selectioncriteria for typical data orientated

themselves upon the SCOR-specificcharacteristics of the companies foundwithin the data pool. The SCOR model’sillustration reflected in the theses modelalso covers all SCOR process areas(chevrons). For this reason, allcompanies whose data were actuallypresent in all SCOR processes andcomplete were selected for the purposeof secondary research. A sample size of73 companies resulted from this. Thereason for the failure of some companiesto give information regarding individualSCOR processes can be traced primarilyin their Supply Chain strategy. In thisway, companies that have, for instance,displaced their production (Outsourcing)could inevitably not make any statements

on the SCOR process Make. The sameprinciple applies to the other SCORprocess areas.527

Particular attention was also paid

to seeking the maximum data available,with the minimum of missing data. Tothis end, sets of data were omitted fromthe selection when a principaldeclaration to all the SCOR processareas had been given, but where partlyincomplete information was presentwithin one or more areas. By thesemeans, a distortion of the results due to afluctuating sample size – and associatedsample consistency – could be avoided,and the set of data could be monitored asexactly as possible. This will be more

closely dealt with in paragraph 3.3.5. 3.3.3 Survey typesWhen conducting primary researchsurveys, observations and experimentsare considered possible methods of datacollection. The most commonly usedmethod in this respect is that of thesurvey. Surveys involve the subjectcontributing either factual information oran expressed opinion (judgment).Depending upon the type of approach itis possible to differentiate between anunsystematic (improper) and asystematic survey.528 Hammann andErichson use the followingdifferentiation between the types ofsurvey:529

1. Written survey2. Verbal survey: Personal interview,

telephone survey3. Electronic survey, either as an online

or offline survey.

Often, the survey is carried out withthe aid of a questionnaire. In this case,the questions are posed in a standardizedform, and answered accordingly. Thepersons in question therefore receivesurvey stimuli via the questions they aregiven, and provide their owninformation as input to thequestionnaire.530

An electronic survey (orprecisely, an online survey)

was assigned in the case of theprimary research. This, inaddition to the data appliedwithin the framework of thework submitted, formed thebasis for the secondaryresearch.531

Data retrieval for primary

examination purposes took place in theperiod between the middle of 2001 untilthe end of 2003. In total, 170 companieswere included in the survey. Theanswers to the questions were divided inaccordance with the SCOR mainprocesses (chevrons). The responsibilityfor the provision of the answers lay withthe managers of the respective process

areas. For instance, the procurementmanager was responsible for thequestionnaire block Source(purchasing).532 The answers to finance-related questions were the responsibilityof the financial manager or ChiefFinancial Officer (CFO), who in mostcases also acted as the sponsor of thecompany surveyed.533 This, of course,does not exclude the fact that furtherlevels within the hierarchy wereincluded into the examination duringdata collection at various points. 3.3.4 Design of the appliedquestionnaireThe issue of the questionnaire’sconstruction plays an important role in

determining the success of a survey,particularly in the arrangement andsequence of the questions. Over time, anumber of basic rules have beendeveloped for the creation ofquestionnaires, which can now beconsidered as binding. Four phases, andconsequently four groups of questions,are differentiated:534

1. Introductory phase (contact questions)2. Information retrieval phase3. Monitoring phase (control questions)4. Personal or organizational

information.

In the case of each type of question,a differentiation can be made between

closed questions and alternativequestions. In turn, closed questions canbe subdivided by looking at the radiuswithin which the categories aredetermined by the questionnaire, and theextent to which these categories areactually revealed to the person beinginterviewed.535 Apart from this, attentionmust also be paid to the externalstructure of the questionnaire.536

Alternative questioning, scalequestioning, and cataloguequestioning were assigned inthe primary survey, whichserved to collect the data usedwithin the study. The build-upfollowed the four phases

mentioned above. Thestructure of the survey tookplace in the form of an onlinequestionnaire (KPIBenchmarkingQuestionnaire).537

The primary research questionnaire

was developed within the framework ofan internal project by the businessconsultancy BearingPoint (formerlyKPMG Consulting),538 in conjunctionwith a business consultancy specializingin the design of questionnaire-basedsurveys, and completed by the beginningof 2001. At this time, SCOR Version 4was available. The changes to thisversion through Version 5 up to Version

6 (which formed the basis for the study)did not, however, influence theextrication of the theses model. This isbecause the model structure of this timealready represented the structure due tobe operationalized and analyzed in therelevant parts of the study.539

3.3.5 Practical examples foranalysisCompanies included in the study weredrawn from various regions or countries,and also from various industrial groups.This was done in order to avoid a weakdegree of generalization in the results,and to make them as universally validand industry-independent as possible.This follows a fundamental

benchmarking idea, namely to apply bestpractices for optimization of individualprocesses, as well as the industry-spanning definition of the SupplyChain’s capability.

The sample size orientates itself, asdescribed, upon the data retrieved aspart of the primary research. Theselection of practical examples tookplace upon the basis of availableempirical data material using typicalcases.540 Consequently, the results ofmore than seventy actual companieswere enlisted as examples and therespective items of data were processed.In this case, an ideal representationcould not be reached.541 Nevertheless, it

is assumed that the results obtained viathe random sampling characteristics –which are to be more closely explainedin later sections – can be taken as anempirical and scientifically strongdeclaration. In this context, it is alsonoted that empirical works with largesample sizes and with a variety ofcriteria, which have the SCOR model astheir topic, are still rare.

A differentiation according toregional distribution and industry-affiliation leads to the following resultsin the study:

The companies have their locationsin the following respective regions orcountries and are distributed as

follows:542

North America (USA and Canada):75.3 percentEurope (France, Germany,Hungary, Italy, Turkey and the UK):16.5 percentAsia (India, Indonesia andSingapore): 8.2 percent.

The companies cover the industrial

sector and comprise the followingspecific industries or industrialgroups:543

Aerospace and Defense

Agriculture and BiotechnologyApparelAutomotiveBiotechnologyChemicals and PharmaceuticalsComputers and ConsumerElectronicsConsumer Packaged Goods, CPGElectronic equipmentHousehold appliancesMachinery and EquipmentMetal ProductsOffice and Printing MachinesRubber and Plastic ProductsTelecommunicationsRetail and Distribution.

The above-mentioned distribution

refers to the 73 selected companies, butalso applies to the primary survey’s datapool. The companies must remainanonymous on the grounds of clientprotection.

A study of company distribution onthe basis of revenue and total employeesleads to the following results, attainedusing the German Commercial Code’s(Handelsgesetzbuch, HGB) guidelinesfor the measurement of companies’size:544

Small companies: 2 percentMiddle-sized companies: 37.5percentLarge companies: 60.5 percent.

With regards to their business

success, the companies underobservation distribute themselves asfollows, whereby Return on Assets(ROA)545 is the measure forjudgment:546

ROA negative: 9.6 percent of thecompaniesROA between 0 and 10 percent: 63percent of the companiesROA over 10 percent: 27.4 percentof the companies.

Of the 73 companies observed, by

the end of 2006 seven no longer existedin the same form in which they foundthemselves during the survey period.Four of them had been taken over byother companies or merged into newcompanies. Three were no longerpresent on the market, or no informationcould be obtained as to theirwhereabouts. Of those three companiesnone had shown a negative ROA duringthe period of the survey.547

The resulting variety in the data

with respect to industry-affiliationrepresents the fundamental principleupon which the SCOR model is based. Itreminds us that it is intended to be anindustry-independent business process

reference model.548 This variationinevitably conceals certaindisadvantages. Under normalcircumstances generalized statementsregarding the illustrated model’suniversal suitability (or more exactly inthis case: the SCOR model’s developeddepiction) are particularly targetedduring such sampling. As a result, lessemphasis is placed upon sub-groupspecific statements (whereby forexample in the sense of a clustering, theexamined sample size had to appearcritical for the latter intention).549 Thedata appeared to permit an exploratoryapproach towards this objective despitethe immanent sampling restrictions.550

A process of differentiationaccording to varying types of strategy or,in the present case, Supply Chainstrategy types (e.g., masscustomization)551 was not possible here,since the data required in order toenable such a differentiation was notacquired within the field research. Inturn, the primary research did not focusupon these data elements, because theSCOR model in the form observed here(i.e., on SCOR levels 1 to 3) does notprovide for such a differentiation.552

Those differences in this form thataddress the issue of distributionaccording to industry affiliation may befound from the fourth level onwards(i.e., upon a project-specific

granularity).553

In turn, the accompanying

heterogeneity of the set of data conceals,in the variables resulting from theindustry-independent observationrepresented above, the risk of unusualeffects which may influence results. Asfar as the extent of this risk is concerned,closer quantification is not possible dueto the above-mentioned lack of relevantdata. However, it must be pointed out inthis context that the focal point ofanalysis should, in the first instance, bethe illustrated model’s fundamentalcapacity, and not those unusual effectswhich are difficult to determine.554

Apart from these issues, as part ofthe data evaluation within the empiricalstudy and in addition to the parametersfor revenue and number of employees(see above), a company’s success in theform of Return on Assets (ROA) wasdifferentiated for test purposes using theaforementioned classification. This wasdone in order to intercept heterogeneitywithin the set of data.555 When this led toan incremental accumulation ofknowledge it is noted in the results. If nofurther declaration is made in the courseof the study, no additional conclusionscould be reached via this discriminatingobservation. 3.4 Execution of the

Empirical Examination3.4.1 Applied method for thedata survey?(primaryresearch)The business consultancy BearingPoint(formerly KPMG Consulting)556

implemented the SCOR model as animmanent component of the methodsapplied for analysis and Supply Chainoptimization. The methods used forSupply Chain transformation and thetools for that purpose are expresslyadapted and tuned to the SCOR model.The company did, however, modify andenhance the SCOR model slightly. Themost significant differences are asfollows:

1. The delivery process (Deliver) was

split into two sub-processes: astorage process (Store) and atransport process (Transport).According to BearingPoint, thelogistical functions can be moreclearly and effectively represented inthis manner.

2. The sales process (Sell) wasintroduced within the deliveryprocess. The reason given for thisstep was the need to accommodate theincreasing importance of theconnection between Supply andDemand Management.

3. New Product Development was

added. BearingPoint states that this is

because, although the process was nota part of the present SCOR model, itwill become all the more importantfor the business success in the future,for example in connection with corecompetences and the constantlyincreasing importance of Outsourcing.

4. The planning process was integratedinto the other above-mentionedprocesses and therefore is notexplicitly apparent. The consultancyexplains that this is so the planningprocesses’ functions appearthroughout the Supply Chain.

One method developed by

BearingPoint for Supply Chain analysisis represented by a questionnaire based

upon the SCOR model.557 Here, thefocal point is the Key PerformanceIndicator (KPI) which has already beenintroduced, and which is supposed to becompared to the values of othercompanies (Benchmarking). In this case,the survey is a quantitatively-orientatedone (KPI Benchmarking questionnaire).According to BearingPoint, the aim ofthis method is the comprehensiveanalysis, representation, and comparisonof firm’s Supply Chains with each otherwithin a relatively short period. As anadditional advantage, the consultancyclaims that this procedure also promotesthe build-up and transfer ofknowledge.558

3.4.1.1 Course of the examinationThe planning and preparation of theexamination was done jointly by thecustomer and the business consultancy.The individual stages were asfollows:559

1. Preparation:

Customer:

• Identification of the company’semployees taking part in theexamination and filling out thequestionnaire

• Identification of an (executive)sponsor to ensure a high returnquota.560

BearingPoint:

• Granting access rights for the onlinequestionnaire completion

• Support during data collection 2. Execution:

Customer:

• Completion of the questionnaire561

BearingPoint:

• Support during data collection.

3. Analysis:

Customer:

• Answers to further questions andavailability of further informationmaterial, if required.

BearingPoint:

• Evaluation of the collected data562

• Compilation of action points forSupply Chain’s improvement.

4. Results:

Customer:

• Validation of the results• Validation of the action points.

BearingPoint:

• Presentation of the results

• Presentation of the suggested actionpoints.

3.4.1.2 Examination resultsThe result report contained theaggregated results of collected datathroughout all the survey’s participants.The submitted results comprisedindividual reports of performancecomparison supported by graphics.Potential improvements were alsocontained within it. In addition to this,the results could be aggregated in aSCORcard format based upon the SCORprocesses.563

The result report included a

combination of graphical forms of

representation; for example, bar chartsfor the illustration of comparison groupaverages and quartile charts forstandardization of results compared tothe average value of a comparisongroup.564 From these it was possible tobuild up a detailed view for everySCOR process, originating from theoverall view. To take this line ofanalysis further, the respectiveperformance measures behind theseresults could be viewed in detail. Asthis also applied to the diagnosis level,suggestions for improvement there werealso contained in the report.565

3.4.2 Evaluation of theresults of the empirical

examination (secondaryresearch)This study evaluated the results of theempirical survey. This process must bedifferentiated from that of the survey,which the BearingPoint consultancy usedto handle their type of field research, asexplored in the previous section. In thisstudy, the results collected were usedmore in the form of secondary research(desk research). 3.4.2.1 Evaluation of dataIt is the general task of data evaluation tosort, investigate and analyze thesurveyed data, and to condense the datainto clearly visible proportions, in orderthat decisions can be reached. Data

evaluation is therefore, ultimately, aboutarriving at assertive and informativemeasurements so that immanent datacorrelations can be recognized.566

The findings (or the diagnosis part

of the study) are orientated around thetheses outlined previously and areclearly and visibly arranged inaccordance with them. Such an approachis in line with the established guidelinesfor empirical research.567 In this sense,the data present was evaluated in orderto investigate the connection between theindividual variables on an empiricalbasis and in accordance with the theses.Interpretations are thereby restrictedexclusively to the statistical durability of

the theses. Estimates or concreterecommendations for action do not playa role as far as the findings part of thestudy is concerned. On the contrary, therespective conclusions are extractedduring the later parts of the work.568

Originating from the data collected

in the survey phase, which has beenchecked for completeness, the evaluationof the data has four stages:569

1. Data preparation2. Data processing3. Interpretation4. Report and presentation of the results. 3.4.2.2 Methodology of evaluation for

the single hypothesesFor the purpose of data preparation, dataprocessing, and result presentation, anapplication program by the corporationSPSS with the same name SPSS(abbreviation for Statistical Product andService Solutions, formerly StatisticalPackage for Social Sciences)570 wasapplied in this study.571 During thisprocess, the data available to the authorwas transferred from an Excel file intothe evaluation program. Based upon this,the following statistical values wereinvestigated:572

Arithmetic Mean:573

A measure of location574 in

conjunction with metrical (i.e., atleast interval-scaled) data. This isoften also described as measure ofcentral tendency, which is (strictlyspeaking) incorrect due to theexistence of other central measures(such as a geometrical or harmoniccenter). It is calculated as the sumof the individual values of the datapackage, divided by the number ofdata elementsRange:The span in variation represents adiffused measure. It is calculated asthe difference between the highestvalue (Maximum) and the lowest(Minimum) of a data package.Standard Deviation:

The standard deviation iscalculated as the root of thevariance of a data package. As withthe variance, a distinction must bereached between the standarddeviation, which characterizes thegiven data (empirical standarddeviation), and that which iscalculated from sample data as anestimated value for the population.Type I Error:The erroneous declination of a nullhypothesis575 is described as a so-called Type I Error.576 It reflectsthe risk of declining a neutralhypothesis purely upon the groundsof the randomness of the respectivesample, therefore also of assuming

a connection or difference that defacto does not exist. A value ofP(a) = 0.05 stands for example fora respective Type I Error or errormargin of 5 percent.577

Correlation Coefficient:Correlative measures emphasizethe strength of the correlationbetween two variables. In this case,if correlations between these andfurther variables are taken intoconsideration, one speaks of partialcorrelation.

Measures for the strength of the

correlation are described, as a rule, ascorrelation coefficients. Correlation

coefficients can often take on values of aminimum of –1 and a maximum of +1. Inthis instance –1 indicates a perfectnegative and +1 a perfect positivecorrelation. The choice of correlationcoefficients is dependent upon thestandard of measurement of thevariables. Often in the case of twometric characteristics, the so-calledProduct-Moment Correlation (PMCorrelation) and the correspondingBravais-Pearson’s correlationcoefficient (BPC) are used, which instatistics is usually abbreviated by usingthe letter R.

The BPC is calculated as thecovariance578 of both variables of

interest, divided by the product of thestandard deviation of both the variables.It can adopt values between +1 (perfectpositive correlation) and –1 (perfectnegative correlation). A value of 0indicates the absence of a (linear)correlation.579 Those methodicalinfluences that may interfere with theBPC, on account of the absence of thelinearity of variable relationships (suchas polynomial progression), and therespective safety insurance measureswithin the work submitted, will be dealtwith more closely in the followingsections.

In the case of the examined theses,we are dealing with bivariate

correlation assumptions based uponpairs of respective characteristics orcontinual variables (interval-scaled) inthe shape of SCOR performancemeasures. Therefore, the BPC waschosen to be the correlation coefficient.

The minimum requirement for asignificant conclusion wasfundamentally established as P(a) < 0.05within the usual (business) scientific andstatistical criteria.580 In conjunction withthe statistical significance, it waspresumed that the result of a hypothesestest could be considered significant ifthe assumption is correct that atheoretically assumed correlation ordifference between the characteristics

found within the data is not explainedpurely by the _blurring_ associated withthe sampling approach (typicalcases).581

In addition to the respective

inferential statistic or conclusive dataanalysis,582 it is possible to achieve anillustration of the acquired knowledgefor exemplary findings – providedunambiguous systematic coupling of thevariables is present – by means ofadditional and descriptive graphics.583

In concrete terms, and for therepresentation of the usual measurementof central tendency, the use of blockdiagrams was deemed the best option.584

During this process – and in accordance

with each diagnostic situation – specialattention was paid to the issue ofwhether companies deviate from a givenbasic tendency, dependent upon factorssuch as turnover and employee numbers,or whether they _cover_ these extremelywell. This was done in order to enablethe derivation of later attempts atclarification.585

Each correlation-immanent null

hypothesis refers to _parallel running,_(i.e., no correlation) of the bivariatenumber of measures involved in thepresent case. Because missing dataplayed no role within the present case,significance-modulating effectsfollowing a variety of sample sizes

require no further consideration.586 Theillustrated statistical values are enlistedin order to confirm (verify) or reject(falsify) the established theses andarrive at an interpretation resulting fromthis. The ultimate purpose of thisprocess is the accommodation ofappropriate answers to the questionsfundamental to the examination.587

The above indication of the level of

significance applied to the inferentialstatistics must not be overlook the factthat this approach can by all meansaccommodate the acceptance of modelimages which – taken individually –contain no significant effects, but docollectively contain a homogenous

pattern. In view of this fact, thisapproach is also used during thefollowing explanation of the first stagetheses results (classic inferentialstatistic, in this case: correlationanalysis) to separate totally unsystematicresults (i.e., roughly in the area of theabsolute correlation level 0 to 0.1, inorder to demarcate partially tendencialor _strictly_ substantial and thereforestatistically significant results). Theconventional respective dichotomy ordifferentiation between significant andnon-significant will be closely observedduring the following.

This binary examination representsthe secured fundamental and statistically

inferential evaluation criteria for theexamined theses. It does, however,represent a momentous difference as towhether:

A thesis is correlatively confirmedin a significant way,A postulated bivariate correlationis to be highlighted as quite simplyunsystematic (see above), orThe example referred to presentsitself contrary to expectation in astatistically significant way.

The question of the classification to

be used within this study in order toincorporate the examined bivariate

correlations can – irrespective of therelevance of basic binary division –contribute to a varied understanding ofthe outcome.588 In accordance with this,the following groups can bedifferentiated:

Significantly model-conformantSignificantly model-contrary (i.e., acorrelation counteracting theexpectancy in a significant way)Unsystematic within the R-rangefrom -0.10 to +0.10Tendentially model-conformant ortendentially model-contrary (i.e.,indicative of the respectiveopposite but not yet significant

correlative direction).

So far, we have dealt with a purely

pragmatic illustration. During thecollective theses findings, binarydivision will also be used.589

3.4.2.3 Special evaluation procedurefor the Meta thesesFor examples chosen to concretelyinvestigate the developed Meta theses ina second stage, it was decided to assigna so-called Structural Equation Model(SEM) or, more exactly, a CovarianceStructure Model590 as an examinationmodel. Namely, it is the so-calledAMOS procedure, whereby AMOS

stands for Analysis of MomentStructures.591 AMOS was chosenbecause this procedure is able to analyzeand exchange data matrix results withSPSS.592 Because SPSS had alreadybeen used to investigate the singletheses, the desired assignment of AMOShad research-economical advantages.

Calculation procedures orprograms such as AMOS or the relatedLISREL593 are, as mentioned, to beassigned to structural equation models.This is a form of statistics which goesbeyond inferential statistics anddescriptive statistical proceduresapplied for investigation of the singletheses. In the recent past, it has

sometimes been the case that peoplehave spoken of _new generation_ inconjunction with AMOS and otherrelated procedures. The fact is,however, that these procedures werealready conceived around 30 years ago.In any case, the respective software hasin recent times increasingly developedin the direction of better usability, and istherefore available to an extended fieldof users.594

Collectively observed, therefore,

programs such as LISREL and AMOSallow more complex models to be tested_en bloc_ for compatibility. The modelsto be tested thereby usually containrespective statements or hypotheses for

the effect association of ?so-calledconstructs, which in turn normallycomprise particular single variables(indicators). AMOS and LISREL enablethe conclusive decision as to whether themodel ideals can be retained (modelconfirmation), or whether they must bedeclined (model rejection). High samplesizes, i.e., N>150 are optimal for modelinvestigation, whereby AMOS andLISREL can also calculate sample sizesfrom 40 onwards. It is something of aprecondition for this process, however,that the model due for investigation doesnot show an excessively high degree ofcomplexity.595 These circumstances willbe explored more closely in Chapter4.596

Various statistical measurements

are available for model evaluationwithin the framework of structuralequation procedures, whereby the so-called Goodness-of-Fit Index (GFI) hasgained an increasingly substantialpresence. The GFI determines aguideline, similar to a conventionallevel of significance, as to the point fromwhich a postulated model is still seen ascompatible (i.e., suitable _Fit_ betweendata and model assumptions), and fromwhich point it is considered to beincompatible. The GFI measures therelative amounts of variance andcovariance justified in total by the modeland illustrates the stability index within

the framework of regression analysis.597

The GFI can accommodate valuesbetween 0 and 1, whereby a value of 1means that all empirical variance andcovariance are exactly reflected by themodel (_perfect model fit,_ usually atheoretical case).

Another noteworthy tool within thecontext of structural equation models isthe Adjusted-Goodness-of-Fit Index(AGFI). The AGFI is similarly ameasure for the variance expressedwithin the model which, however,additionally takes into consideration themodel complexity in the form of degreesof freedom.598 The AGFI alsoaccommodates for values between 0 and

1. A model’s _Fit_ may be seen as moresuitable the closer it becomes to a valueof 1.599

Without preempting the illustration

of results that follows, it must be notedhere that inevitable conclusions canalready be drawn as to the model’scompatibility from within the frameworkof the conventional inferential statistic(therefore correlation analytical)observation of the theses. The structure-analytical approach strives to make a_holistic_ contribution to the modelevaluation.

Chapter Four

Comparison of workhypotheses and

acknowledged results ofthe empirical study

Chapter four continues the context ofjustification within the framework of theresearch-logical course, started in theprevious chapter and as defined byFriedrichs.

Evaluation, statisticalinvestigation and result

interpretation transform datainto findings and conclusions.The evaluation does not takeplace randomly, but is led bythe hypotheses. Description,analysis and explanations arethe most important parts of theinterpretation process.600

4.1 Results of the Evaluationsof the ThesesIn this section the evaluation results forthe theses developed previously will beexplained using statistical procedures. Inthis context, statistics can be defined asthe art of analyzing, illustrating, andinterpreting accumulated data so that theuser arrives at new knowledge.601

The data gathered from the more

than 70 actual companies available forthe evaluation was of essentialimportance here. The calculations for theindividual variables or respectiveperformance indicators, and the furtherinformation thereto, can be found insection 4 of the appendix. The completeset of data is available from the author.The close subject matter proximitybetween the data basis and the centralquestions made it easier to meet therequirement of a reflective reference tothe examination-leading hypotheses inthe course of interpreting theexamination results.602 Several factorscan constitute the cause for a possible

“Mis-Match” between accumulated dataand empirical reality in a study likethis.603 On the sampling procedure level,for instance, surveying units or personsquestioned for information on thedecisive examination object couldsimply prove to be unsuitable, or leadone to a restricted or distorted selection.This could occur in the case of a surveywhose results are intended to beapplicable to large industrial companies,but where results were gained followinga survey of companies that belongedexclusively to a particular industry.604

Amongst several other artifact

sources,605 a “Mis-Match” can resultfrom the fact that – despite the partial

basic suitability of a selection –examiners and persons questioned havediffering frames of reference available.This risk is to be classified as especially“delicate”, because it can lead tosubstantially false content conclusionswithout this always immediatelybecoming obvious. This can happen forexample in the form of a weakquestionnaire return quota, or criticismarticulated by the persons questioned, orsimilar peculiarities.606 In this studysuch a difference in frames of referencecould have existed in the fact thatexaminers and persons questioned had avarying comprehension of the relativelyhighly specialized question categories.In the same way, the case could be

possible where, for socially desirablereasons, the persons questionedsubmitted information pertaining toquestion categories not applicable tocompany reality. An example of thiswould be the issue of estimativestatements as to certain processattributes not followed by the companyitself, and therefore not specificallyincluded into the survey.

As has already been pointed outelsewhere,607 after the focus had beenplaced upon those answers obtainedwithin the consolidation of the primaryresearch which contained gapless anddifferentiated statements, the previouslymentioned risks become more

improbable (reversed conclusion ofhigh statement authenticity). Theconclusion of risk improbabilitytherefore became more applicable to thestudy as the statements were made by theexecutives within each businessspecializing in Supply Chain topics.608

In the case of the presence of a “Mis-Match” between the screening questionsand company reality, those respondentswould have been more likely to expresscriticism than if the questions had beenput to less professionally experiencedcontact persons. The case of gapless anddifferentiated statements being given asto the individual Supply Chain attributes(if the question categories had not hadclose proximity to business reality) must

be considered relatively improbable.However, the aforementioned risks maynot be completely ruled out, as totalavoidance of artifacts is not practicallyachievable in any empirical examinationin which the “Human Factor” plays arole (surveys or interview forinformation, etc.)609

All following evaluations took

place discriminately and in accordancewith the groupings represented inparagraph 3.3.5. This was done in orderto capture heterogeneity within the set ofdata and respectively monitor inputmeasures on the evaluation side withregards to possible result-influencingdistortions. Those cases in which this

led to an incremental knowledgeaccumulation are noted with theirrespective results. If no furtherdeclarations are made, additionalrecognition could not be gained from thediscriminate observation. The diagnosticsituation presents itself as a roughcomparison for the companiescharacterized by various discriminatingcharacteristics, i.e., no inferential-statistically relevant interactions werepresent.610 The representation andinterpretation of the results wasperformed by means of the followingrough screening process:

Description of the evaluation

results by using statistical figuresas for example arithmetic mean,standard deviation, etc.Explanation of the bivariatecorrelation by means of statisticalvalues, as for example the Bravais-Pearson’s correlation coefficient(R) and the Type I Error (P(α).The direction of conclusion beingwhether the thesis is confirmed(verification), rejected(falsification) or judged to beunsystematic.611

Exemplary graphic illustration, ifmeaningful. In this case, it wasdecided for inferential-statistical aswell as content reasons to carry outvisualization of the bivariate

correlations from a correlationlevel of R = 0.30 (absolute).612

For better comprehension of thefollowing illustrations it did, however,seem important to first illustrate thepertinent descriptive-statisticalmeasures of the central tendency anddispersion (especially arithmetic meansand standard deviations) in addition tothe respective units of measurement (i.e.,percentages, hourly or daily recording,etc.) of all model parameters involved inthe assumed correlations. The necessaryinformation is made apparent by thefollowing tables 4-2a to 4-2e. Anoverview of the examined sample’s

distribution takes place beforehand inthe tables 4-1a to 4-1e. Tbl. 4-1a: Distribution of the examined companies (N =

73) by region and country613

Country Region Quantity Percentage

Canada North America 2 2.74

USA North America 53 72.6

France Europe 1 1.37

Germany Europe 1 1.37

Hungary Europe 1 1.37

Italy Europe 5 6.85

Turkey Europe 1 1.37

UK Europe 3 4.11

India Asia 2 2.74

Indonesia Asia 3 4.11

Singapore Asia 1 1.37

Tbl. 4-1b: Distribution of the examined companies (N =

73) by industry614

Industry Quantity Percentage

Aerospace and Defense 2 2.74

Agriculture and Biotechnology 3 4.11

Apparel 3 4.11

Automotive 2 2.74

Chemicals and Pharmaceuticals 7 9.59

Computers and ConsumerElectronics 7 9.59

Consumer Packaged Goods 7 9.59

Electric Utilities 4 5.48

Household Appliances 3 4.11

Machinery and Equipment 6 8.22

Metal Products 5 6.84

Office and Printing Machines 4 5.48

Rubber and Plastic Products 5 6.85

Telecommunications 7 9.59

Retail and Distribution 6 8.22

Others 2 2.74

Tbl. 4-1c: Distribution of the examined companies (N =

73) by group size based on revenue according toGerman Commercial Code (HGB)615

Size range Revenue Quantity Percentage

Small-sizecompanies

Less than 6.77m Euros(approx. 8.5m US-Dollars)

3 4.1

Mid-sizecompanies

6.77 to 27.5m Euros(approx. 8.5m to 35mUS-Dollars)

14 19.2

Largecompanies

Greater than 27.5mEuros (approx. 35m US-Dollars)

56 76.7

Tbl. 4-1d: Distribution of the examined companies (N =73) by group size based on FTE number according to

German Commercial Code (HGB)616

Size range Number ofEmployees Quantity Percentage

Small-size Less than 50 2 2.7

companies

Mid-sizecompanies 50 to 250 16 21.9

Large companies Greater than 250 55 75.4

Tbl. 4-1e: Distribution of the examined companies (N =

73) by Return on Assets (ROA)617

Return on Assets (ROA) Quantity Percentage

Negative (< 0 percent) 7 9.6

0 to 10 percent 46 63.0

Greater than 10 percent 20 27.4

Tbl. 4-2a: Description of Source629

No.Parameter(PerformanceMeasure)

ME618 X619 s620 Min621 Max622

1

Purchasingcost as apercentageof revenue

Percent 1.33 0.64 0.23 3.67

Percentage

2of purchasedordersreceived ontime andcomplete

Percent 80.64 18.56 5624 99

3

Percentageof purchasedlinesreceived ontime andcomplete

Percent 81.79 20.32 0625 99

4aTransactionsprocessedvia web

Percent 4.45 13.18 0626 90

4bTransactionsprocessedvia EDI

Percent 3.27 13.19 0627 90

5

Number ofactivesuppliers perFTE

Quantity 81.92 121.95 1628 550

6 Purchasingcost per FTE Dollar 72,692 45,614 66 199,200

Tbl. 4-2b: Description of Produce631

No.Parameter(PerformanceMeasure)

ME x s Min Max V

1

Manufacturingcost aspercentage ofRevenue

Percent 60.11 20.17 8.89 65.38 56.49

2

Averageoperating-equipmentefficiency rate

Percent 84.80 14.25 45.00 99.80 54.80

3Averagemanufacturingcycle time

Percent 201.52 356.70 1630 2,160 2,159

4Average MPSplant deliveryPerformance

Percent 87.16 13.93 15 95

5Average plantcapacityutilization

Percent 70.35 19.85 20 94

6 Manufacturing Dollar 387,266 752,362 29.851 3.81818m

cost per FTE

7Averagethroughputper FTE

Pieces 496,616 909,367 481 5.454555m

Tbl. 4-2c: Description of Deliver – Store634

No.Parameter(PerformanceMeasure)

ME x s Min Max V

1

Inventorymanagementcost aspercentage ofrevenue

Percent 1.95 1.54 0.02632 7.88 7.86

2

Inventorymgmt. cost aspercentage ofinventoryvalue

Percent 37.16 33.04 0.79 80 79.21

3Averageinventoryturnover

Quantity 15.08 19.73 2 109 107

Inactive

4 inventorypercentage

Percent 10.16 17.28 3 67 64

5

Inventoryobsolescencecost aspercentage ofrevenue

Percent 1.07 2.29 0.50 11.64 11.14

6Cycle countaccuracypercent.

Percent 90.34 19.22 1.89 98 96.11

7

Averagereceivedfinishedgoodsturnaroundtime

Numberofhours

19.20 38.34 1 240 239

8Inventorystockoutpercentage

Percent 14.00 20.13 5633 60 55

9

Averagewarehousingspaceutilization

Percent 84.45 16.27 30 96 66

10Inventorymanagementcost per FTE

Dollar 253,331 1.07689m 480 7.75Mio. 7.74952m

11

Inventorymgmt. costper customerorder

Dollar 219,83 320,62 10 1,500 1,490

Tbl. 4-2d: Description of Deliver – Transport636

No.Parameter(PerformanceMeasure)

ME x s Min Max V

1

Transportationcost aspercentage ofrevenue

Percent 3.93 5.20 1 35.23 34.23

2b Percentage ofinbound cost Percent 40.04 25.86 5 80 75

2b Percentage ofoutbound cost Percent 62.34 26.01 10 80 70

3 Damagedshipments Quantity 1.56 2.16 0635 10 10

On-time

4a deliverypercentage(inbound)

Percent 92.04 8.91 60 98 38

4b

On-timedeliverypercentage(outbound)

Percent 94.01 7.29 60 99

5Transporta-tion costper FTE

Dollar 1.04822m 1.61365m 1.8298m 8.1032m

Tbl. 4-2e: Description Deliver – Sell639

No.Parameter(PerformanceMeasure)

ME x s Min Max

1

Customerservice costaspercentageof revenue

Percent 3.21 7.87 0.50 56.10

2Customerretention Percent 85.03 18.17 30 97

rate

3 Customerdisputes Percent 4.33 7.56 1 50

4 Perfectorders rate Percent 85.12 16.28 25 98

5 Lines on-time fill rate Percent 88.95 10.14 60 98

6 Backordersvalue Dollar 11.17801m 28.7459m 10.000 1.38359bn

7

Averageorder- to-shipmentlead time

Hours 281.88 378.21 1 2,000

8Customerservice costper FTE

Dollar 108,577 254,600 221,900 1.81912m

9Percentageof sales viaweb

Percent 22.07 33.32 0637 100

4.1.1 Results of the theses of

the SCOR model groupIntra-PerformanceAttribute (I-P)4.1.1.1 Performance AttributeCustomer Service (reliability andresponsiveness)Thesis 1:The thesis that a high percentage of on-time delivery percentage – inbound oroutbound would lead to a high customerretention rate, was correlation-analytically confirmed for the“inbound”-component (supplier side).An increased customer retention ratewas also coupled with an increasedpercentage of on-time deliveries. Anunsystematic result was consequently

present for the “outbound”-component.The respective Product-Moment-Correlation (PM-Correlation) can becollectively found in Tbl. 4-3.640

The additional consideration of the

parameters company revenue and FTE-Number in a multiple regression(percentage of on-time deliveries as acriterion, manufacturing cycle-time inaddition to the am. parameters as thepredicates) were of no incrementalvalue. This means that the correlation tobe substantially assessed mainlyrepresented itself independently from therevenue and employee-relatedoperationalized company measure.

This statement was valid on thewhole for all other theses, which is whythese aspects are only enlisted in thefollowing in cases where incrementalinformation value is actually present(additional explanatory value) for theparameter s company revenue andnumber of FTE.

Tbl. 4-3: Correlation between on-time deliveries andcustomer retention rate

Correlation N641 R642 P(α)643 M644

on-time delivery percentage– inbound & customerretention rate

73 +0.23 < 0.05 I-P

on-time delivery percentage– outbound & customerretention rate

73 –0.04 non-signif. I-P

Thesis 2:

For the thesis whereby a high perfectorders rate determines a high customerretention rate, only a tendencialconfirmation could be provided. Thecorrelation does not therefore representitself as significant, but is at leastrudimentarily present.

Tbl. 4-4: Correlation between perfect order rate andcustomer retention rate

Correlation N R P(α) M

perfect orders rate & customerretention rate 73 +0.17 non-

signif.I-P

Thesis 3:The position whereby a low on-timedelivery percentage – inbound oroutbound correlates with a low perfectorders rate was confirmed in the case of

the “outbound”-component. In additionto this there was an extremelyhomogeneous relationship between ahigh percentage of on-time deliveriesand a strong proportion in percentage ofperfect customer orders. Therefore, areversible relationship of variables waspresent.645 On the other hand, and withreference to the “inbound”-component,only a tendencial correlation could beassumed.

The respective PM-Correlationsand other evaluation results may betaken from Tbl. 4-5.646 The factualsituation can be differentiated further bymeans of a collective observation.647

Tbl. 4-5: Correlation between on-time deliveries andperfect order rate

Correlation N R P(α) M

on-time delivery percentage –inbound & perfect orders rate 73 +0.16 non-

signif.I-P

on-time delivery percentage –outbound & perfect orders rate 73 +0.48 <

0.001I-P

Thesis 4:The thesis of a “counter-rotating”correlation between the manufacturingcycle-time on the one side and thepercentage of on-time deliveries –inbound or outbound on the other sidecould be corroborated by correlationanalysis.The negative PM-correlationcoefficients mean in this context thatwith a high manufacturing cycle-time, thepercentage of on-time deliveries

decreases. Seen from the other side, itincreases with decreasing manufacturingcycle-time, and this points towards thepresence of a reversible variablerelationship. As a result of this, only thecorrelation for the “outbound”-component was to be deemed to besubstantial.

Diag. 4-1: On-time deliveries (inbound or outbound)and perfect customer orders

On the basis of these results, a“counter-rotating” relationship can beassumed between the amount of on-time“outbound” deliveries and themanufacturing cycle-time. In the case of“inbound” deliveries, a purely marginalcorrelation could be noted.

Tbl. 4-6: Correlation between on-time deliveries andmanufacturing cycle-time

Correlation N R P(α) M

on-time delivery percentage – inbound& average manufacturing cycle time 73 –

0.09non-signif.

I-P

on-time delivery percentage –outbound & average manufacturingcycle time

73 –0.22 < 0.05 I-

P

The reliable delivery of purchase

orders was therefore unequivocallybound to defect-free (perfect) customer

orders on the basis of the empirical data,and as a result of this a positiverelationship of both parameters could becorroborated. Thesis 5:A positive correlation between thepercentage of purchased orders receivedon time and complete and the perfectorders rate was confirmed asstatistically significant. Therefore, adeterministic variable relationship couldbe proven. Tbl. 4-7: Correlation between perfect purchase orders

and perfect order rateCorrelation N R P(α) M

percentage of purchased ordersreceived on time and complete & perfectorders rate

73 +0.27 <0.05

I-P

As a result, the respective

correlation level lay in an area whichalready allowed for the conclusion of ameasurably narrow parameter-coupling.648

Thesis 6:It was possible to find a significantdegree of support for the model’sconcept that a “counter-rotating”coupling exists between average MPSplant delivery performance – workorders and average manufacturingcycle time.

High average MPS plant deliveryperformance – work orders

accompanied shorter averagemanufacturing cycle times. Contrary tothis low average MPS plant deliveryperformance – work ordersaccompanied an increase in averagemanufacturing cycle time to asignificant degree. An opposingrelationship could therefore be provenbetween the two parameters, and thisrelationship can also be seen asdeterministic in nature.

Tbl. 4-8: Connection between MPS plant deliveryperformance (work orders) and manufacturing cycle-

timeCorrelation N R P(α) M

average MPS plant deliveryperformance – work orders averagemanufacturing cycle time

73 −0.26 <0.05

I-P

Thesis 7:The position that a short averagepurchase requisition to delivery cycletime determines a high lines on-time fillrate could not be empiricallycorroborated to a sufficient degree. Thecorrelation of the two parameters can,for the most part, be characterized asunsystematic. Tbl. 4-9: Correlation between delivery cycle-time for

purchase requisitions and perfect customer order linesCorrelation N R P(α) M

average purchase requisition todelivery cycle time & lines on-time fillrate

73 +0.09 non-signif.

I-P

4.1.1.2 Performance attributeFlexibility

Thesis 8:In a similar fashion, no empiricalconfirmation could be found for theassumption that a high inventorystockout percentage leads to a highbackorders value. Both parametersstood in a positive relationship, butconclusively had to be characterized aspurely unsystematic (Tbl. 4-10).

Tbl. 4-10: Correlation between stockout andbackorders

Correlation N R P(α) M

inventory stockout percentage &backorders value 73 +0.06 non-

signif.I-P

4.1.1.3 Performance attribute CostThesis 9:It was expected that a positive

relationship would be found betweenpurchasing cost as a percentage ofrevenue and inventory managementcost as a percentage of revenue . On thebasis of the data gathered, thisassumption received sustainedconfirmation.

Tbl. 4-11: Correlation between revenue-relatedpurchase and inventory management cost

Correlation N R P(α) M

purchasing cost as a percentage ofrevenue & inventory management costas a percentage of revenue

73 +0.34 <0.01

I-P

H i g h purchasing cost as a

percentage of revenue thereforeoccurred significantly with highinventory management cost as a

percentage of revenue . On the otherhand, low purchasing cost wasrespectively present with low inventorymanagement cost.

The unison of both parametersinvolved and the associateddeterministic relationship can also beconvincingly corroborated by collectiveobservation, as can be taken from Diag.4-2.

Diag. 4-2: Revenue-related purchase and inventorymanagement cost

Thesis 10:Only tendencial indications were givenby PM-correlation to support the thesisthat a high inventory management costas a percentage of revenue wouldaccompany a high inventorymanagement cost per FTE. Inaccordance with this, the calculated

relationship cannot be described assignificant. However, a slightly positivecorrelation could be proven.

Tbl. 4-12: Correlation between revenue and FTE-related inventory management cost

Correlation N R P(α) M

inventory management cost as apercentage of revenue & inventorymanagement cost per FTE

73 +0.16 non-signif.

I-P

Thesis 11:The assumption of a positive correlationbetween high transportation cost as apercentage of revenue and hightransportation cost per FTE wascorroborated. Both parameters indicatedan extremely homogeneous andstatistically significant relationship.

Tbl. 4-13: Correlation between revenue and FTE-

related transport costCorrelation N R P(α) M

transportation cost as a percentage ofrevenue & transportation cost per FTE 73 +0.67 <

0.001I-P

The unambiguous deterministic

relationship of both parameters is alsoapparent in the following graphicalillustration (collective observationmode). Thesis 12:A “counter-rotating” relationship wasexpected between the expression oftransportation cost as a percentage ofrevenue and the amount of damagedshipments. As can be seen from the

following table, a negative correlationwas calculated on the basis of theempirical data for both parametersinvolved. The latter was, though, finallymarked as unsystematic when measuredupon the height of the PM-Correlation.

Diag. 4-3: Revenue and FTE-related transport cost

Tbl. 4-14: Correlation between revenue-relatedtransport cost and damaged shipments

Correlation N R P(α) M

transportation cost as a percentage ofrevenue & damaged shipments 73 –

0.06non-signif.

I-P

Thesis 13:The thesis that a high purchasing cost asa percentage of revenue accompanies ahi gh purchasing cost per FTE wasempirically confirmed. Despite theunusually strong correlation, thecriterion of statistical significance wasstill achieved.

Tbl. 4-15: Correlation between revenue and FTE-related purchase cost

Correlation N R P(α) M

purchasing cost as a percentage ofrevenue & purchasing cost per FTE 73 +0.20 <

0.05I-P

A deterministic variable

relationship was therefore proven uponthe basis of the examined data, as can betaken from Tbl. 4-15. Thesis 14:An empirical confirmation could not beproduced for the correlation of a highcustomer service cost per FTE with alow number of customer disputes.Absolutely no factual relationshipbetween customer service cost per FTEand amount of customer disputes existsin the submitted data pool. Tbl. 4-16: Correlation between FTE-related customer

service cost and customer disputesCorrelation N R P(α) M

customer service cost per FTE &customer disputes

73 –0.01

non-signif.

I-P

Thesis 15:As was the case with thesis 14, acompletely unsystematic result arosehere and therefore offered nojustification for acceptance of the modelassumption. In the end, purely a “non-correlation” was given between theparameters transportation cost per FTEand the amount of damaged shipments. Tbl. 4-17: Correlation between FTE-related customer

service cost and damaged shipmentsCorrelation N R P(α) M

transportation cost per FTE &damaged shipments 73 –

0.03non-signif.

I-P

4.1.1.4 Performance attribute “Assets”

Thesis 16:A “counter-rotating” relationship wasexpected between the parametersinactive inventory percentage andaverage inventory turnover. Thetendencial contrary relationship of bothparameters could, in fact, be empiricallyproven. The criteria of statisticalsignificance were, however, missed(albeit narrowly) (Tbl. 4-18).

Tbl. 4-18: Correlation between inactive inventorypercentage and inventory turnover

Correlation N R P(α) M

inactive inventory percentage &average inventory turnover 73 –

0.18non-signif.

I-P

As a basic tendency, it can be

assumed from the results that a high

inactive inventory percentage wouldaccompany a low average inventoryturnover. Thesis 17:No empirical confirmation could befound for the thesis according to which ahigh inactive inventory percentage wascoupled with a high averagewarehousing space utilization. Bothparameters extensively stood in anunsystematic relationship with oneanother.

Tbl. 4-19: Correlation between inactive inventorypercentage and warehousing space utilization

Correlation N R P(α) M

inactive inventory percentage &average warehousing space utilization 73 +0.08 non-

signif.I-P

Thesis 18:The presumption that a high averageinventory turnover accompanies a lowaverage warehousing space utilizationcould not be empirically corroboratedeither. The respective PM-correlationdid show a negative prognosis, but thecorrelation lay within a completelyunsystematic area. Tbl. 4-20: Correlation between inventory turnover and

warehousing space utilizationCorrelation N R P(α) M

average inventory turnover & averagewarehousing space utilization 73 +0.04 non-

signif.I-P

4.1.2 Results of the theses ofthe SCOR model group

intra-competence (I-C)4.1.2.1 Customer-facing indicatorsThesis 19:A “counter-rotating” relationship wasexpected between the on-time deliverypercentage – inbound or outbound andthe backorders value. In this respect, ahigh amount of on-time deliveries shouldaccompany a low value of back orders(assumption of a negative correlation).

Positive correlations as to thisquestion formulation could still be seentendentially as a threshold value, even ifthis was in a direction contrary to that ofthe model. Nevertheless, the coupling ofboth components of on-time delivery(“inbound” and “outbound”) with the

respective value of backorders was notsufficiently unequivocal. It musttherefore be noted that the resultsobtained did not precipitate in a model-conformant manner, although this itselfwas not in a significant way. Tbl. 4-21: Correlation between on-time deliveries and

backorder valueCorrelation N R P(α) M

on-time delivery percentage –inbound & backorders value 73 +0.13 non-

signif.I-C

on-time delivery percentage –outbound & backorders value 73 +0.12 non-

signif.I-C

Thesis 20:It is significant with this thesis that in thecase of the “inbound”-component, thepostulated correlation between a lowinventory stockout percentage and a

high on-time delivery percentage couldnot be substantiated. In the case of the“outbound”-component, the postulatewas more than meaningfully illustrated.

Tbl. 4-22: Correlation between stockout and on-timedeliveries

Correlation N R P(α) M

inventory stockout percentage & on-time delivery percentage – inbound 73 –0.05 non-

signif.I-C

inventory stockout percentage & on-time delivery percentage – outbound 73 +0.46 <

0.001I-C

In this way, it must be noted that the

said “counter-rotating” variablecorrelation exists overtly and primarilyfor the “outbound“-situation, i.e.,outgoing inventory or customer siderespectively. Diag. 4-4 illustrates the

factual situation in a graphical form.

Diag. 4-4: On-time deliveries (inbound or outbound)and inventory stockout649

Thesis 21:The thesis according to which a highpercentage of purchased orders

received on time and complete wouldcontribute to a low backorders valuewas confirmed for the empirical datapool. The respective contrarycorrelation appeared to be moderatelyrevealed, but not statisticallyunambiguous. It could therefore beproven that both parameters stood in a“counter-rotating” relationship with oneanother.650

Tbl. 4-23: Correlation between perfect purchase orders

and backordersCorrelation N R P(α) M

percentage of purchased orders receivedon time and complete & backorders value 73 –

0.21<0.05

I-C

Thesis 22:The data situation with regards to this

thesis can be compared to the one for thenext thesis, the content of which issimilar: A strongly evident percentageof purchased lines received on time andcomplete significantly contributed to alow backorders value.651

Tbl. 4-24: Correlation between perfect purchase order

lines and backordersCorrelation N R P(α) M

percentage of purchased lines receivedon time and complete & backorders value 73 –

0.37<0.01

I-C

It can be taken from the associated

Diag. 4-5 that this relationship may becharacterized as thoroughly unequivocal(grouped view). Thesis 23:

The correlation analysis for the postulateof a “counter-rotating” correlationbetween stockout and MPS plantdelivery performance (work orders)produced a supportive corroboration. In actual fact, both parameters’characteristics showed themselves to beextremely contrary to one another. Thismeans that a high stockout percentageand a reduced average MPS plantdelivery performance accompaniedeach other (and vice versa for lowstockout percentage, i.e., reversiblevariable relationship). The constellationof the results was therefore unequivocal,as is clearly expressed in the associatedDiag. 4-6.

Diag. 4-5: Perfect purchase order lines and backorders

Tbl. 4-25: Correlation between stockout and MPS plant

delivery performance (work orders)Correlation N R P(α) M

inventory stockout percentage &average MPS plant delivery performance(work orders)

73 –0.63

<0.001

I-C

Thesis 24:Although not expressed so strongly as

was the case with the last thesis, thispostulate of a “counter-rotating”relationship between perfect customerorder lines and backorders was alsoconfirmed as statistically significant.High lines on-time fill rates were in thisway empirically proven to contribute toa reduced backorders value. In thiscase, both parameters appeared to reactin a distinctly contrary manner to oneanother.

Diag. 4-6: Stock out and MPS plant deliveryperformance (work orders)

Tbl. 4-26: Correlation between percentage of perfect

customer order lines and value of backordersCorrelation N R P(α) M

lines on-time fill rate average MPS plantdelivery performance (work orders)backorders value

73 –0.19

<0.05

I-C

Thesis 25:The model assumption whereby a lowinventory stockout percentage would

accompany a perfect orders rate wasconvincingly confirmed. A “counter-rotation” of both the parameters wasclearly evident. The contraryconstellation of both parameters is alsohighlighted by the graphical illustrationin Diag. 4-7.

Tbl. 4-27: Correlation between stockout and perfectcustomer orders

Correlation N R P(α) M

inventory stockout percentage & perfectorders rate

73 –0.63

<0.001

I-C

Diag. 4-7: Stock out and perfect customer orders

In accordance with this, low

percentages of stockout were veryclearly coupled with the increasedprobability of perfect customer orders.The reverse appeared to be the case forincreased stockout, i.e., a reversiblevariable relationship was present.

Thesis 26:Thesis 26, whose content was closelyrelated to that of thesis 25, whereby al o w inventory stockout percentagecontributed to a high lines on-time fillrate, was unequivocally corroborated.Both parameters stood in a markedlycontrary relationship to one another.652

Tbl. 4-28: Correlation between stockout and perfect

customer order lineCorrelation N R P(α) M

inventory stockout percentage & lineson-time fill rate 73

–0.49

<0.001

I-C

The non-ambiguity of the contrary

relationship (deterministic variablecorrelation) also becomes apparent in

the respective visualization, as can betaken from the following Diag. 4-8. Diag. 4-8: Stock out and perfect customer order lines

Thesis 27:The thesis by which a low averagemanufacturing cycle time contributes toa decreased backorders value was notconfirmed by the calculated correlation.Moreover, a rather “counter-rotating”

relationship was shown by both theseparameters. The relationship, though,proved itself to be relatively weak. Aconclusion of statistical significancewas therefore respectively eliminated. Thesis 28:No empirical confirmation could bereached for the model assumption of apositive correlation between theinventory stockout percentage and ahigh average manufacturing cycle time.The relationship of both parameters was,in fact, proven to be positive, butnevertheless – measured by thecorrelation level calculated – it alsoappeared totally unsystematic.

Tbl. 4-29: Correlation between manufacturing cycle-

time and backordersCorrelation N R P(α) M

average manufacturing cycle time &backorders value 73 –

0.15non-signif.

I-C

Tbl. 4-30: Correlation between stockout and

manufacturing cycle-timeCorrelation N R P(α) M

inventory stockout percentage &average manufacturing cycle time 73 +0.03 non-

signif.I-C

4.1.2.2 Internal-facing indicatorsThesis 29:The postulated model relationshipbetween inventory management cost asa percentage of revenue and averagereceived finished goods turnaroundtime was given clear confirmation on thebasis of the data pool. Both parameters,

in fact, stood in an expressly positiverelationship.

In this respect, high inventorymanagement costs, expressed as apercentage of revenue, accompanied anincrease in average received finishedgoods turnaround time to a markeddegree. Tbl. 4-31: Correlation between inventory management

cost and received finished goods turnaround timeCorrelation N R P(α) M

inventory management cost as apercentage of revenue & averagereceived finished goods turnaroundtime

73 +0.41 <0.001

I-C

On the strength of this a “synchronous”(and therefore also a deterministic)

variable relationship could be provenbetween the two parameters. The factualsituation is graphically illustrated inDiag. 4-9.

Diag. 4-9: Inventory management cost and receivedfinished goods turnaround time

Thesis 30:The thesis according to which a parallel

would seem to exist between increasedinventory obsolescence cost as apercentage of revenue and a similarlyincreased inactive inventory percentageproved itself to be empirically stable.Both parameters substantially correlatedpositively with one another.

A coupling of the inventoryobsolescence costs with the inactiveinventory percentage was thereforeconsidered as proven. Tbl. 4-32: Correlation between inventory obsolescence

cost and inactive inventory percentageCorrelation N R P(α) M

inventory obsolescence cost as apercentage of revenue & inactiveinventory percentage

73 +0.29 <0.01 I-C

Thesis 31:No enduring confirmation was found forthe model assumption that a highinventory obsolescence cost as apercentage of revenue would return alow average inventory turnover. On theone hand, the expected negativecorrelation between the two parameterswas apparent. On the other hand, thiscorrelation expressed itself in anequality – i.e., a correlation level thatdid not allow the evaluation“substantially narrow” in the lastanalysis. Tbl. 4-33: Correlation between inventory obsolescence

cost and inventory turnoverCorrelation N R P(α) M

inventory obsolescence cost as apercentage of revenue & averageinventory turnover

73 −0.13 non-signif.

I-C

Thesis 32:No supporting evidence was producedbased on the evaluated data material forthe thesis of a “counter-rotating”relationship between inventorymanagement cost per customer orderand a low average warehousing spaceutilization. In this case, one was notdealing with a systematic correlation.Both parameters showed the absence ofsystematic coupling, absent in both apositive and negative direction. Inaccordance with this, the existence of a“non-correlation” was proven. Tbl. 4-34: Correlation between inventory management

cost/customer order and warehousing space utilizationCorrelation N R P(α) M

inventory management cost percustomer order & averagewarehousing space utilization

73 +0.03 non-signif.

I-C

Thesis 33:In the same way, no convincingempirical proof could be found for thethesis wherein a high inventorymanagement cost per FTE would bepresent with a high average receivedfinished goods turnaround time. Thecalculated correlation did show a slightpositive indication. In the last analysisthe correlation level lay, however, in arange that could only be marked asunsystematic.

Tbl. 4-35: Correlation between FTE-related inventorymanagement cost and received finished goods

turnaround timeCorrelation N R P(α) M

inventory management cost per FTE& average received finished goodsturnaround time

73 +0.05 non-signif.

I-C

Thesis 34:The postulate of a negative coupling ofthe inventory management cost per FTEwith the average inventory turnoverwas confirmed in a statisticallysignificant way. Although this contrarycorrelation was not abundantly evident,a significantly increased occurrence-probability of high inventorymanagement cost per FTE simultaneouswith a low average inventory turnoverwas found.

Tbl. 4-36: Correlation between FTE-related inventory

management cost and inventory turnoverCorrelation N R P(α) M

inventory management cost per FTE &average inventory turnover 73 −0.21 <

0.05I-C

Thesis 35:The thesis that a high averagethroughput per FTE would correlatewith a high average plant capacityutilization for finished products couldnot be corroborated sufficiently or withany certainty. Both parameterscorrelated, as expected, in a positiveway. Nevertheless, in the face of thecorrelation level found, a meaningfulcorrelation could not be assumed. Allthe same, at least, a rudimentary positive

relationship could be proven. Tbl. 4-37: Correlation between FTE-related throughput

and plant capacity utilizationCorrelation N R P(α) M

average throughtput per FTE &average plant capacity utilization 73 +0.11 non-

signif.I-C

Thesis 36:No certain statistical significance couldbe produced for the assumption of a“counter-rotating” relationship betweenthe inventory management cost per FTEand the average warehousing spaceutilization. In a similar manner to thecase of thesis 35, the expected (in thiscase negative) direction in therelationship of both parameters wasapparent. However, measured upon the

correlation level, the variablecorrelation could not be viewed assubstantial. Tbl. 4-38: Correlation between FTE-related inventorymanagement cost and warehousing space utilization

Correlation N R P(α) M

inventory management cost per FTE& average warehousing spaceutilization

73 −0.12 non-signif.

I-C

Thesis 37:A far-reaching unsystematic diagnosiswas apparent with regards to thesis 37,whereby a degree of unison between al o w average order-to-shipment leadtime and a low amount of customerdisputes was assumed. The correlationlevel lay within the marginalized area,

as illustrated in Tbl. 4-39. Tbl. 4-39: Correlation between order to shipment lead

time and customer disputesCorrelation N R P(α) M

average order-to-shipment lead time &customer disputes 73 −0.05 non-

signif.I-C

4.1.3 Results of the theses ofthe SCOR model groupInter-Competence/Performance Attribute (I-CP)4.1.3.1 Customer Service (reliability ?and responsiveness) vs. costThesis 38:The model assumption that a high

inventory management cost as apercentage of revenue wouldaccompany a low value in backorderscould not be empirically supported. Thecorrelation identified between the twoparameters could only be marked asunsystematic in the last analysis. Therespective correlation values can betaken from Tbl. 4-40. ?In view of thesefacts, a systematic coupling of theparameters involved was assumed on thebasis of the examined data. Tbl. 4-40: Correlation between inventory management

cost and backordersCorrelation N R P(α) M

inventory management cost as apercentage of revenue & backordersvalue

73 +0.03 non-signif.

I-CP

Thesis 39:It was assumed that a positivecorrelation exists between customerservice cost as a percentage of revenueand the on-time delivery percentage –inbound and outbound. Analysis of thecorrelation could not, however, confirmthis. Negative relationships were foundbetween the cost for customer service onthe one hand, and on-time deliveries onthe other. However, in the face of theempirically identified correlation levels,the contrary constellations were not tobe viewed as substantial. Thesis 40:With regards to the thesis whereby ahi gh customer service cost per FTE

would correlate with a high on-timedelivery percentage – inbound andoutbound, no “strict” pattern ofcorrelation became apparent. Havingsaid that, the correlations on the wholeconsistently fell positively in theexpected direction. The criteria forstatistical significance were met in thecase of the “inbound”-component. Tbl. 4-41: Correlation between customer service cost

as a percentage of revenue and on-time deliveriesCorrelation N R P(α) M

customer service cost as apercentage of revenue & on-timedelivery percentage – inbound

73 −0.03 non-signif.

I-CP

customer service cost as apercentage of revenue & on-timedelivery percentage – outbound

73 −0.08 non-signif.

I-CP

Tbl. 4-42: Correlation between FTE-related customerservice cost and on-time deliveries

Correlation N R P(α) M

customer service cost per FTE & on-time delivery percentage – inbound 73 +0.19 < 0.05 I-

CP

customer service cost per FTE & on-time delivery percentage – outbound 73 +0.03 non-

signif.I-CP

It could be collectively recorded in

this case that high FTE-related costs forcustomer service do positivelyaccompany a higher degree of on-timedeliveries. Thesis 41:No convincing empirical proof wasfound for thesis 41, according to which alow amount of revealed customerdisputes was assumed to accompany ahigh customer retention rate.

The respective correlation

direction was negative and therefore“counter-rotating” as expected, butmeasured by the correlation level thecorrelation could not be described asanything other than unsystematic. Tbl. 4-43: Correlation between customer disputes and

customer retention rateCorrelation N R P(α) M

customer disputes & customerretention rate 73 −0.04 non-

signif.I-CP

Thesis 42:The model assumption of a positiverelationship between cycle countaccuracy percentage and inventorymanagement cost as a percentage of

inventory value was not corroboratedwith empirical certainty in the lastanalysis. On the one hand, a “parallel-running” of both parameters was present(positive correlation). On the other hand,the correlation level did not lie in anarea that would have allowed theassumption of a substantial correlation.

Tbl. 4-44: Correlation between cycle count accuracypercentage and inventory management cost

Correlation N R P(α) M

cycle count accuracy percentage &inventory management cost as apercentage of inventory value

73 +0.06 non-signif.

I-CP

Thesis 43:A non-uniform result was noted as far asthe model assumption is concerned, by

which a high percentage of purchasedorders received on time and completewould correlate with a high purchasingcost as a percentage of revenue.

The correlation prognosis of thetwo parameters proved itself to benegative, whereby however therespective “counter-rotation” was, afterthe last analysis, to be classified asunsystematic in the face of thecorrelation level. Thesis 44:For the thesis of a positive correlationb e tw e e n percentage of purchasedorders received on time and completea nd purchasing cost per FTE, it waspossibly to derive at least rudimentary

confirmation from the empirical dataused for analysis. Tbl. 4-45: Correlation between perfect purchase orders

and purchasing cost as a percentage of revenueCorrelation N R P(α) M

percentage of purchased ordersreceived on time and complete &purchasing cost as a percentage ofrevenue

73 −0.09 non-signif.

I-CP

Both parameters correlated

positively with one another, i.e., a highpercentage of perfect purchase ordersincreasingly accompanied high FTE-related purchasing costs. However, inthe case of the correlation present,statistical significance was not reached. Tbl. 4-46: Correlation between perfect purchase orders

and FTE-related purchasing costCorrelation N R P(α) M

percentage of purchased ordersreceived on time and complete &purchasing cost per FTE

73 +0.14 non-signif.

I-CP

All the same, a fundamentally

positive coupling of both parameterscould be considered proven. Thesis 45:The relationship between themanufacturing cost per FTE andaverage MPS plant deliveryperformance took shape in the directionof the prognosis, i.e., resulted in theexpected positive correlationcoefficient. Despite the significance ofthe diagnosis, a narrow correlation

between the two parameters could not beassumed in the face of the empiricallydiscovered correlation level. On thewhole, though, a degree of “parallel-running” could be assumed by means ofthe evaluation results between FTE-related production costs and averageplant delivery performance.

Tbl. 4-47: Correlation between FTE-relatedmanufacturing cost and MPS plant delivery

performance (work orders)Correlation N R P(α) M

manufacturing cost per FTE & averageMPS plant delivery performance (workorders)

73 +0.20 <0.05

I-CP

Thesis 46:The thesis whereby a strongly markedmeasure of percentage of purchased

orders received on time and completewould preferentially occur inconjunction with a reduced amount ofdamaged shipments was completelyconfirmed in the submitted data pool.Reliably carried-out purchase ordersand damaged shipments therefore stoodin a positive way to one another in thepostulated negative relationship. Tbl. 4-48: Correlation between perfect purchase orders

and damaged shipmentsCorrelation N R P(α) M

percentage of purchased ordersreceived on time and complete &damaged shipments

73 −0.27 <0.01

I-CP

In accordance with this, the

contrary relationship of both variables

was convincingly proven.653

Thesis 47:A contrary relationship between theaverage MPS plant deliveryperformance and the number ofcustomer disputes was convincinglycorroborated on the basis of theempirical data material. Although notdrastic, a significant negative correlationdid exist between both parameters, i.e.,a high average MPS plant deliveryperformance occurred in significantconjunction with a reduced number ofcustomer disputes. The correlation canbe taken from Tbl. 4-49. Thesis 48:

The thesis that a high inventorymanagement cost per customer ordercorrelates with a high perfect ordersrate was confirmed in the empirical datapool. A significant, but not strictlydeterministic relationship existed in theexpected positive direction betweenboth parameters, as highlighted in Tbl.4-50.

Tbl. 4-49: Correlation between MPS plant deliveryperformance (work orders) and customer disputes

Correlation N R P(α) M

average MPS plant deliveryperformance & customer disputes 73 −0.22 <

0.01I-CP

Tbl. 4-50: Correlation between inventory management

cost/customer order and perfect order rateCorrelation N R P(α) M

inventory management cost per < I-

customer order & perfect orders rate 73 +0.19 0.05 CP

Thesis 49:The assumption, according to which apositive relationship was to be expectedbetween inventory management costper customer order and perfectpurchase order lines , might beconsidered empirically secured. Thepostulated positive relationship betweenboth parameters was apparent in asignificant way.

High inventory management costsper customer order thereforeaccompanied equally high perfectpurchase order lines, and a deterministicvariable relationship could beproven.654

Tbl. 4-51: Correlation between inventory managementcost/customer order and perfect purchase order linesCorrelation N R P(α) M

inventory management cost percustomer order & lines on-time fill rate 73 +0.25 <

0.05I-CP

Thesis 50:The expected “parallelism” betweencustomer service cost as a percentageof revenue and a high perfect ordersrate could not be confirmed. Contrary tosuch an assumption, a “counter-rotating”relationship was present between bothparameters. In the face of the identifiedcorrelation level, the correlation could,on the whole, rather be classified asunsystematic. Tbl. 4-52: Correlation between customer service cost

as percentage of revenue and perfect order rateCorrelation N R P(α) M

customer service cost as apercentage of revenue & perfectorders rate

73 −0.07 non-signif.

I-CP

Thesis 51:It was assumed that a positivecorrelation between the customerservice cost per FTE and a lines on-time fill rate would be identified. Inactual fact such a positive correlationwas present between both parameters. Tbl. 4-53: Correlation between FTE-related customer

service cost and perfect purchase order linesCorrelation N R P(α) M

customer service cost per FTE &lines on-time fill rate 73 +0.13 non-

signif.I-CP

The correlation, however, laywithin an area which did not showconclusively that high FTE-relatedcustomer service costs contribute to ahigh lines on-time fill-rate in the senseof a strict and unambiguous accordanceto rule. Therefore, based upon thefundament of the data observed, only atendencial “synchronous” relationshipbetween the variables concerned couldso far be presumed. Consequently, atleast a rudimentary “synchronous”correlation might be assumed upon thebasis of the examination results. Thesis 52:The thesis of a “counter-rotating”correlation between the perfect orders

rate and the number of customerdisputes was not corroborated by meansof the empirical data. The respectivecorrelation level moved within astatistically almost completelyunsystematic range. In actual facttherefore, no substantial or eventendencial correlation existed betweenthe two parameters.

Tbl. 4-54: Correlation between perfect customerorders and customer disputes

Correlation N R P(α) M

perfect orders rate & customerdisputes 73 +0.04 non-

signif.I-CP

Thesis 53:A “synchronism” was expected betweena low average purchase requisition to

delivery cycle time and a high inventorymanagement cost as a percentage ofrevenue. However, the correlation-analytical investigation brought forwardno adequate proof for the suitability ofsuch an assumption. No factual andrecognizable correlation existedbetween the two parameters. On thecontrary, the calculated correlationshowed itself to be unsystematic. Tbl. 4-55: Correlation between purchase requisition todelivery cycle time and inventory management cost as

a percentage of revenueCorrelation N R P(α) M

average purchase requisition todelivery cycle time & inventorymanagement cost as a percentage ofrevenue

73 +0.03non-signif.

I-CP

Thesis 54:No unequivocal confirmation was foundupon an empirical level as regards thethesis that a high average purchaserequisition to delivery cycle time wouldcorrelate with low order-relatedpurchase costs. On the one hand, thecorrelation direction was, as expected,negative and therefore indicated a“counter-rotating” relationship betweenboth parameters. On the other hand,measured by the correlation level, itwould not have been suitable toconclude that a substantial correlationexisted. Tbl. 4-56: Correlation between purchase requisition to

delivery time and order-related purchase cost

Correlation N R P(α) Maverage purchase requisition todelivery cycle time & purchasing costper purchase order

73 −0.08 non-signif.

I-CP

Thesis 55:The thesis gave unambiguous support tothe presence of a positive relationshipbetween purchasing cost per FTE andt h e average purchase requisition todelivery cycle time.

Both parameters correlatedsignificantly positively with one anotherand produced a respective pattern ofcorrelation. As far as this is concerned,increased FTE-related purchase costsactually did appear in conjunction withan increasing purchase requisition to

delivery cycle time. Tbl. 4-57: Correlation between FTE-related purchasecost and purchase requisition to delivery cycle time

Correlation N R P(α) M

purchasing cost per FTE & averagepurchase requisition to delivery cycletime

73 +0.25 <0.05

I-CP

Thesis 56:With respect to the thesis that a“counter-rotating” correlation existedbetween manufacturing cost per FTEand the average manufacturing cycletime, a picture of a threshold value wasfound within the data material. Asexpected, both parameters correlatednegatively with one another, i.e.,increased manufacturing cost per FTE

accompanied a reduced averagemanufacturing cycle time (Tbl. 4-58).

Nonetheless, the identifiedcorrelation coefficient missed – albeitnarrowly – the criteria of statisticalsignificance, so that a substantialcorrelation could not be presumed in thefinal analysis. The accumulated evaluation results did,however, permit the conclusion that, onthe whole, a contrary relationship existsbetween the two parameters.

Tbl. 4-58: Correlation between FTE-relatedmanufacturing cost and manufacturing cycle time

Correlation N R P(α) M

manufacturing cost per FTE & 73 +0.16 non- I-

average manufacturing cycle time signif. CP Thesis 57:A threshold value also resulted in thecase of thesis 57: A homogeneousrelationship was expected betweenaverage purchase requisition todelivery cycle time and an equallyreduced number of customer disputes.The empirical data indicated an exactlyopposing coupling, as both parameterscorrelated negatively with each other, ascan be taken from Tbl. 4-59. Tbl. 4-59: Correlation between purchase requisition to

delivery cycle time and customer disputesCorrelation N R P(α) M

average purchase requisition todelivery cycle time & customerdisputes

73 −0.17non-signif.

I-CP

Despite this fact, a substantial

effect could not be assumed in the faceof the explicit correlation level. For allthat, this thesis could not be supported(for an attempted explanation, see alsothe comments and explanationsfollowing the presentation of thetheses).655

4.1.3.2 Flexibility vs. costThesis 58:With thesis 58, a “counter-rotating”relationship between inventorymanagement cost as a percentage ofinventory value and the backordersvalue was assumed. The empiricalexamination of the facts produced an at

least rudimentary indication of thesuitability of this thesis. Tbl. 4-60: Correlation between inventory managementcost as a percentage of inventory and backorder valueCorrelation N R P(α) M

inventory management cost as apercentage of inventory value &backorders value

73 −0.13 non-signif.

I-CP

Both parameters correlated

negatively with one another as expected,i.e., strongly marked inventorymanagement costs as a percentage ofinventory actually accompanied a ratherreduced amount of backorders. A secureproof of the tendencial model-conformant constellation was, however,not found.

Thesis 59:The thesis by which an increasedmanufacturing cost as a percentage ofrevenue would accompany a lowbackorders value was enduringlyconfirmed. The identified correlation-analytical diagnosis underlined the“counter-rotation” of both parameters ina significant way, as can be taken fromTbl. 4-61. Tbl. 4-61: Correlation between manufacturing cost as a

percentage of revenue and backorder valueCorrelation N R P(α) M

manufacturing cost as a percentage ofrevenue & backorders value 73 −0.30 <

0.01I-CP

The following Diag. 4-10

emphasizes the opposing coursedirections ?of manufacturing cost as apercentage of revenue and the backordervalue. Thesis 60:The empirical proof necessary in orderto support thesis 60, ?in which a lowcustomer service cost per FTEincreasingly appears in conjunction witha high backorders value, could not beproduced with sufficient security. It was,however, apparent that both parameterscorrelated negatively with one another,as expected. But in the last examination,the relationship lay in a far-reaching andstatistically unsystematic area.

Diag. 4-10: Manufacturing cost as a percentage of

revenue and backorder value

Tbl. 4-62: Correlation between FTE-related customer

service cost and backorder valueCorrelation N R P(α) M

customer service cost per FTE &backorders value 73 −0.06 non-

signif.I-CP

Thesis 61:

The thesis by which a low backordersvalue would accompany a low numbero f customer disputes could not beadopted. A negative correlationconsequently existed between bothparameters. However, the level ofcorrelation lay within an area of valuethat would only permit the conclusionthat an extensively unsystematicrelationship exists between bothparameters.

Tbl. 4-63: Correlation between backorder value andcustomer disputes

Correlation N R P(α) M

backorders value & customerdisputes 73 −0.05 non-

signif.I-CP

Thesis 62:

The model assumption suggested apositive relationship between inventorystockout percentage and inventoryobsolescence cost as a percentage ofrevenue. A positive correlation of bothparameters was actually calculated.

Tbl. 4-64: Correlation between stockout andpercentage of inventory obsolescence cost as a

percentage of revenueCorrelation N R P(α) M

inventory stockout percentage &inventory obsolescence cost as apercentage of revenue

73 +0.13 non-signif.

I-CP

It could therefore be confirmed that

a high stockout percentage accompaniedhigh inventory obsolescence cost.Nevertheless, the correlation did not lieat a level that could be described as

substantial. Thesis 63:It was expected that a low inventorystockout percentage would appear inconjunction with a high manufacturingcost as a percentage of revenue . Thenegative correlation assumed thus farwas actually to be found upon the basisof the empirical data material.

Tbl. 4-65: Correlation between stockout andmanufacturing cost as a percentage of revenue

Correlation N R P(α) M

inventory stockout percentage &manufacturing cost as a percentageof revenue

73 −0.14 non-signif.

I-CP

In the case of this inferential-

statistical investigation, however, nolevel of correlation extent was presentwhich would have justified therespective conclusion of clearness or“stringency” of the (“counter- rotating”)correlation between both parameters. Thesis 64:The model expectation for thesis 64 alsocomprised an opposing relationshipbetween the two parameters. It wasassumed that a high manufacturing costper FTE would correlate with a lowinventory stockout percentage. Themodel expectation could becorroborated using the data pool. Therelationship between the two parametersdid not manifest itself as expressly

narrow, but it was statisticallysignificant, as can be taken from Tbl. 4-66.

Tbl. 4-66: Correlation between FTE-relatedmanufacturing cost and stockout

Correlation N R P(α) M

manufacturing cost per FTE &inventory stockout percentage 73 −0.19 <

0.05I-CP

Thesis 65The thesis, whereby a high customerservice cost per FTE should oftenappear in conjunction with a lowinventory stockout percentage could notbe confirmed with sufficient certainty.This result can, though, be seentendentially as a threshold value. On theone hand, the expected negative

correlation was apparent, i.e., “counter-rotation” of both parameters waspresent. On the other hand, however, thecorrelation level had not reached astatistically significant mark, as can betaken from Tbl. 4-67.

As a result of this, the classificationas “unequivocal” within the basictendency of a model-conformantcorrelation had to be disregarded. Tbl. 4-67: Correlation between FTE-related customer

service cost and stockoutCorrelation N R P(α) M

customer service cost per FTE &inventory stockout percentage 73 −0.12 non-

signif.I-CP

4.1.3.3 Customer Service (reliability ?

and responsiveness) vs. assetsThesis 66:No empirical support could be found forthe thesis that a high on-time deliverypercentage – inbound and outboundwould correlate with a low inactiveinventory percentage, as reflected in thefollowing Tbl. 4-68. Tbl. 4-68: Correlation between on-time deliveries and

inactive inventory percentagesCorrelation N R P(α) M

on-time delivery percentage –inbound & inactive inventorypercentage

73 +0.02 non-signif.

I-CP

on-time delivery percentage –inbound & inactive inventorypercentage

73 +0.07 non-signif.

I-CP

Therefore, the analysis of the data

material produced the result that thepercentage of on-time deliveries in thecase of both components (inbound andoutbound) did not stand in the assumednegative relationship with inactiveinventory percentage. It was rather alargely unspecific correlation, andshowed therefore an unsystematicpattern of correlation. Thesis 67:An increased appearance-probability ofa high average inventory turnover inconjunction with a reduced backordersvalue was expected. The identifiedcorrelation was in accordance with thisassumption and turned out to benegative.

However, in the face of the

resulting correlation level only atendencial, as opposed to a stronglymarked, correlation could be concluded. Tbl. 4-69: Correlation between inventory turnover and

backorder valueCorrelation N R P(α) M

average inventory turnover &backorders value 73 −0.15 non-

signif.I-CP

Thesis 68:The thesis of a “counter-rotating”correlation between average order-to-shipment lead time and a high on-timedelivery percentage – inbound andoutbound was convincingly confirmedbased on the empirical material.

In the case of both components –

“inbound” and “outbound” – a highdegree of on-time executionaccompanied a reduced average order-to-shipment lead time, i.e., adeterministic variable relationship wasproven. Tbl. 4-70: Correlation between order to shipment lead

time and on-time deliveries (inbound or outbound)Correlation N R P(α) M

average order-to-shipment lead time &on-time delivery percentage – inbound 73 −0.22 <

0.05I-CP

average order-to-shipment lead time &on-time delivery percentage –outbound

73 −0.43<0.001

I-CP

Diag. 4-11: On-time deliveries (inbound or outbound)

and order to shipment lead time 656

In turn – for both named

components – the contrary relationshipalso proved itself to be statisticallysignificant. The “counter-rotation” forthe “outbound” component, i.e., on thecustomer side, became especiallyapparent. The Diag. 4-11 serves thepurpose of further visualization.

Thesis 69:The thesis whereby a “counter-rotating”relationship existed between cycle countaccuracy percentage and inactiveinventory percentage was, on thewhole, confirmed. The correlation uponthe basis of the empirical data wasnegative. With this, the datarelationships presented themselves in amodel-conformant manner. Thecorrelation was not proven to bestrongly expressed or “stringent”.However, the criteria of inferential-statistical significance were met, as Tbl.4-71 indicates.

Tbl. 4-71: Correlation between cycle count accuracypercentage and inactive inventory percentage

Correlation N R P(α) Mcycle count accuracy percentage &inactive inventory percentage 73 −0.20 <

0.05I-CP

Thesis 70:The model assumption according towhich a high percentage of purchasedlines received on time and completewould appear in conjunction with a lowaverage order-to-shipment lead timecould not be corroborated by means ofthe empirical data.

No unambiguous “counter-rotation”of the two parameters could beidentified. They stood moreover in awidely unsystematic relationship withone another (non-correlation).

Tbl. 4-72: Correlation between perfect purchase orderlines and order to shipment lead time

Correlation N R P(α) M

percentage of purchased linesreceived on time and complete &average order-to-shipment lead time

73 −0.02 non-signif.

I-CP

Thesis 71:The thesis by which an increasedoccurence probability of a high inactiveinventory percentage would be presenttogether with a high lines on-time fillrate could not be confirmed. Accordingto the data situation the relationshipbetween the two parameters was evenshown as negative, as illustrated in thefollowing Tbl. 4-73.

Tbl. 4-73: Correlation between inactive inventorypercentage and perfect customer order lines

Correlation N R P(α) Minactive inventory percentage & lineson-time fill rate 73 −0.09 non-

signif.I-CP

However, in the face of the

identified correlation level only theassumption of an unsystematiccorrelation could be adopted. Thesis 72:A stringently positive relationship wasexpected between the average order-to-shipment lead time and perfectcustomer order lines. The calculatedcorrelation of the two parameters did notconfirm this assumption. A recognizablenegative correlation was present, butthis did not meet the criteria of statisticalsignificance, as reflected in Tbl. 4-74.

Tbl. 4-74: Correlation between order to shipment lead

time and perfect customer order linesCorrelation N R P(α) M

average order-to-shipment lead time& lines on-time fill rate 73 −0.14 non-

signif.I-CP

Nevertheless, the conclusion may

be drawn from this that within theempirical data pool, a high averageorder to shipment lead time does notaccompany a similarly high amount ofperfect customer order lines, buttendentially rather accompanies areduced amount. In accordance with this,the constellation has a tendentiallymodel-contrary character, as theevaluation results above showed.

Thesis 73:In the case of thesis 73, a stringentlynegative relationship betweentransactions processed via web/EDIand the average received finishedgoods turnaround time was assumed.The data observed, however, showedthat a “reversed” – and thereforepositive – correlation existed betweenthe two parameters, via the web as wellas EDI. Both correlations appeared asrecognizably positive, although notexpressly “stringent”, as can be takenfrom the following Tbl. 4-75. Tbl. 4-75: Correlation between purchasing transactions

processed via web/EDI and received finished goodsturnaround time

Correlation N R P(α) M

transactions processed via web &average received finished goodsturnaround time

73 −0.13 non-signif.

I-CP

transactions processed via EDI &average received finished goodsturnaround time

73 −0.20 < 0.05 I-CP

During the correlation of the

transactions processed via web/EDIand the received finished goodsturnaround time, a statisticallysignificant result was actually identified.Possible explanations of this so farbasically model-contrary diagnosis willbe commented upon in detailelsewhere.657

Thesis 74:With regards to thesis 74, according towhich a high percentage of sales via

web would correlate with a low averageorder-to-shipment lead time, thetendencial negative relationship of thetwo parameters was present in theempirical data pool, as expected.However, the correlation level wassituated in a value range that wasregarded as totally unsystematic, asemphasized in Tbl. 4-76. The conclusionof a confirmation of the modelassumption in a substantial way wouldtherefore not have been justifiable.

Tbl. 4-76: Correlation between sales transactionsprocessed via web and order to shipment lead time

Correlation N R P(α) M

percentage of sales via web &average order-to-shipment leadtime

73 0.00658 non-signif.

I-CP

4.1.3.4 Flexibility vs. assetsThesis 75:In the case of thesis 75, conformity of thebivariate parameter relationship wasexpected. This is to say that a lowaverage received finished goodsturnaround time was assumed tocorrelate with a low backorder value(vice versa for high values of bothparameters). Tbl. 4-77: Correlation between received finished goods

turnaround time and backorder valueCorrelation N R P(α) M

average received finished goodsturnaround time & backorders value 73 −0.02 non-

signif.I-CP

The thesis could not be

corroborated by means of the observeddata, as can be seen in the above Tbl. 4-77. The received finished goodsturnaround time and the backorder valuestood, moreover, in an almost totallyunsystematic statistical relationship toone another (as such, a “non-correlation” existed). Thesis 76:No convincing empirical confirmationcould be produced for the thesis that ahigh average inventory turnover wouldestablish itself parallel to a reducedbackorder value. A negative correlationexisted between the two parameters. Therespective correlation level, however,reached only a poorly expressed (and

therefore unsystematic) value range, ascan be taken from Tbl. 4-78. Tbl. 4-78: Correlation between inventory turnover and

backorder valueCorrelation N R P(α) M

average inventory turnover &backorders value 73 −0.08 non-

signif.I-CP

Thesis 77:

The model assumption that a lowinventory stockout percentage wouldaccompany a low average receivedfinished goods turnaround time (andvice versa for a high value of the twoparameters) could be unequivocallyconfirmed by the data. In accordancewith this, a reversible deterministic

variable relationship could be proven. Tbl. 4-79: Correlation between stockout and received

finished goods turnaround timeCorrelation N R P(α) M

inventory stockout percentage &average received finished goodsturnaround time

73 +0.27 <0.05

I-CP

The characteristics of the

percentage of stockout and the receivedfinished goods turnaround time indicatedthe model-conformant “synchronism”, asillustrated in Tbl. 4-79. In addition tothis the respective correlation wasstatistically significant. Thesis 78:A parallel-running relationship wasexpected between the inventory

stockout percentage and the averageorder-to-shipment lead time. Accordingto this, a low percentage of inventorystockout should appear in conjunctionwith a reduced average order-to-shipment lead time (“synchronism” alsoin the reverse case, i.e., high values ofboth parameters).

A model-conformant correlationwas in fact present within the data pool.Beyond this, the applicability of themodel assumption was corroborated asstatistically significant, and a reversibledeterministic variable correlation couldbe proven.

Tbl. 4-80: Correlation between stockout and order toshipment lead time

Correlation N R P(α) Minventory stockout percentage &average order-to-shipment lead time 73 +0.24 <

0.05I-CP

Thesis 79:In accordance with the model idea, a“counter-rotating” relationship wasexpected between the inventorystockout percentage and the averageinventory turnover. This relationshipallowed itself to be corroborated asmodel-conformant based on thesubmitted data material. The respectivecorrelation coefficient also proved to bestatistically significant, as can be seenfrom Tbl. 4-81. Tbl. 4-81: Correlation between stockout and inventory

turnoverCorrelation N R P(α) M

inventory stockout percentage &average inventory turnover 73 −0.21 <

0.05I-CP

Thesis 80:A “counter-rotating” relationshipbetween the inventory stockoutpercentage and the average operating-equipment efficiency rate (OEE) forfinished products was assumed. Theidentified correlation did in fact showthat such a contrary relationship existedbetween the two parameters: Weaklymarked stockout percentages thereforeincreasingly appeared in conjunctionwith an increased degree in operatingequipment efficiency rate. Tbl. 4-82: Correlation between stockout and operating

equipment efficiency rate (OEE)

Correlation N R P(α) Minventory stockout percentage &average operating-equipmentefficiency rate (OEE)

73 −0.10 non-signif.

I-CP

Nonetheless, the result could only

be considered a threshold value, as theempirical parameter correlation was notpresent to an extent that could be judgedas “stringent” or meaningful. The criteriaof statistical significance wererespectively not met, as illustrated in theabove Tbl. 4-82. 4.2 Rating of theExamination Results of theSingle HypothesesIn the present section the followingquestions are to be answered upon the

basis of the described examinationresults:

Which theses could be confirmed,and how are these results to beclassified?659

Which theses were not confirmedor had to be rejected? What are thepossible reasons for this?Which theses should be changedand in what way, so that they can beeither confirmed or validated infuture empirical examinations?

4.2.1 Consequences fromthe respective differences

and conformities betweenthe theses and actual resultsOf the 80 single theses evaluated, about60 percent were confirmed assignificant, or at least indicative of theprognosis of correlation direction.Roughly 35 percent proved themselvesto be unsystematic, 5 percent werefactually model-contrary and thereforehad to be unconditionally rejected(falsified). The details of the confirmedtheses may be taken from the aforesaidevaluation results. The thesesconformant with the SCOR modelcorroborate the central assumptionestablished at the beginning of Chapter 3and the system of hypotheses foundthere.

In the following, the second and

third categories will be dealt with indetail. During this, the four model-contrary theses will be individuallydiscussed and explanations for theirstatus will be sought. The unsystematictheses will be discussed in expedientblocks of topics. 4.2.2 Approaches toclarification ?of theunsystematic thesesThe statistically totally unsubstantial andtherefore unsystematically classifiedtheses do not, in the last instance,disprove the respective adopted central

assumption or the fundamental system ofhypotheses. It was moreover notpossible to confirm or reject them in astatistically unambiguous way. In thefollowing, possible explanations are tobe found for the unproven correlations(“non-correlations”) of each involvedparameter for those theses.

Factors will be discussed herewhich could have contributed to createan unsystematic picture. In this context,the limitations (boundaries) of the SCORmodel highlighted in Chapter 2 are alsoconsidered.

In accordance with this, the modeldoes not lay claim to explicitly includeevery company process or every

activity.660 Unsystematic results cantherefore quite possibly manifestthemselves for the performancemeasures respective to that type ofcompany process. The cases in whichthis correlation is possible withunsystematic results will be specificallyindicated. Based on the results, theunsystematic theses allow themselves tobe allocated to the following fourcategories: Customer order management,inventory management, transport, andpurchasing. 4.2.2.1 Customer order managementFifteen theses fell beneath this heading,namely:

Within the SCOR model groupIntra-Performance Attribute (I-P):Nos. 7, 8, 14661

Within the SCOR model groupIntra-Competence (I-C): Nos. 27,37662

Within SCOR model group Inter-Competence/Performance Attribute(I-CP): Nos. 8, 39, 41, 50, 52, 60,61, 70, 74, 75.663

In the category customer ordermanagement, the theses could be furtherallocated to two sub-categories:

Time: On-time deliveries, lines on-time fill rate, order to shipment

lead time, backordersQuality: Perfect customer orders,customer disputes.

Both the further competitive factors

in Supply Chain Management’s so-c a l l e d strategic square, cost andflexibility, are implicitly containedwithin those factors which are allocatedto the formerly-named parameters timeand quality.664 The followingcorrelations could not be individuallyproven:

On-time deliveries, lines on-timefill rate, backorders and:

Order to shipment lead time: Itcould be possible that theallocation of both variables todiffering SCOR processes (Deliveron the one side and Source on theother side) represents the reasonfor the fact that only anunsystematic correlation could beproven between both the variables.The correlation would otherwisehave to extend over the wholeSupply Chain, so to speak.665 It ispossible that in order to prove thecorrelation and receive asufficiently strong indication, alarger sample size would benecessary.

Stock out: A possible reason couldbe that the examined companies hadbuilt-up a sufficient inventory orapplied powerful production andinventory stock planning systems inorder to make themselvesindependent from deliverybottlenecks – at least partially. Inconjunction with the companies towhich this does not apply thiscould, in actual fact, lead to anunsystematic picture on the whole,and thus contribute to a correlationthat cannot be proven.666

Customer service costs: It ispossible that delivery reliability is

so strongly influenced by furtherparameters that higher assets alonecannot possess a significantinfluence. An unsystematic totalpicture could result from this. Thistrain of thought is based upon theassumption that delivery reliabilityalone is not sufficient in order tosecure the customer servicenecessary to uphold a competitiveposition.667Another potentialexplanation could lie in the presentefforts to be in a position to ensurea better customer service by meansof suitable Supply Chain concepts,with simultaneously low costs.668

An opposing, yet also interestingquestion would be whether

companies would be better offinvesting in other areas and shouldsave on customer service costs.669

This factual situation will be dealtwith more closely in Chapter 5.Inventory management costs: Thefailure of the study to prove thiscorrelation could be attributed tothe fact that some of the companiesexamined already work with thepreviously described concept ofVendor Managed Inventory (VMI) .Larger amounts of unfinished ordelivered material would then onlyhave a restricted influence upon theinventory costs. To be more exact,the costs would be shifted onto thesupplier and “externalized”, as it

were.670 Similar conditions shouldbe valid for the correlation withturnaround times: Longerturnaround times do not adequatelyequate to higher inventorymanagement costs, becauserespective inventory strategies areapplied to counteract this.671

Average received finished goodsturnaround time: The company-internal turnaround time has asubstantial influence upon the finaldelivery of the finished product tothe customer, and with this the on-time contract fulfillment. Undernormal circumstances, theperformance indicators for theSupply Chain contain this internal

component as a constituent of thedelivery performance.672 It cantherefore be presumed that the datamaterial in conjunction with thiswas not consistent.

Customer disputes: This correlationfalls under the aspect of customerservice after delivery which theSCOR model takes, amongst otherthings, as a given entity. A possibleway to operationalize thiscorrelation could be aquestionnaire to investigate thecustomer’s opinion of the deliveryperformance.673 It must be assumed

that further parameters have asubstantial influence upon thefrequency of customer disputes.On-time purchase orders: It can bepresumed that the procurementstrategy and the associatedobjective of on-time purchaseorders have an influence upon thedelivery performance.674 However,purchase orders alone probably donot have a sufficiently largeinfluence upon the total orderprocessing duration. One reasoncould lie in the fact that thecompanies have built up enoughinventory stock in order to balanceout delivery fluctuations. This inturn would indicate potential

savings. Another approach atexplanation could lie in respectiveprocurement strategies forcooperation with suppliers –Strategic Sourcing, for example.675

Sales via the internet: The internethas apparently not been able tocontribute to the acceleration oforder processing cycle time.According to this, internet usage insales seems to have made a saleschannel available which cancontribute to increasing marketshares and revenues, but does notnecessarily have to lead to asubstantial shortening of processlead time.676

Perfect customer orders and:

Cost of customer service: Thoseissues mentioned above for the on-time deliveries also apply here.Perfect customer orders arepossibly so strongly influenced byother parameters that high capitalexpenditure alone has noidentifiable influence. The questionalso arises here as to whethercompanies would be better advisedto invest in other areas and save oncustomer service costs. This willbe more closely dealt with inChapter 5.Customer disputes: If perfect

customer orders have noidentifiable positive influence uponthe number of customer disputes, itwould mean in reverse thatimperfect customer orders do nothave to lead to ?an increasednumber of disputes. This wouldsuggest the conclusion that some ofthe examined companies applylarge sums in order to “mend” or“touch-up” their systems, as itwere, in order to avoid disputes.677

Similarly, no identifiablecorrelation could be provenbetween the amount of customerdisputes and customer service

costs. Also, no significantcorrelation between customerdisputes and customer retentioncould be proven. This correlationhowever, falls under the categoryof customer service after delivery,which the SCOR model – asexplained above – takes as a givenentity. On the one hand,improvement possibilities withregards to the SCOR model resultfrom this. On the other hand,possibilities for processoptimization arise for therespective companies.678 Bothcases will be addressed in thecourse of Chapter 5.

4.2.2.2 Inventory managementEight theses fell within this category,namely:

Within the SCOR model groupIntra-Performance Attribute (I-P):Nos. 17, 18679

Within the SCOR model groupIntra-Competence (I-C): Nos. 28,32, 33680

Within SCOR model group Inter-Competence/Performance Attribute(I-CP): Nos. 42, 53, 66.681

Sub-categories could be constructedwithin the category of inventory

management:

Effectiveness: Inventory turnoverfrequency, material availabilityEfficiency: Warehousing spaceutilization, inventory turnoverperiods, inventory managementcosts.

The following correlations could not beproven:

Warehousing space utilization and:

Inventory turnover frequency: It canbe assumed that the same applies

here as in the above mentioned casewith regards to the relationbetween the ability to deliver andinventory management costs.Inventory turnover frequency hasprobably no identifiable influenceupon warehousing space efficiency,because some of the examinedcompanies work with the conceptof Vendor Managed Inventory(VMI). In this case too, the costswould be shifted onto the suppliersand “externalized”, so to speak.682

This would also apply for theinventory management costs:Inventory management costs would,in this case, be displaced and“uncoupled”, as it were, from the

warehousing space utility. Thatwould also explain why noidentifiable correlation betweeninventory management costs andcycle count accuracy percentagecould be proven: The responsibilityfor the cycle count accuracy wouldlay – at least partially – with thesupplier.683

Inactive stored material: Apossible reason for the fact that nosignificant correlation could beidentified may be the fact that someof the examined companiesspecialize in products with arelatively short life cycle. The life

cycle of stored material has a directinfluence upon inventory aging: Theshorter the life cycle is, the morefrequently old or inactive materialmust be replaced by new.684 Inaddition to the life cycle, however,the service level strived for is alsorelevant: The higher this is, thehigher the risk of building upinactive material.685

Inventory management costs: Thenon-proven correlation couldrepresent a further indication of thefact that the examined companiesalready enlist to a greater extent theuse of the concept of VendorManaged Inventory (VMI) orrelated concepts.

Stock out and manufacturing cycle time:

It can be presumed that amongst theexamined companies a partialuncoupling of the stocking levels and theproduction process has already takenplace.686 As a result of this, thedependency or respective correlationbetween the factors would be minimal,and the correlation therefore notidentifiable. 4.2.2.3 TransportTwo theses were allocated to thiscategory and fall into the SCOR modelgroup Intra-Performance Attribute (I-P):Nos. 12 and 15.687

Both theses could be allocated to

costs. Concretely, no correlation wasfound between transport costs (aspercentage of revenue and per FTE) andthe extent of damaged shipments. Thismay be because a number of theexamined companies are alreadycooperating with external logisticsservices (Third-Party Logistics ServiceProvider, 3PL) . This would beconsistent with present tendencies,whereby the cooperation with a 3PLplays a great role for an increasingnumber of companies.688

4.2.2.4 PurchasingTwo theses fell within this category in

the SCOR model group Inter-Competence/Performance Attribute (I-CP): Nos. 43 and 54.689

In this case, both theses also allow

themselves to be allocated to costs. Inaccordance with this, no significantcorrelation could be proven betweenpurchasing costs and:

On-time purchase orders: Highercosts in the purchasing area did notseem to contribute to guaranteeing abetter processing of purchaseorders. This could be because amajor problem within theprocurement process normally liesin the expense involved in

information retrieval, complexprocess chains, and the multitude ofmanual tasks.690

Purchase requisition to deliverycycle time: As in the previous case,the same would be applicable here:The reason for the inability toprove the correlation may lie inhigh procurement processcomplexity.691

4.2.3 Clarificationpossibilities of the model-contrary thesesIn the case of those hypotheses thatproved to be model-contrary in a

significant or at least tendencial way, afactual more exact “reverse” correlationwas found than was presumed during theinitial thesis development. In thisrespect, the original theses had to berepresented in the shape of new“counter-theses”, in order to draw themodel closer to the empirical reality(model-conformant correlation).

The derivation and suitability (orrather the “non-defensibleness” of thecontent) of such counter-theses will bedealt with more closely here inconjunction with the possible reasonsfor the model-contrary resultconstellation. Recommendations forimprovement of the SCOR model result

from the extrication of potential reasons,which will be dealt with in detail inChapter 5.692

The model-contrary cases

undoubtedly represent the mostproblematic group, as they caninvalidate the adopted centralassumption. However, the percentagethey represent of the total amount oftheses investigated is less influentialthan it would need to be to cause seriousproblems. In this study they account forsomewhere in the proximity of fivepercent, which is way short of theamount that would be necessary in orderto consider the basic hypotheses systemas fundamentally unsuitable.693 One

thesis fell within the SCOR model groupIntra-Competence (I-C): No. 19. Theother three theses had to be allocated toSCOR model group Inter-Competence/Performance Attribute (I-CP): Nos. 58, 72 and 73. 4.2.3.1 Model-contrary thesis of SCORmodel Group ?Intra-Competence (I-C)The “counter-rotating” correlationwithin the customer-facing indicators,i.e., between customer service(reliability and responsiveness),concretely delivery performance –inbound or outbound, and flexibility,concretely supply chain response time,had to be rejected as model-contrary.The respective correlations were exactly

“reversed”. The resulting counter-thesiswould be as follows:

A high on-time deliverypercentage – inbound and outboundaccompanies a high backorders value.

The counter-thesis content does notmake sense from the aspect ofplausibility, because it is the primaryintention of on-time deliveries to avoidbackorders.694As far as this isconcerned, the counter-thesis cannottherefore be theoretically supportedwithin the model. It is to be assumed thatinconsistencies within the data materialavailable account for this examinationdiagnosis. In addition to this, it must be

noted that in the face of the empiricalconstellation, the model-contrary“counter-rotation” was to be derivedfrom the original theses. However, thecorrelation fell short of beingsignificant. The conclusion thatinconsistencies or “random fluctuations”exist within the data material wastherefore corroborated. 4.2.3.2 Model-contrary theses of SCORmodel group Inter-Competence/Performance Attribute (I-CP)The (positive) correlation betweencustomer service (reliability andresponsiveness), concretely orderfulfillment lead time, and cost,

concretely warranty cost or returnsprocessing cost adopted in thesis 57 hadto be rejected. The constellation upon the basis of theempirical data proved itself to be“counter-rotating”. The counter-thesiscan therefore be formulated as follows:

A low average purchaserequisition to delivery cycle timeappears in conjunction with a highnumber customer disputes.

From the point of view of thecontent, such a counter-thesis wouldseem highly plausible: The highcomplexity of the procurement processdetermines that, even with procurement

of the simplest or respectively low-value products, several departmentsmust be involved within the procurementprocess.695

The SCOR model in its present

form obviously does not (yet) makeenough allowance for the influence ofpresent-day procurement processcomplexity upon customer satisfaction.A possible reason for this could be thatSCOR represents a respective function-or process-orientated model, as opposedto a data-orientated model. Data-orientated models are in a position toillustrate data and relationships, underwhich for example the relationshipbetween delivery data and customer data

would fall. One possibility for further-development of the SCOR model in thatdirection could be the extension of theprocess model to illustrate associatedmaterial and information flows betweencustomers and suppliers.696

The (positive) correlation adopted

in thesis 72 between customer service(reliability and responsiveness),concretely perfect order fulfillment, andassets, concretely inventory days ofsupply, had to be rejected, as arecognizably contrary relationshipexisted between both parameters uponthe basis of the empirical material. Thepossible counter-thesis is therefore:

A low average order-to-shipment lead time correlateswith a high lines on-time fillrate.

Such a counter-thesis may be

justified on the following bases: It canbe assumed that the reduction in order toshipment lead time ranks high in theimportance of today’s Supply Chainstrategies. A possible effect of thiswould be the achievement of a perfectorders rate.697

The optimization of the processes

regarding order to shipment lead timealready represents a substantialcomponent of the respective Supply

Chain and competitive strategyrespectively for a multitude ofcompanies.698 And if one presumes thatbeyond this fact a high rate of perfectorders has a positive influence uponpayments on the customer side, theprocess acceleration in the sense of adecrease in average order-to-shipmentlead time is of substantial importance forthe competitive factor time in the contextof contemporary competitivestrategies.699

It may be concluded that the SCOR

model does not yet make sufficientprovision for the circumstance whereby,due the contemporary prioritizing ofdecreased average order-to-shipment

lead times, a “counter-rotating”influence upon the order completion rateis no longer present.700 The correlationadopted in thesis 73 between customerservice (reliability and responsiveness),concretely order fulfilment lead time,and assets, concretely cash-to-cashcycle time, was rejected on the groundsof the significantly “counter-rotating”diagnosis. The possible counter-thesisin accordance with this is as follows:

A high number of transactionsprocessed via web/EDIaccompanies a high averagereceived finished goodsturnaround time.

A possible explanation for thejustification of this counter-thesis couldlay in the fact that – although theelectronic processing of procurementprocedures, according to industryaffiliation, can make possible costsavings of 10 to 20 percent of processcosts and 3 to 12.5 percent of total costs– the savings are probably not achievedvia the shortening of process cycle time.Moreover, a lack of networking andsynchronization between the departmentsinvolved leads to redundant stages ofwork which mainly have to be manuallycarried out, and are therefore relativelypersonnel- and time-intensive.701 TheSCOR model does not account enoughfor this and attaches too little importance

to this factor. A reason for this factcould be that the concept of E-Businesswas only first introduced into the modelin Version 6.702

4.2.4 Summary of theexamination resultsaccumulated for the singlehypothesesOf the 80 evaluated single theses,roughly three fifths were proven to besignificant or at least model-conformingly indicative of thecorrelation direction in the prognosis.703

About a third of the theses were provento be unsystematic in the definedsense.704 A small amount of roughly a

twentieth had to be classified assignificant or at least tendentiallycontrary to the model and presumedcorrelation direction.705

By the enlisted use of pure binary

division according to p(α)-errorprobability (significant vs. non-significant), roughly 37 percent of thetheses were to be classified assignificantly model-conformant andabout 1.5 percent as significantly model-contrary.706

In connection with this it must be

considered that a summarizing string ofsingle theses is only restrictedly suitablefor the purpose of confirming a theory –

understood as a hypotheses system – in astatistical-methodical sense. Severalhypotheses or a system of hypothesesbelong to a theory in this sense.707 Aswas made clear at the beginning of thestudy, the objective here was, amongstother things, to make an initialcontribution towards a theory of thiskind. This study does not, however,claim to have conclusively investigateda theory.708 Rather, it adopted anexploratory approach, in which asuccessive accumulation of knowledgestood in the foreground. Such anapproach must be continued beyond thisstudy by means of further iterativeexaminations building upon it.709

Furthermore, a multitude ofalternative illustration options for themodel exist in addition to the onedeveloped in the context of the work athand. The inference to the SCOR model,in a general sense, must really be seenwith this as a background. Beyond this,interfering influences occurring withprobable certainty and the limitationspresent during the examination must notbe disregarded. These will be dealt withat the end of the present chapter. Thederivation of respectiverecommendations for improvement orinnovative indications with regards tothe SCOR model and its application,gained on the basis of the accumulatedresults, needs to continue. This aspect

will be addressed in Chapter 5.710

4.3 Attempt at Application ofStructure-analyticalProcedures ?to Verify theMeta Theses4.3.1 Design of theexaminationDirectly after the investigation of thesingle theses by means of inferential-statistical procedures, an attempt wasmade to examine the seven establishedMeta theses711 using a structure-analytical procedure. During this,concretely the AMOS procedure cameinto use.712 Because the Meta theses

focus upon a superior level, theassignment of this procedure seemedappropriate. It is important to note in thisinstance, though, that the single thesessubsumed beneath a Meta thesis for thepurpose of structural analysis may in noway be allocated to the same extent ofthe respective Meta theses. This canalready be seen by the fact that somesingle theses were emphatically model-conformant, whilst others wereunsystematic and, in their turn, somewere even partially model-contrary. Thepossible reasons for this state of affairswere equally described.

The attempt to apply the structure-analytical procedure must be seen with

this as a background: It should serve toclarify whether the results accumulatedwithin the framework of the detailed(inferential-statistical) examination and(interpretative) observation andregarding the single theses can betendentially confirmed and, if not, whatthe possible reasons for this could be.During this it should not be about, as itwere, hastily confirming (or rejecting)blocks of theses (on Meta theses level)using structural analyses.

Firstly, the focus of the study layclearly in the detailed investigation ofbivariate assumptions of correlationbased on the compiled singlehypotheses. Secondly, the sample size of

N = 73 would in no way have beensufficient for the exclusive dependenceupon the support of structural equationmodeling procedures.713 Within theframework of the additional structure-analytical calculations – respectivelysplit according to the seven Meta theses– it was decided to transpose the presentparameters. Up to that point, these hadbeen calculated on a bivariate level.Now, they were transformed into morecomplex models, or rather models on amore aggregated level. The investigationof these partial models for sufficientcompatibility with the empiricallyidentified data situation, was madepossible by means of the previously-mentioned AMOS program.

With regards to the partial models

to be tested, it must be noted thatpatterns of correlation should only beextracted when they fulfill the followingtwo conditions: 1. The partial models arise logically

from each single theses allocated toone Meta thesis. They thereforecomprehend the bivariate correlationspostulated by means of singlehypotheses.

2. The partial models actually extendbeyond the level of complexity ofsingle hypotheses which existexclusively upon a bivariate level,and at least illustrate the correlationpresumptions associated with a Meta

thesis.

The second condition thereforecontained the illustration of a pattern ofcorrelation comprising severalparameters. Modeled upon pertinentliterature,714 the theoretical measurementsupportability was assumed if certainindicators715 could be identifiablyallocated to a factor or respectivefundamental dimension, and thisstatistical allocation was durable goingby factor-analytical conventions, i.e.,had a substantially high intercorrelationof the respective indicators amongstthemselves.716 Because more than twovariables were principally involved,“advanced” variable relationships were

present.717

A degree of orientation by

structure-analytical conventions,according to which the followingsymbols are customarily used, wassought during the illustration of theformed partial models:718

An ellipse represents respectivedimensions or factors consisting ofindicators (single measures). Instatistical literature, they aredescribed as hypotheticalconstructs or latent, non-measurable dimensions, dependingon the field of use.

Rectangular boxes represent so-c a l l e d indicators as anoperationalization of factors.Arrows visualize relationshipswhich are postulated as substantial,whereby the arrow’s directionshows the direction of dependency(assumed causal direction) andgives an indication as to whetherthe observed measures converge ordiverge.

A variety of information can be

found in literature with regards to theacceptance or rejection of structure-analytical models, whereby theorientation takes place by means of thepreviously mentioned Goodness-of-Fit

Index (GFI) or Adjusted Goodness-of-Fit Index (AGFI).719 In some cases, aGFI or AGFI of ¾0.9 is required, and inothers a value of ¾0.8 in conjunctionwith a positive degree of freedom720 ofthe model.721

4.3.2 Verification of thesuitability ?of the Metatheses for creation ?ofstructure-analytical partialmodelsAs already introduced and outlinedelsewhere,722 it is necessary to make useof so-called hypothetical constructs inorder to investigate a structure-

analytical model and the causaldependencies postulated therein with thesupport of suitable measurementindicators. In scientific methodology,reference is made in this respect to atheoretical and an observationallanguage. The theoretical language orlanguage on a general model-descriptivelevel works primarily in conjunctionwith hypothetical constructs, whilst theobservational language uses terms thatrefer to the directly observableempirical phenomena.723

In order to describe the correlation

between the hypothetical constructs, onecannot avoid defining every latentvariable by means of an indicator or,

better still, several indicators: Theindicators – as explained above –represent the empirical illustration of thenon-observable latent variables. Themapping takes place with the aid ofcorresponding hypotheses, whichconnect the theoretical terms with theterms of the observational language.724

The AMOS approach at causal

analysis, useful for model evaluation inthe submitted work, is also founded uponthe previously-mentioned concepts: Astructural model is formed, whichillustrates theoretically or subject-logically derived relationships betweenhypothetical constructs. The dependentlatent variables are represented as

endogenous, and the independentvariables as exogenous measures.Subsequent to this, a measurement modelis determined for the latent exogenous aswell as for the latent endogenousvariables. The acquisition of the latentvariables can only take place via theempirical indicators (constructionaloperationalization). Causaldependencies between the indicatorvariables are defined under AMOS bycovariances and correlations. Duringthis, differentiations can be establishedbetween latent variables and theirindicators, as well as above all else,between latent endogenous andexogenous variables. On the whole,AMOS comprises an analysis on the

level of aggregated data material(covariance and correlation data) and isintended to evaluate a given hypothesessystem en bloc, i.e., as a completeentity.725

In the work at hand and built upon

the AMOS guidelines, an exemplarypartial model allows itself to be draftedin the form of a graph, as depicted in thefollowing illustration. In this case, weare actually dealing with therepresentation of the partial modelbelonging to Meta thesis VI.726 In thiscontext, the postulated latent dimensionsand overt indicators are pictured,whereby the Performance Attributesfundamental to the Meta theses – which

cannot be immediately measured –represent latent dimensions. A “bottom-up” mapping of (immediatelymeasurable) Performance Measurestherefore takes place in the shape ofovert indicators (illustrated byrectangular boxes) to PerformanceAttributes in the form of hypotheticalconstructs (illustrated by ellipses). Arepresentation of residual variables ormeasurement errors has been excludedfor reasons of lucidity.727

After determination of the model

structure by AMOS, it is necessary toinvestigate the extent to which themathematical estimation of the modelparameters is actually possible, or

whether the degrees of freedom areappropriate for the parameters to beestimated. In accordance with this, thenumber of degrees of freedom requiredin order to solve a structural equationmodel must be larger than, or equal to,zero. The following formula expressesthis factual situation:729

t = p*= 1/2 (p+q) (p+q+q)

With: t = number ofparameters to be estimated

p = number of the y-variables

q = number of the x-variables

Tbl. 4-83: Legend to Diag. 4-12: Index of the applied

performance measures728

Index Performance Measure

CS-1 Customer retention rate

CS-2 Backorders value

CS-3 On-time delivery percentage (inbound andoutbound)

CS-4 Percentage of purchased orders received on timeand complete

CS-5 Percentage of purchased lines received on time andcomplete

CS-6 Average MPS plant delivery performance (workorders)

CS-7 Cycle count accuracy percentage

CS-8 On-time delivery percentage (inbound andoutbound)

CS-9 Perfect orders rate

CS-10 Lines on-time fill rate

CS-11 Customer retention rate

CS- Average purchase requisition to delivery cycle time

12CS-13 Transactions processed via web/EDI

CS-14 Average manufacturing cycle time

CS-15 Percentage of sales via web

A-1 Average received finished goods turnaround time

A-2 Inactive inventory percentage

A-3 Average order-to-shipment lead time

A-4 Average inventory turnover

A-5 Average operating-equipment efficiency rate (OEE)

A-6 Average plant capacity utilization

A-7 Average warehousing space utilization

The testing for the model

structure’s ability to be identified isguaranteed by AMOS itself (byautomatic parameter estimation),because in the case of non-identifiable

models, the model does not complete thecalculation, in so far as that nounequivocal solution for the equationsystem is given. Also, the model’squality is questionable in the presence ofnegative variances, correlations ¾1(excluded anyway according to theconvention) or very high standard errors,as such effects would suggest theconclusion of problems duringidentification of the model.

Diag. 4-12: Mapping of structure-analytical partialmodel to Meta thesis VI

The implementation of structure-analytical calculations using AMOS isprincipally made easier by satisfactorilyhigh levels of sample sizes.TheUnweighted-Least-Squares’ method hassometimes verified its ability for thepurpose of parameter determination.730

Unlike alternative estimationprocedures, such as the Maximum-

Likelihood-Variant, this method offersthe advantage that consistent estimatesare possible and do not depend upon aminimum sample size.731

With a sample size compatible with

the presently implemented examination,reliable and valid results – withconsideration to the previously-mentioned reservations and in view ofalbeit rather simpler model structures –could be attained in other structure-analytical studies.732 It was also clearfrom the beginning that the achievablesample size for the work submitted lay atbest at or even under the minimum limitrequired for the complete testing of amodel.733 For this reason, the

investigation of the model structure forthe partial models was not successfullyrealizable, in as much as all relevantsingle theses for this purpose had beenprocessed. Sample sizes of at least N =100, or possibly even larger samples asrecommended by Backhaus et al. andother researchers, would have beennecessary here. Consequently, aninvestigation of the total model, i.e., thecomplete combination of all theindividual partial models, was notpossible. It may be summarized that, inthe present case, it was not possible todevelop a structure-analytical modelwhich, with regards to the associatedvariables, would have providedsufficient coverage and simultaneously

fulfilled the necessary assumptionconditions to meet scientificrequirements. It would perhaps havebeen principally possible to composepartial models which contain purely arestricted number of variables. By doingthis, however, the significance of thestatements would have been furtherreduced, in addition to the alreadycritical sample size (N = 73) necessaryfor structure-analytical evaluationsunder application of strict scientificmeasures.

Furthermore, it must be consideredthat structural equation models alsodemand, in addition to statisticalcriteria, certain content requirements

from the data material to be examined –for example, the presence of a securedtheory (at least to a rudimentarydegree).734 Because this study is of anexploratory nature, it cannot sufficientlyaccount for this requirement and mustonly make an initial contributiontowards it. This train of thought iscaptured again elsewhere in Chapter 6 inconjunction with the possibilities of theassignment of structure-analyticalprocedures within future studies.735

4.4 Identification ofInterfering Influences andErrors4.4.1 Criticism of the

selection procedureThe procedure of typical case selectionapplied in the study shows severalknown disadvantages. The first problemcomprises determining those criteria inaccordance with which the elementsobserved are to be classified. Thesecriteria can only be defined by theexamination objective, and the influencethat this can have upon the examinationresults may prove difficult to estimate.

Secondly, the procedure alreadypredetermines respective priorknowledge of the total population. Onemust know in advance, for example, howthe relevant characteristics (according towhich the typical cases are defined) are

distributed within the total population.

Thirdly, in the empirical context,the selection cannot be adjusted to thecharacteristics of actual interest, butsubstitute characteristics have to beenlisted in order to determine the typicalcases. During this, it must be consideredthat the substitute characteristics mustalso be typical with regards to the actualcharacteristics of interest.736 The firstdisadvantage is appropriate in this case,but it must be noted that the examinationobjective was clearly known.Disadvantages two and three are onlyrestrictedly valid, because with the typeof information retrieval in question, oneis dealing with a case of secondary

research. As a result of this, knowledgeof the total population was alreadypresent and a typical selection wassimplified. The person conducting thesecondary analysis is similarlyrestricted during the hypotheses testingby the quality of the material used,which is determined by factors such asthe method of the primary research, thesample, etc.737 In addition to this, theactual characteristics of interest wereknown at the time of the examinationimplementation, so that no substitutecharacteristics had to be defined. It muststill be recorded, though, that thesampling method applied does not layclaim to representing the examinationresults.

4.4.2 “Blurring” of thehierarchical assignment ?ofperformance indicators“Blurring” is possible with regards tothe allocation of performance indicatorsfrom latterly to formerly situated levels(i.e., performance measures to level 1metrics). The Supply-Chain Counciladmits that within the SCOR model, theperformance indicators may not be, orare not always clearly able to be,assigned to the SCOR main processes(chevrons).738 In addition to this,overlapping sometimes occurs, which isdue to the comprehensive detail of themodel.739 On the other hand, the

performance indicators used within theframework of the examination referdirectly to a SCOR main process.740

An attempt was made to confront

this disadvantage by means ofinvestigating where the reasons lay incase of the rejection (falsification) of athesis. Within the context of thisadditional and interpretative attempt atexplanation, the “blurring” in assignmentalready mentioned was taken intoconsideration.741

4.4.3 Realization of theexamination ?as asecondary analysis

The examination submitted wasimplemented as a secondary analysis,which conceals advantages as well asdisadvantages. The biggest advantagecan almost certainly be seen in the factthat the necessary data did not need to beaccumulated, but was already present.The aspects of research economy andtime were decisive criteria during theselection of the analytical procedure.

A substantial disadvantage ofsecondary analysis exists, however, inthe fact that the evaluation mustinevitably be restricted to the dataalready present. The researcher planningthe examination therefore needs, in allcases, an example of the questionnaire

assigned within the primary survey, theinterview instructions, and the resultingdata.742

In this study, the required

information was unrestrictedly availableto the author. For this reason, anassessment on the extent to which thedata could be used was possible to asufficient extent. The completequestionnaire and the instructions forcompletion may be taken from theappendix;743 the data material in itsentirety is available from the author.744

4.4.4 Scope of the examinedsampleThe sample selected for the purpose of

the secondary examination contained thedata from 73 (typical) companies. Thescope of this sample seemed thoroughlyadequate for meaningful descriptive-statistical and correlation-analyticalresults. With the given scope, the risk ofthe correlation level being substantiallyblurred by extreme values is alsominimized.745

However, as has already been

mentioned, a sample size of over 100 isusually favored for structural equationmodels similar to the applied AMOSprocedure.746 A two-stage approach waschosen to counter this possible point ofcriticism: First, a correlation analysiswas carried out and, following this and

purely for selected special cases –concretely for investigation of the Metatheses – an attempt was made to apply astructural equation model. Therestriction therefore only refers to thosecases in which the second stage wasapplied. Beyond this, the second stageprimarily served as the attempt at anadditional corroboration of the resultspreviously compiled by means ofinferential-statistical methods anddescriptive-analytical descriptions.747

4.4.5 Inadequacies in theterminology ?for theperformance termsIn Chapter 3, the insufficiencies present

concerning the definition of terms inconjunction with the SCOR model weredealt with in detail.748 An attempt wasmade to confront this problem byadopting Seibt’s incitement and creatinga consistent definition of terms, whichwas adhered to throughout the study.749

Chapter Five

Summary of conclusionsand innovative

assessments This chapter addresses the context ofrealization, as seen within theframework of the course of researchlogic according to Friedrichs. Thiscontext, which has consistently beenidentified as a central theme from thepoint of view of critical social-scientificresearch theory,750 may usefully bedescribed as follows:

Under context of realization,the effects of an examinationare to be understood, as is itscontribution to solving theinitially posed problem. Theexamination has a knowledge-theoretical function, in that itexpands our knowledge ofsocial connections.751

5.1 Overall Appraisal andInterpretation of the SCORModel due to the Results ofthe ExaminationIn this chapter the empirical results arecollectively analyzed once again by the

use of single theses blocks within theSCOR model groups. This is done withthe aim of undertaking a completeevaluation of both the illustrationdeveloped within the framework of thework submitted, and the SCOR modeloperationalized by means of the derivedtheses model. Consequently, ahermeneutic observation752 of the singletheses’ results can take place upon anaggregated level. Together with thedetailed explanation of the examinationresults and the interpretational work inChapter 4, it accounts for the postulateaccording to which the analysis isconstituted by (statistical) evaluationsand (content-theoretical) interpretations,together with the data collected by

means of empirical instruments (in otherwords: the empirical material present inthe form of numbers).753

Chapter 4 also contains a

discussion on the possible reasons forresults that are barely or definitively notcompatible with the model concepts.754

As emphasized in the introduction, thisstudy cannot (and does not wish to) layclaim to having investigated thesuitability and validity of the SCORmodel per se. Rather, it represents anexploratory contribution in this field,which must be taken up and continued byfurther studies. The followingexplanations must be seen with thispremise in mind.

5.1.1 Reflection of theSCOR model based uponthe results of the SCORmodel groupsThe first Meta thesis establishedreferred to the SCOR model group Intra-Performance Attribute (I-P).755 In thisthesis, the performance measures withina performance attribute conform to oneanother, and thus a degree of consistencymust exist between these performancemeasures. The Meta thesis wasunambiguously confirmed by theempirical data. Of the eighteen singletheses subsumed beneath this Metathesis, eleven could be classified as

significantly or at least tendentiallymodel-conformant. An unsystematicdiagnosis was reached for seven of theinvestigated theses; i.e., a “non-correlation” had to be correlation-analytically assumed.756 Consequently, aclear model-contrary relationship wasnot identified for any of the examinedparameters of the (single) hypothesesassociated with this Meta thesis.

During the application of purebinary division according tothe p(a)-error probability(significant vs. non-significant), 38 percent of thetheses were thereforeclassified as significantly

model-conformant, and 0percent classified assignificantly model-contrary.757

The SCOR model group Intra-Performance Attribute (I-P)could therefore be validatedas tentatively confirmed.

The second and third Meta theses

referred to the SCOR model group Intra-Competence (I-C).758 The second Metathesis contained the correlation betweencustomer service and flexibility. In thisarea it was expected that the generalstatement that a high (low) customerservice would accompany a high (low)

flexibility would be confirmed. Thisstatement may be considered assupportively corroborated upon thebasis of the observed factualconnections. For the ten single thesesrelevant here, in two cases a tendencial– not statistically meaningful – model-contrary constellation was present, andin one case a totally unsystematicconstellation was given. The results ofall other theses were covered on thewhole by the model postulaterepresented by Meta thesis II.

During the application of purebinary division according tothe p(a)-error probability(significant vs. non-

significant), 58 percent of thetheses were thereforeclassified as significantlymodel-conformant, and 0percent classified assignificantly model-contrary.759

Meta thesis III assumed a parallel

correlation between costs and assets. Inthe case of the nine single thesesestablished, a model-contrary diagnosticsituation (even if it was tendencial orrudimentary in nature) was notunexceptionally present. Only in the caseof three single theses did a conclusion oflargely unsystematic circumstances haveto be drawn in the face of an explicit

correlation level within the area of 0.00to 0.10 (absolute). On the whole,however, nothing contradicted thefundamental suitability of the respectiveMeta thesis.

During the application of purebinary division according tothe p (a)-error probability(significant vs. non-significant), roughly 33percent of the theses weretherefore classified assignificantly model-conformant, and 0 percentclassified as significantlymodel-contrary.760

With this, the SCOR modelgroup Intra-Competence (I-C)could therefore be validatedas tentatively confirmed.

Finally, Meta theses IV to VII

referred to the SCOR model group Inter-Competence/Performance Attribute (I-CP).761 A comparatively high number ofsingle theses were assigned to theseMeta theses, according to which a high(low) customer service correlates withhigh (low) costs. Of these twenty singletheses, half proved themselves to besignificantly model-conformant. Themajority of the remaining single theseswere more or less statistically classifiedas unsystematic. A model-contrary

parameter relationship could only beidentified for one single thesis (expected“synchronization” between purchaserequisition to delivery cycle time andnumber of customer disputes).762 On thewhole therefore, a model-compatiblediagnostic situation existed for Metathesis IV, even if this compatibility wasnot as strongly expressed as in the caseof theses I to III.763

During the application of purebinary division according tothe p(a)-error probability(significant vs. non-significant), roughly 32percent of the theses wereclassified as significantly

model-conformant, and 0percent classified assignificantly model-contrary.764

Meta thesis V, which assumed that

a positive correlation existed betweenflexibility and costs, included eightsingle theses. Only a quarter of thesesingle hypotheses were classified asunsystematic, and a model-contrarycorrelation was not given. All othertheses proved themselves to be model-conformant in the face of the identifiedparameter relationship directions, albeitwith an emphatic and only tendentiallymodel-compatible percentage. So far,this Meta thesis seemed an acceptable

measurement based upon the empiricalfacts.

During the application of purebinary division according tothe p(a)-error probability(significant vs. non-significant), roughly 25percent of the theses wereclassified as significantlymodel-conformant, and 0percent classified assignificantly model-contrary.765

A different conclusion had to be

drawn with respect to the sixth Metathesis. In this case, a positive correlation

or a “parallelism” between customerservice and assets was assumed.Roughly half of the respective ninesingle theses were judged as model-adequate. Two single theses, on theother hand, proved themselves to besubstantially, or at least rudimentarily,model-contrary, and the remaining singletheses proved themselves to beunsystematic to the largest extent. Facedwith this empirical diagnostic situation,it has become necessary to reflect moreclosely upon the possible reasons forsuch a model inadequacy (even if it waspartial), and to work out respectiveoptions for innovation. This occurs inthe following paragraph.

During the application of purebinary division according tothe p(a)-error probability(significant vs. non-significant), roughly 25percent of the theses wereclassified as significantlymodel-conformant, and 8percent classified assignificantly model-contrary.766

In the case of Meta thesis VII,

whereby a positive correlation wasassumed between flexibility and assets,a model-conformant pattern becameapparent at large. Of the six single thesesassigned to this Meta thesis, half were

model-conformant in a statisticallysignificant way. Two single theses couldbe tendentially confirmed, only one wasstatistically unsystematic.767

During the application of purebinary division according tothe p(a)-error probability(significant vs. non-significant), 50 percent of thetheses were classified assignificantly model-conformant, and 0 percentclassified as significantlymodel-contrary.768

With this, the SCOR modelgroup Intra-

Competence/PerformanceAttribute

(I-CP) could therefore also bevalidated as tentativelyconfirmed.

Consequently, the following

statement arises regarding the centralassumption, which was established forthe purpose of the empiricalexamination:769

After consideration of theaforementioned restrictions,and for the developeddepiction of the SCOR modelin hypotheses form, it can beconsidered as tentatively

confirmed that thePerformance Metrics assignedto the Performance Attributeswithin one of the two SupplyChain competences(performance capability andefficiency) are consistent withone another, i.e., point in thesame direction. Theperformance metrics assignedto the Performance Attributesbetween the two competencesmutually compensate eachother, i.e., they guarantee abalance between the variousobjectives.

This assessment rests upon the

substantial predominance oftheses confirmed in astatistically significant wayover significant model-contrary theses. Empirically,the last named case hardlyplayed a role.

Based on the attained examination

results, and in addition to the derivationof the above-mentioned conclusions,further conclusions may be drawn in theform of recommendations. These will bespelt out in the following paragraph. Asalready mentioned, it must be borne inmind that the recommendations refer, inthe first instance, to theoperationalization of the SCOR model.

As previously indicated, other forms ofmodel operationalization arepossible.770 An inference to the(abstract) SCOR model must thereforebe seen in the respective context. As aresult, generalizations are only possiblein a restricted manner and mustinevitably be seen with the premise of anexploratory approach.771

5.1.2 Potentials forimprovement andrecommendationsAbove all, the results of Meta theses VIindicated that potential for improvementto the SCOR model seems to exist withregards to the connection between

customer service and assets. In detail,the Meta thesis assumed that a high(low) customer service correlates withhigh (low) assets.

As already mentioned, the SCORmodel does not attempt to describeevery company process or activitywithin the Supply Chain.772 Thecomponents consciously “omitted” aremarketing and sales (i.e., demandgeneration), research and technologydevelopment, product development, andsome areas of post-delivery customerservice.773 The results of the submittedexamination suggest that in the contextoutlined here, the inclusion of marketingand sales would be necessary,

especially as these represent asubstantial component of customerservice in the present-day competitiveenvironment.774 Modern Supply Chainstrategies775 assume the existence, as arule, of sales and distribution-controlleddelivery ability.776 Inevitably,consequences arise for the stock levelsand range of stock.777 In conjunctionwith this, it is important to note that thesimultaneous requirement for a highcustomer service, especially withrespect to high delivery reliability andshort delivery lead times, has a decisiveinfluence upon the SC strategy and withthat also upon the required assets,especially the inventory days ofsupply.778 Additionally, the step from

traditional Supply Chain Managementtowards a value-generating approach isintroduced – previously describedelsewhere as the Value Chain.779

The value generating approach

focuses upon the gradual increase invalue and, accordingly, it explicitlyincludes marketing- and sales-orientatedelements in addition to the actualphysical availability, disposal, usageand exploitation of goods.780 However,no efforts have been made by theSupply-Chain Council to change theSCOR model in Version 7.0 and 8.0 inthis direction.781 Hence, therecommendation below can be derived.

First recommendation: Dueto the results of theexamination as well aspresent-day developments inthe competitive companyenvironment, tendencies areapparent whereby theinclusion of marketing andsales into a future SCORmodel version couldcontribute to addressweaknesses and thereforefurther optimize the model.

As explained in Chapter 4 in the

context of the interpretation of model-contrary diagnoses,782 potential forimprovement obviously exists upon the

basis of examined Version 6.0 of SCORwith regards to the procurementprocesses. This situation has notchanged in Version 7.0 and 8.0 ofSCOR.783 Consequently, the followingrecommendation is derived from this:

Second recommendation:The examination resultssuggest that the SCOR modelin its present form does not(yet) fully account for theinfluence of today’sprocurement processcomplexity upon customersatisfaction. A possibility forfurther development of theSCOR model could, therefore,

be the expansion of theprocess model to include theillustration of associatedmaterial and informationflows between customer andsupplier.

Another finding of the study, also

reached through the model-contraryresults, was that the electronic executionof the procurement processes does notseem to be sufficiently expressed withinthe SCOR model Version 6.0.784 Therewere also no significant changes made inthis regard in the subsequent versions ofSCOR.785 Thus, a furtherrecommendation may be made:

Third recommendation: Theinclusion of the concept of E-Business into the SCORmodel, which began withVersion 6.0, should beconsequently continued withspecific regard to theelectronic execution ofpurchasing processes. Bythese means and amongst otherthings, the requirement for anincreasing network andsynchronization between thedepartments involved inprocurement could be betteraccounted for. In addition tothis, suggestions for bestpractices would offer support

for the realization ofimprovements in processexecution.

It must be borne in mind that, in the

case of the aforesaid recommendations,we would not be dealing with anextension to the SCOR model structure,but rather a continuative“supplementing” of the model structurealready present. This aspect will beinvoked again at a later stage within theframework of the recommendations forfurther research.786

5.1.3 Recapitulatoryappreciation of the

operationalization of theSCOR modelThe three SCOR model groupsillustrated above mutually cover thedeveloped operationalization of theSCOR model as a whole, with the SCc o mp e te nc e s customer-facing andinternal-facing on the one side, and thehierarchically arranged key performanceindicators (performance attributes –level 1 metrics – performance measures)on the other side.787 A hermeneuticappraisal788 of the results of the sevenaggregated Meta theses and theassociated blocks of single hypothesesrespectively allow the conclusion thatthose are to be deemed to be supportive.

Based on the examination results, thedepiction of the SCOR model developedwithin the framework of the work can beseen respectively as suitable, or ashaving a close proximity to the truth.789

In this case, it must be borne in

mind that we were dealing with anexploratory examination of the model.As a result of this, a universally validstatement as to the suitability or closeproximity to the truth of the model assuch can in no way be made based uponthe accumulated knowledge. Thefindings rather represent an initialcontribution in the form of a confirmedcentral assumption790 towards auniversally valid statement.791

On the whole, noticeable

discrepancies could not be foundbetween the model concept andempirical reality.792 However, certaindeficits became apparent in the case ofMeta thesis VI, which contained therelationship between customer serviceand assets, and in the case of whichpossible areas for improvement wereindicated. A representation of modernconcepts and tools will now beinitialized for Supply Chain formationunder application of the SCOR modeland based on the accumulatedexamination results. For this purpose,the following continuative questions areformulated and raised:

Which innovative approaches forthe Supply Chain’s formation andoptimization are currently beingdiscussed in academic science andcompany practice?Which possibilities and moderntools exist that could be applied forthe improvement of the SCORmodel’s usage and consequently theformation and optimization of theSupply Chain?793

As described earlier, this study

was particularly concerned withanswering the following researchquestions:794

How could the SCOR model betransposed onto a thesis model andoperationalized based on themodel-immanent performanceindicators?How could the SCOR model’sconveyed depiction be submitted toan exploratory examination basedon empirical data?

This study had the explicit

objective of developing a special formof SCOR model operationalization andinvestigating its adequacy and proximityto truth. It is therefore to be left toadvanced studies to undertake an attempt

at more exhaustive answers to theaforementioned questions, as well asother questions resulting from them in asimilar exploratory context, and tocorroborate them by means of scientificinvestigation. In Chapter 6, an attempt ismade to derive concrete suggestions asto in which areas and in which form thiscould take place.795

5.2 Innovative Approachesfor the Formation andOptimization of the SupplyChain5.2.1 Representation of theAdaptive Supply Chain

It has become apparent since around theyear 2000 that the Supply Chain’sperformance capability and efficiencyrepresent necessary conditions forcompanies’ success in the contemporarycompetitive environment, especially insectors of the production industry likewholesale and retail. To ensure thefulfillment of these conditions, so-calledAdaptive Supply Chains or AdaptiveBusiness Networks are currently beingdiscussed.796

Adaptive Supply Chains (ASC)

highlight known redundancies in order tohelp companies cope with unforeseenevents. They are at present (beginning of2007) in the process of replacing the

traditional SC approaches outlinedearlier, including the advanced virtualSC networks. ASC possess theflexibility to continuously adjustthemselves to changing marketrequirements and therefore react in anoptimal way to environmental variables,i.e., with maximum efficiency and inreal-time.797 In order to fulfill theserequirements, ASC combine ordermanagement, planning, production anddistribution management procedures intointegrated business processes andprovide the SC network with real timeinformation. In this way, they enablequick decisions in addition to theirefficient and effective execution.798 Theadvantages of ASC may be collectively

set down as follows:

“Adaptive Supply Chainsprovide a cohesive processinfrastructure that connectsnetwork participants, providesvisibility, and monitors forchanging conditions. Whenconditions change, theconsequences are immediatelydetermined and affectedparties are notified withrecommended courses ofaction for optimal results.Once approved, a new actionis executed and the plan isadapted within context of thisnew process. The result is

improved performance acrossthe global supply chainnetwork.”799

The transformation of a traditional

Supply Chain into an ASC necessitatesthe investigation of and necessarychanges to the basic Supply Chainprocesses in order to remaincompetitive. The Supply Chain then nolonger represents a static system, butmoreover a dynamic, self-changing andadaptive, high-performance network.The changes to market conditionsdetermined by the internet play a majorrole in this case.800 The definition of theASC resulting from this must be seen incontext with the presently available

internet-based possibilities.801 For thispurpose, the definition of theInformation Management View, as wellas the E-Business concept according toSeibt already mentioned elsewhere, iscalled-upon:802

An Adaptive Supply Chain(ASC) is based upon a SupplyChain integrated by means ofinformation technology, inwhich the flow of informationbetween diverse partiesrepresents the integrationfactor. In this sense, itpossesses a communalinformation basis, in additionto mechanisms enabling an

exchange of this informationamongst the participants. Forthis purpose, several to all ofan organization’s processeswith reference to the SupplyChain

within a companybetween it and its business partnersbetween it and a third party (e.g.,authorities)

are totally or partially realizedby electronic communicationnetworks, and supported bythe assignment of Informationand Communication

Technology (ICT) systems.803

Having set down what an ASC

involves, the way in which this kind ofSupply Chain can be put into action – orrather, can be realized – is to beaddressed in the following paragraph. 5.2.2 Realization ofAdaptive Supply ChainsThe realization of ASC stems from anexisting Supply Chain and can be bestexplained in the form of a stagedprocess. Heinrich and Betts, forexample, describe the procedure toachieve an ASC by means of a four-stage process:804

First stage: Visibility: Exchange ofinformation with SC partners,standard processes for most routinetransactions with SC partners,information exchange by means ofinternet-based technology, andadditional insight into a company’sprocesses and data problems.

Second stage: Supply ChainCommunity: Execution of regularlyoccurring transactions by means ofso-called portals,805 introduction ofminimum and maximum monitoringvalues (for example for inventorystocks), reduction of inventorystock, and increased efficiency of

process flows by means ofautomation.Third stage: Collaboration:Exchange of customer requirementinformation amongst SCpartners,806 determination of targetinventory replenishmentmeasures,807 transferal ofresponsibility for stockreplenishment to the suppliers,808

and the possibility of allocatingstock in accordance with orderreceipt to fulfill the maximumamount of orders.809

Fourth stage: Adaptability:Significant reduction of processtimes, multiple elimination of workstages, significant reduction of

inventory levels and workingcapital, release of new marketchances through strategicpartnerships, and the introductionof new products.

Stages one to three were previously

developed and applied in the past withinthe framework of traditional SupplyChain and Value Chain strategies. Thesubstantial difference in the ASC lies inthe presence of the fourth stage, asconventional Supply Chains are of astatic nature. Only the fourth stageenables the development up to and intoan ASC.810 Heinrich and Bettscharacterize this fourth stage and the

changes accompanying it as follows:

“In step four, companies beginto automate many morebusiness processes (…). Inaddition, the move from stepthree to step four involvesincreased technologycomplexity and a heighteneddegree of automation amongan expanding number ofnetwork partners.”811

Lawrenz and Nenninger also call

the transition from a linear Supply Chainto an ASC, i.e., from stage three to stagefour, a transition to E-BusinessNetworks.812 In agreement with this,

Schäfer, in his empirical study, arrivesat the conclusion that nearly 97 percentof companies surveyed agreed with thestatement that the internet – as aninstrument of E-Business – offerscompletely new possibilities ofarranging business processes in acompany-spanning manner.813

The special challenges with

regards to ASC result from therequirement to exchange informationwithin a company, as well as withrelevant SC partners. To arrange thecreation of electronic partnerships andalliances effectively, firms must link-in avariety of information systems in orderto avoid inefficiencies and

redundancies. The SC environmentspresently found are of a progressivelycomplex nature and comprise a multitudeof sub-processes and activities. In thiscase, industrial companies do notrepresent the only group that has theoptimization of the company processesin general and the SC processes inparticular as their objective. The sameapplies to the public sector, asdescribed elsewhere by the example ofthe US Department of Defense, DoD.814

The requirements on the

competitive side literally forcerevolutionary changes to the existing SCprocesses. The result is the mutation oftraditional linear and static Supply

Chains into dynamic value chains, or inother words, ASC.815 Radjou et al.postulate as follows in conjunction withthis:

“To cope with volatility firmsneed to migrate their staticsupply chains to adaptivesupply networks.”816

The existing tools and applications

for management of the Supply Chain, asalready described elsewhere within thecontext of Supply Chain Management,are often no longer (or only restrictedly)capable of keeping up with the changesthat are required in order to achievehigher efficiency.817 The modern tools to

remedy this will be outlined in the nextsection. Because these tools are basedupon the SCOR model, it would alsohave been plausible to imagine them incorrelation with the present condition ofthe SCOR model’s development.818

However, as will be emphasized, sincethey are founded upon the AdaptiveSupply Chains discussed in the previoussection, it seemed more appropriate tointroduce them subsequent to these. Forthis reason, an illustration of existingestablished Supply Chain concepts andSCOR applications may be found at thebeginning of the work, whereas in theprevious chapter an actual outlook uponnew, although not yet further diffusedand tested concepts and assignment

possibilities, is presented. 5.3 Modern Tools forImprovement of theAssignment and ApplicationPossibilities of the SCORmodelAs already often mentioned, the SCORmodel represents a descriptive but not aformative model of the Supply Chain.The model does highlight Supply Chainweaknesses and therefore potential areasfor optimization, but these must then beremoved or realized by using othermeasures. Such a measure for SupplyChain improvement is represented byBusiness Process Reengineering

(BPR).819 Within the framework of BPRliterature, Information Technology (IT)is often seen as an essential enabler820

for the change process. No other factoris said to possess such large potentialfor the achievement of radical processimprovements.821

Hofmann collectively and tersely

unites the requirement for a continuous(re-) design in the face of a permanentlychanging Supply Chain or ASC asfollows:

“The faster the supply chainchanges, the more importantsupply chain designbecomes.”822

The SCOR model does not deal

closely with levels beneath the thirdlevel. This can, for example, be seen inthe fact that the SCC describes the fourthlevel as the Implementation Level, uponwhich companies implement specificSupply Chain flows.823 On these lowerlevels of the SCOR model, process,design and modeling tools for analysisand documentation can be assigned.Tools are available today for static, aswell as dynamic simulations of suchmodeling.

Most companies begin to analyzetheir existing Supply Chain for potentialimprovements using the SCOR model, as

well as definitions and performanceindicators contained within it. In thisway, gaps in processes andinefficiencies in process performancecan be revealed. Improvements can thenbe executed by means of a SupplyChain-related BPR initiative. As a rule,this includes the definition,implementation, and continuousmeasurement of performance indicators.During this, there is a dominatingaspiration to achieve a leading level ofperformance in all relevant SCprocesses that would set the companyabove the competition.824

5.3.1 The concept of SupplyChain Design Management

(SCDM)Supply Chain Design Management(SCDM) is based upon a given SCmodel and aims at the simulation andcontinuous optimization of the ASC. It isa new kind of tool that seeks to helpfirms to identify and improve SCprocesses, performance indicators, andinformation flows within companies andwith other SC partners.825 In thefollowing, the focus is exclusivelyplaced upon such applications and toolsas are based upon the SCOR model.

SCDM primarily has the followingobjectives:826

Validation of the present SC modelby means of existing or actualcompany processes (As-Is businessprocesses);Simulation and prediction of theinfluence upon SC performance inthe case of changes to the SCstructure, up to the To-Beconfiguration;Application of performanceindicators in accordance withindustrial standards for theexecution of the analysis ofalternative SC scenarios (What-ifimpact analysis);Measurement, prediction, andmonitoring of SC influence factorsfor the identification of

improvement potentials;Connection of those corporate andSC processes upon lower levelswhich are relevant for themonitoring of operationalprocedures and systems.

A Supply Chain model is above all

highly effective if it is recognized andaccepted not only internally (i.e., withinthe company) by executives responsiblefor decisions, but also by external SCpartners. In this way, changes in demandand alternative scenarios resulting fromthem can be quickly analyzed in order todetermine what influence they have uponcorporate-policy, financial, and SupplyChain specific performance

indicators.827

The result is a clear comprehension

of the options, risks and effects upon theSupply Chain. In this way, SCDMcreates greater flexibility andadaptability for companies. On astrategic level, SCDM serves not only toinitially design, but also to continuouslyredesign the complete Supply Chain, andtherefore to enable the support of anASC.828

The benefit of a SCDM, dependent

upon the planning level, can bedescribed as follows:829

On a strategic level, it serves forexample to analyze the SupplyChain’s performance capability bymeans of the application ofalternative scenarios to analyzepossible effects of changes in SCstructure in the context of redesign,the identification of optimal SCstrategies – beneath which forexample the previously mentionedPostponement Strategies fall – andthe investigation of the influence ofnew systems and applications tosupport SC processes, i.e., ERPsystems or APS.On an operational level, it servesfor example to forecast demand andthe analysis and simulation of

possible changes in demand, thesupport of the CollaborativePlanning, Forecasting andReplenishment (CPFR) concept, theidentification of availablecapacities to react to changes indemand, and the determination ofbest practices in the case ofdelivery failure by suppliers.

With consideration to these

premises and with the inclusion of thelisted ASC definitions, SCDM can beembedded into this context as follows:

Supply Chain DesignManagement (SCDM) makes auniversally valid language

convention available as aminimum requirement forSupply Chain processes(preferably upon the basis ofthe SCOR model), illustratesthe extended (i.e., company-internal and company-spanning) Supply Chain,enables an analysis ofpossible effects in the case ofchanges in supply and demandfactors in addition to thesimulation of changes inSupply Chain structure andprocesses. It therefore servesto realize and supportAdaptive Supply Chains(ASC) and their design and

redesign. It must, as acomponent of E-Businesssystems and in the total systemlife cycle, be continuouslyadjusted to marketdevelopments and alteredbusiness requirements.830

The following sections relate to an

evaluation undertaken into which IT-based applications are available todayin order to fulfill the aim of supportingSCDM. 5.3.2 Applications forSupply Chain DesignManagement

The applications for Supply Chainformation are not to be confused withapplications for planning of companyprocedures. This latter group includes,amongst others, Enterprise ResourcePlanning (ERP) solutions. Some manufacturers of such integrated,SC-orientated solutions – as for exampleInternational Business Systems (IBS)831

or Business Objects 832 – state that theirproducts orientate themselves or arerespectively based upon the SCORmodel.833

These applications are also not to

be confused with those that are assignedto the initial design and possible

sporadic redesign of the Supply Chain.These are for instance the previouslynamed ERP systems or the AdvancedPlanning Systems (APS).834 Until a shortwhile ago, no electronic tools for thecontinuous design or redesign of theSupply Chain existed. For the furthercourse of the work, the term SCDMapplication was used for tools of thiskind. The objective of the SCDMapplications is to design complexSupply Chains for strategic, tactical andoperational predictions. The researchcompany Gartner835 assumes the marketfor SCDM applications is presentlygaining in importance, and that this willcarry on in the future.836

The author’s research revealed thatat the beginning of 2007 only a fewapplications existed which immediatelysupported Supply Chain formationwithin the framework of SCDM. Thefollowing three manufacturers andapplications, which may be takenseriously and are based in unison uponthe SCOR model, fall within theseapplications:837

e-SCOR by GensymARIS EasySCOR by IDSADOLog by BOC.

The applications that are to be

explored in more detail below share the

explicit purpose of supporting therespective formation or design andcontinuous redesign of the Supply Chainupon the SCOR model basis, as stated inthe previous paragraph. This is not anappraisal of the applications, but ratheran exemplary overview of the scope oftheir functions and their main assignmentpossibilities.838

5.3.2.1 e-SCOR by GensymThe software manufacturer Gensym839

has developed an application by thename of e-SCOR. This applicationdesigns, simulates and controlsgraphically-supported SC scenarios. e-SCOR is based upon the SCOR modelwith its associated processes and

performance attributes. Its main strengths are perceived to be itsability to simulate SC structures andaffect the Supply Chain’s behavior, inaddition to being able to analyze theexactness of an existing SC model. Thistakes place by means of description ofthe SC structure, processes andinformation flows upon an aggregatedlevel, and spans the extended SupplyChain. The aggregated analysis level isconnected to the SCOR model’s lowerlevels (below the third level).

After each respective role has beendetermined (in the sense of SCparticipants and functions) and the SCprocesses have been recorded, the

existing model (As-Is Model) can berepresented and validated by usingperformance indicators and informationabout the work course. In this way, abasis for comparison (Baseline) iscreated for investigating the effects ofchanges.840

Furthermore, alternative To-Be

scenarios can be simulated, whichautomatically project the SCOR-basedperformance indicators into the future.This includes the measurement andsimulation of effects of changes to theSC structure and processes upon therelevant performance indicators (What-if scenarios). Apart from that, modelchanges can be undertaken parallel to

this in order to demonstrate their effectsin real-time. Additional, distinctivecharacteristics of e-SCOR are theanalysis possibilities with regards toorder fulfillment and distributionstrategies, planning and procurementstrategies, as well as financialperformance.841

Due to the fact that the performance

indicators correspond with the SCORperformance indicators, customer-facingindicators are included on the one side,such as delivery ability and orderexecution performance. On the otherside, internal-facing performanceindicators are taken into consideration,as for example cash-to-cash cycle time

and Return on Assets (ROA). In thisway, the known conflict in objectives(trade-off) between the performanceindicators can be analyzed and theireffects can be highlighted.842

5.3.2.2 ARIS EasySCOR by IDSThe consultancy IDS843 distributes anapplication by the name of ARISEasySCOR. This product belongs to theARIS family of products, which servethe purpose of Business Process Designin the general sense. According to IDS, Business ProcessDesign means that companies adjusttheir business processes according totheir own requirements and necessities

and to those of the market in three stages.The associated section in the ContinuousImprovement cycle is comprised of thethree aspects design, analysis, andoptimization. The process design, i.e.,the graphical representation of existingprocedures, answers the questions as towho does what and in which order,which services are provided, and whichsoftware systems are assigned for thispurpose.844

The next stage is concerned with

the analysis and appraisal of the actualprocesses. During this, weaknesses inthe procedures are revealed andimprovement potentials are exploited.The adjoining derivation of To-Be

processes, i.e., the ways and means bywhich these are to support the companywith value generation in the future, isbased upon the results of the prioranalysis and rounds off this approach.845

ARIS EasySCOR especially focusesupon the Supply Chain field and containsthe following functionalities:846

Design of the company’s processesDesign of dynamic businessprocess modelsImplementation of simulations withregards to changes in the SC designContinuous monitoring and, ifnecessary, adjustment (redesign) ofthe business processes

Integration of automated decisionprocesses into the existing(subjective) procedures fordecision making.

EasySCOR therefore represents an

application for the analysis, design andcontinuous redesign of Supply Chains. Itcombines a company’s process design,which may be already present withinARIS, with the SCOR model. Thecombination resulting from this issupposed to enable efficient design,analysis, and optimization of SCprocesses in accordance with the SCORmodel and the standards defined by theSCC. The application ARIS EasySCORcomprises process definitions, best

practices, and performance indicatorsfor the SCOR main processes Plan,Source, Make, Deliver, and Return.847

EasySCOR is supposed to simplify andaccelerate the identification ofbottlenecks, weaknesses andimprovement potentials within theSupply Chain by use of predefined andstandardized elements.

Beyond this, it allows thecomparison of a company’s specific SCprocesses and their performancecapability with those of competitorsbased on the SCOR performanceindicators and within the framework ofbenchmarking. Furthermore, existingprocesses can be defined and

documented in an easy way, and mostvarious scenarios of SC models can besimulated and investigated beforechanges are undertaken within theframework of a required SC redesign.848

The following illustration gives aninsight into the application. The strongintegration with SCOR is visuallyreflected by the model’s knownconstitutional elements (processelements, performance indicators, and soon).

Version 6.0 of ARIS EasySCOR isconsistent with Version 6.0 of the SCORmodel. It is already being assigned byseveral previously named organizations,as for example Intel and the US

Department of Defense (DoD).850

5.3.2.3 ADOLog by BOCThe SW provider BOC851 has developedanother SCDM application by the nameo f ADOLog. This application is basedupon the SCOR model and is consistentwith the processes and performanceindicators contained therein. It offerssupport during strategic planning anddecision processes with the frameworkof Supply Chain Management. ADOLogis supposed to offer support for theSupply Chain manager and theemployees in the SCM field, in order toachieve the desired profitability andperformance targets more efficiently.The modeling of the SC processes upon

the basis of the SCOR model should, inconnection with this, serve as a basis forSupply Chain optimization.

The description and representationof the SC processes present in theapplication should enable the processintegration and the undertaking of so-called transversal analyses852 from thepoint of view of each individualcompany. These analyses aim to abolishthe separation of individual businessareas. During this, a connection iscreated with the documented processeswithin the SCOR model that thereforefunction as reference processes.853

Because the modeling methodimplemented in ADOLog is based

completely upon the SCOR model, theapplication also contains all conceptsdeveloped by the SCC in order to be inaccordance with SCOR and thecorresponding descriptions andevaluation standards for Supply Chains.

Diag. 5-1: Representative example from ARISEasySCOR849

It is presumed that SCOR

represents the best option of undertakinga thorough investigation (audit) of theSupply Chain, even if some companiespossess their own developedapplications for the evaluation ofinternal processes.

In conjunction with this, alladvantages of the SCOR model are used,for example, a standard terminology,company-spanning Supply Chaindescriptions, predefined performanceindicators as a basis for benchmarking,and best practices as an orientationguide. Building upon this, ADOLogshould corroborate decisions pertinentto the Supply Chain by facts and

numbers, identify deviations from theperformance metrics strived for, andenable adjustments to the SC designresulting from this.854 ADOLog ishighlighted by the following fourprogress stages.855

1. Geographic Product Flow: Locations

and infrastructure:The so-called Geographic

Product Flow (GPF) models servethe purpose of regional-geographicalillustration of locations andinfrastructure. The user has a numberof plans available for this purpose,with the aid of which the physicalpositioning and the distance ofindividual locations can be visualized

and illustrated. As far as locations inthe GPF model are concerned,production plants and procurementand distribution activities are ofinterest. Material and informationflows can also be visualized. Inaddition to this, the specification ofthe resources necessary for individualmaterial flows (methods of transport)is supported.

2. SCOR Level II: Configuration:

In this case, the physicalillustration of the Supply Chain istransposed from the GPF model intothe SCOR model’s process-orientatedterminology, i.e., transferral into theSCOR standard processes by means

of the known process categories takesplace. Products thereby go through aseries of ideal and typical SCORbasic processes (Source, Make andDeliver), as well as variousalternative editions of the SupplyChain – from make-to-stock right upto make-to-order. The depth of themodeling depends upon theimportance of the formerly andlatterly situated production stages. Inthis way, continual Supply Chains canbe modeled (i.e., from supplier’ssupplier to customer’s customer).

3. SCOR Level III: Decomposition:

Here the results gained in LevelII are broken down into single,

standardized process elements. Eachprocess category contains a referenceto a particular Level III model.

The process elements represent SCOR-

defined standard processes, whichconcretely describe Supply Chainactivities. Input and outputinformation, performance indicatorsand best practices are defined foreach process element.

4. Level IV: Processes and organization:Level IV models illustrate the

individual, company-specificprocedures and generate data for theoptimization of SC configuration bymeans of simulation. In conjunctionwith this, Level IV process models

are supposed to enable the detailedillustration of the individual companyprocesses. In this case, each stage ofwork is represented by an activity.The various procedural variants areillustrated and later simulated with theaid of variables and alternativedecisions. Beyond this, Level IVorganizational models represent thenecessary personnel and businessresources. Apart from this, theorganizational model comprisesbusiness resources, such as ITinfrastructure and productionequipment.

5.3.2.4 Recapitulating observation of

the SCDM applicationsConventional applications only supportdecision making by means of simple,static business rules, which areembedded into the companies’procedures. The majority of decisions,as well as the fundamental businessprocesses, still require expertise andspecialized knowledge, which includesthe continuously changing environmentalvariables. For this reason, many of theevents associated with this cannot beautomated at present, which results inthe fact that organizations’ decisions stillprimarily lie with respective expertsand, at least in a partial degree, areconsequently of a subjective nature.

In order to ensure an automatedexecution within the context of SCDM,the associated applications must enabletransparency with regards to the rulesfor business operations processing,dynamic modification, scenario planningand continuous improvement. Existingapplications, like ERP systems and APSapplications for example, are at presentnot in a position to provide the requiredfunctionality.856 The SCDM applicationsdescribed go beyond this and have themain objective of continuously analysingand improving business processes, ormore exactly: Supply Chain processes.As a result of this, they should supportthe SCOR model-based Supply Chaincompetences – customer-facing

performance capability and internal-f a c i ng efficiency – and thereforecontribute towards an improvement ofperformance.857 Schäfer and Seibtcollectively describe the correspondingsituation as follows:

A decisive factor for futurecompany success will be thecompetence to be able tomanufacture top quality,innovative products atmarketable prices and fasterthan the competition. In orderto realize this, companyprocesses must becontinuously improved andformed more effectively and

efficiently by the integration ofnew, innovative ideas.858

The SCDM applications, as well as

the underlying ASC, will be examined atthe end of Chapter 6 within theframework of suggestions andpossibilities for further research. Now,however, again focus needs to be placedupon the SCOR model, and its presentlimitations discussed.

Chapter Six

Limitations of thepresently available

SCOR model “Organizations today face multipleenvironmental forces affecting theirsurvival, growth, and success. In such acomplex reality it is desirable to see anorganization as a social, technological,economic, and human system. In thiscontext, it is important to see theinterrelationship of individual, group,and organization development processes

as needed for organization renewal.Renewal, in this context, impliespurposeful and planned change.”859

6.1 Observation of theFormation Dimensions ofOrganization and Personnelin the Submitted ContextThe general weaknesses and limitationsof the SCOR model have been addressedin Chapter 2.860 Chapter 5 discussedpotential areas for improvement asidentified in the results of the empiricalexamination.861 A comparison of thegeneral limitations and the potentialimprovements shows that the functionalareas given as preconditioned by the

Supply-Chain Council – the respectiveemployee or personnel areas of QualityAssurance and Training862 – did notproduce any indicators worth mentioningwith regards to the examination results.

This is self-explanatory to the pointthat the quantitative questionnairedeveloped for the primary data surveydid not – in keeping with the SCORmodel – deal closely with thesefunctional areas. The issue of qualityassurance was, however, implicitlyquestioned by means of performancemeasures, as for example damagedshipments and customer disputes.863

Having said this, the area of employee

and personnel matters (HumanResources, HR)864 was not dealt with atall. The same applies to the issue oftraining which, on account of itsassociation to personnel development,can be considered as a component of thepersonnel area.865 An attempt thereforeneeds to be made to define the context ofthis case more clearly. For this purpose,the organization is determined by meansof the following framework.866

According to Leavitt, an organization isconstituted by four independent systemvariables:867

1 . Strategy/task: This means the

preparation of goods and services,inclusive of all associated operational

“sub-tasks.”2 . People/actors: In the main, the

observation of persons or actors andtheir respective behaviour falls withinthis category.

3 . Structure: Communication systems,role models and work procedures.

4 . Technology: This is taken to meaninventions for the immediate solutionof problems – for example,technologies for the measurement ofwork performance or computers.

These variables are often used to

categorize differing approaches toorganizational change. Whilsttechnology-, organizational- andstructural-based approaches mainly

focus upon mechanisms to solveproblems, the personnel-based approachconcentrates above all uponorganizational change. Ultimately,however, all the approaches mentionedseek the improvement of theperformance capability of partialareas.868

The personnel-based approaches

assume that a change in the organizationpredetermines a change in the behaviourof the members of the organization, i.e.,the actors, and therefore attribute aspecial importance to the peoplevariable.869 In conjunction with this, twomain trains of thought may beidentified:870

Influencing and changing thebehaviour of the actors themselves:Guest871 and O’Shaughnessy872 canbe named as advocates of thisschool of thought, as they view thestyle of leadership as the focalpoint. In contrast, Lawrence andLorsch focus on the interactionbetween the actors, the organizationand the company’s environment.873

Issuing “suitable” employees withpermission to give instructions inorder to implement changes(Power-Equalization Theory):874

Group behaviour also falls withinthis category (changing groups), asportrayed for example by Lippitt.875

On the other hand, there are those(like Likert876 or Litterer877) whoobserve the changes to theorganization by looking at theemployees issued with permissionto give instructions.

There is not sufficient space here to

discuss in depth the differing approachesto organizational development.878 Itshould be noted, though, that the systemvariable people represents a substantialfactor during the implementation oforganizational changes.879 As opposed

to the others, however, this variable hasnot been closely analyzed for theaforementioned reasons. If one transfersthe system variables onto the SupplyChain area, Supply Chain Managementis made up of five elements oforganizational design:880

1 . Strategy development: This is to

enable the conception of a SupplyChain based upon company objectivesand market requirements.881

2. Process formation: Here the tasks aredescribed that are necessary for theprocedures and management of theSupply Chain. Included in this are therelationships between the processesand the respective best practices.

3. Performance measurement: In orderto measure, assess and monitor theSupply Chain’s performancecapability and efficiency, a balancedselection of process-relatedperformance indicators is required.882

4 . Organizational model: Thiscomprehends the description of theorganization’s structure, theresponsibility of the departments, aswell as the tasks and jurisdiction ofthe individual employees. Themanagement of Human Resources alsofalls within this category.883

5 . Technology formation: IntegratedInformation Systems (IS) represent anecessary aid for the planning andimplementation of Supply Chain

processes.884

It is apparent that the aforesaid

concept by Leavitt, in addition to thesystem variables contained within it,also applies to the management of theSupply Chain. This is consistent with thepresumption that Supply ChainManagement represents a partial area ofcompany management and, as a result,must be adjusted to suit superiorcorporate objectives.885

The special importance of the

“people” factor in correlation with theconcept of E-Business – a field that alsorepresents, as indicated, the frameworkof reference for Supply Chain

Management, and therefore representsthe application of SCOR as well886 –was proven by Schäfer in an empiricalexamination. More than three quarters ofthe companies questioned agreed uponthe fact that the “human” factorrepresents the largest challenge forsuccessful E-Business. Apart from this,it became apparent that potential benefitscannot be fully exploited without asystematic accumulation of knowledge inthe form of qualifying measures.887

Kuglin and Rosenbaum accentuate thepeople-orientated variable by explicitlypostulating its alignment to the othersystem variables:

“People are the key to success

in any organization. Therefore,it is essential thatorganizational structures andperformance metrics beestablished so that everyone isworking together to achievethe overall strategic goals ofthe company.”888

This view inevitably highlights

limitations to the application of SCOR.As the “human factor” is not includedexplicitly in the SCOR model’sstructure, no performance indicators areear-marked to enable the measurement ofthis particular performance capability.As a result, only limited improvementpotentials can be identified in this

direction. Finally, no best practices arecontained within the model descriptionthat could contribute to processimprovements. Given the focal point ofthis study, the following respective areasof observation or dimensions of SupplyChain formation arise: 1 . Strategy/task: Supply Chain

expression2 . Processes and Organizational

structure: Supply Chain referencemodel

3. Technology: Supporting Supply Chainapplications

4. People: The human resources for the

execution of all activities pertinent tothe Supply Chain, as well as their

influence within the framework of thechange process (renewal).889

The illustration that follows

represents an attempt to place theobserved approaches and proceduresinto a frame of reference under theaforementioned focal point. No claim toentirety is made in this case; it ismoreover the provision of an overview.The illustrated time stream purely servesto provide a rough orientation; flowingtransitions de facto often exist. Thearrow between the system variablespeople and structure is deliberatelyshown in form of a dashed line in orderto graphically represent the describedproblem.

The structural dimensions shown on

the left hand side of the illustrationrepresent, in a transposed sense, theorganizational transition. The right handside describes the concrete structuring ofthe changes associated with it, i.e., theways and means of the concretemanifestation of the change process.From this, it also becomes apparent whythe time stream mainly applies to theright hand side: Change Managementdetermines a permanent transitionprocess.891

Diag. 6-1: Dimensions of the formation in the context

of the Supply Chain890

6.2 Consequences of thePeripheral Conditions andErrors in Own WorkWhat follows is an attempt to findpossible answers to the followingquestions:

Which respective events orconditions had a restrictive ordisadvantageous effect during theexecution of the study?How can the influence of theaforementioned points becharacterized with regards to thestudy, and what steps were taken tominimize the occurrence ofnegative influences?What could future researchers lookto do better or differently?

Unfortunately, despite intensive

literature research, no previous studiescould be found that both contained anempirical examination of the SCORmodel and would satisfy scientific

requirements. In the literature reviewedit was either the case that generally heldoptimization potentials founded upon thevalue of experience were proven,892 orthere were respective studies or projectsthat applied the SCOR model or builtupon it.893 One certain reason for this isthat the SCOR model originated fromcompany practice, rather than from thescientific or academic environment.894

As a consequence of that, this study

entered new territory, so to speak, andwas therefore obliged to assume anexploratory nature.895 Amongst otherthings, a large amount of contemporaryliterature was enlisted and evaluated(both from academic research and from

company practice) in order to take intoaccount the balance and pluralityrequired by scientific aspects.

Furthermore, consideration must begiven to the fact that in the case of theSCOR model, we are dealing with arelatively new concept that was onlyinitiated within the last decade.896 Thismay be the reason why, despiteconstantly increasing membershipnumbers, the model is still notuniversally present in science andpractice.897 The 2002 study by Göpfertand Neher is significant here, as 85percent of the firms questioned by theauthors stated that they did not use theSCOR model or did not plan to assign it

in the future.898 Although it must benoted that this study was directedparticularly at the German market, thepercentage is still high.

The examples described in Chapter2 from the American and Asian regionsshow that development there hasprogressed substantially further. Apartfrom this, it can definitely be assumedthat the SCOR model’s importance anddiffusion will increase considerably inEurope, and in particular in Germany,within the next few years.899 Withregards to those influences that affect thecontext of the empirical examination,reference is made to the detailedexplanations in Chapter 4.900 During the

resulting measurement of the empiricalexamination’s contribution with therestrictions and insufficiencies,consideration must be given to the factthat it is only the first stage towards ascientific establishment within thecontext of an exploratory approach. Thesubmitted work cannot be generalized toapply to the industrial or logisticsituation in Europe or even in NorthAmerica or Asia. Similarly, it in no wayclaims to deliver a conclusive empiricalappraisal of the SCOR model and itssuitability for Supply Chain analysis. Onthe contrary, this study stands upon itsclaim as the basis for further advancedstudies, as should be emphasized by thesuggested examples in the following

section.

The method of data acquisition inthe form of secondary research, aschosen within the framework of thework, inevitably conceals thedisadvantage of a time delay. Analternative would have been to proceedwith a primary survey. In this case,however, the research-economicalaspect must be considered, especially asdata was included from companies in theEuropean, North American and Asianregions. Apart from this, the influence ofa time delay should only have arestricted effect upon the quality of theexamination results: Although the SCORmodel finds itself in a continuous

development process, the basicassumptions on the part of the model aremainly fundamental in nature, and areonly submitted to changes within a verylimited scope. The aspect of changeapplies primarily to furtherdevelopments and improvements, as forexample the fully implemented inclusionof E-Business in Version 6.0 or apotential innovation in the model’scompany-spanning aspects that is sought,yet still outstanding.901 Therecommendation for optimization withregards to an explicit inclusion ofmarketing and sales into future modelversions would fall within thissphere.902

6.3 Suggestions for FurtherResearch in the Fields ofSupply Chain Managementand SCORThis study seeks to provide a helpfulinsight into the subject matter of SupplyChain Management in general and theSCOR model in particular. Given thecomplexity, scope, and ever-changingnature of the field, however, a number ofpoints inevitably remain unanswered.These can be more closely addressed infuture studies. One of the postulates inthe context of empirical research is thata theory should, amongst other things,provide an indication of gaps in presentknowledge.903 This study stands, then, as

an initial and exploratory contribution tothe development of such a theory.

Upon the basis of the knowledgegained within the framework of theempirical examination – the presentdevelopments introduced in Chapter 5,and the restrictions discussed in section6.1 – an attempt will here be made toextricate continuative researchsuggestions. During this, reference willbe made to existing research, and anattempt will be made to derive aconcrete example of a theoreticallyfounded empirical and adjoiningresearch project.

By enrolling the possible incentivesstated, and also incentives going beyond

them for future research in this field,further steps can be taken to build uponand extend the SCOR model’s scientificnature above and beyond this study, andto continue to reduce the uncertaintiesassociated with it. 6.3.1 Extensive researchsuggestions

1. Examination design and thesismodel:• The investigation of the SCOR

model should be repeated in asimilar way in order to validate theresults found. A time relatedcomponent is seen as rathermarginal, as the fundamental

validity of the model may only berestrictedly submitted to temporalchanges. Further developments ofthe model represent a restriction tothis, as they take place with everynew version of the model.

• Continuative examinations couldalso differentiate betweenparticular criteria, such as companysize or region.904 A substantialcondition for the consideration ofpossible discriminating factors isrepresented by the presence of arespective data basis or asufficiently high sample size.905

• In the present case, it was not

possible to achieve a moreunequivocal model adaptation bymeans of the additional variables.Certain indicators, in the sense ofincremental information, werepresent for the fact that the SCORmodel’s illustration particularlycorresponded to the situation oflarger (and partially moresuccessful) companies.906 In thiscase, however, the results gathereddid not seem sufficient togeneralize this statement.Continuous empirical work wouldseem to be necessary.

• A number of theses showedthemselves to be unsystematic uponthe basis of the results. For several

of them, it was only possible toformulate a rudimentarilyexplanation, as explained in detailin Chapter 4.907 It would, in anycase, be interesting to investigatethe respective constellations moreintensively in future studies, andextract explanations for what are atpresent inconclusive diagnosticsituations.

2. Modern concepts and tools for

Supply Chain formation:• It remains to be seen whether the

concept of the Adaptive SupplyChain (ASC) establishes itself, orwhether it is prematurelysuperseded by other concepts. At

present, a multitude of articles anddissertations exist to address thistheme from a practical as well as ascientific point of view. Theseresult from the possibilitiesprovided by new Information andCommunication Technologies(ICT), for example the internet, bymeans of which the structuralpossibilities of company-internal aswell as company-spanningprocesses were placed upon a newplatform.908 One effect in the fieldof the Supply Chain is the furtherdevelopment into an ASC. Thismust not obscure the fact that, atpresent, scientific studies into theASC concept and its influence upon

a company’s performance abilitiesdo not exist. There is, then, a clearneed for further studies in thisfield.909

• The above point also applies toSupply Chain Design Management(SCDM) and the associatedapplications. Although the conceptis classified as very relevant, thereis at present little scientific proofas to the extent of its influence uponthe performance indicators. Thetruly substantial applications in thiscontext are, without exception,based upon the SCOR model.

• Further leading studies could

therefore build upon the resultsfound within the framework of thework submitted, and contribute tothe provision of objective andcomparable measures of theinfluence of SCDM applicationsupon corporate success.910

• Finally, it would be of interest toinvestigate the influence of SCDMupon other E-Business concepts, asfor example Customer RelationshipManagement (CRM) or ElectronicProcurement.911

3. Expansion of the SCOR model

structure:912

• The importance of the “HumanFactor” within the framework of a

total observation of organizationalsystem variables has beendiscussed in detail. This hashighlighted the case for theinclusion of this factor into acomprehensive concept of SupplyChain Management in general, andthe SCOR model structure inparticular.913

• The additional performanceindicators associated with the newsystem variables would also haveto be included into an extendedSupply Chain Scorecard (in thesense of an approach including the“Human Factor” for themeasurement of Supply Chainperformance), in order to make a

continuous monitoring of this newperformance area possible.914 Froma performance indicator-specificpoint of view, it would be practicalto enlist the Balanced Scorecard(BSC), as this contains a speciallearning and growth perspective.915

Due to its similarity in content tothe SCOR model (with reference tothe performance indicator-specificSCOR model observation),916 theBSC seems predestined to serve asa basis for the inclusion ofpersonnel into organizationalperformance measurement.917 Iteven explicitly preconditions theinclusion of the employees andtherefore the “Human Factor” for

successful assignment.918 Anextended Supply Chain SCORCardresulting from this could be usedfor the progress monitoring ofChange Management, because thiscan be considered to be a specialcharacteristic of the “HumanFactor.”919

• The SCOR model comprises, as hasbeen indicated, non-monetarymeasures with regards to theassociated performanceindicators.920 The disadvantagesconnected with this, as for instancethe problem of aggregating non-monetary indicators, could beconfronted by an expansion of theSCOR model structure or the

respective inclusion of financialperformance terms. For theaforesaid reasons, the closestapproach would seem to lie in theorientation upon the BSC.

• The result would be a Supply ChainSCORCard expanded by onefinancial entity that would forexample enable the creation of areference between theimprovements sought upon a SCORmodel-based Supply Chainanalysis, and the gains achieved.921

No referential results exist, atpresent, with regards to the suggested

expansion of the SCOR model structure.If the validity and effects of thisextended model structure are to beassessed, further research in the field isneeded as a matter of some urgency. 6.3.2 Example of atheoretically foundedempirical research projectas a possibility for adjoiningresearchAn attempt is made here to give aconcrete example of a future empiricalresearch project. As a basis for this, theexamination results accumulated withinthe framework of the empirical studywill be called-upon, as will

developments in company practice andbusiness-economical research. This willenable the study to justify its claim tohaving exploratively contributed to astep-by-step accumulation of knowledge,which can then be continued by furtherfocused investigations.922

Primarily, the application of

structure-analytical procedures serves toinvestigate a thesis model, understood asa system of hypotheses. In the presentcase, however, the necessary proceduralconditions were not sufficientlyavailable.923 It would therefore appearappropriate to allow these procedures tobe assigned within a study building upona model. A decisive factor in

conjunction with this is the availabilityof a data basis as large as possible (witha sample size of at least 150 or more).To what extent this is achievable from aresearch-economical perspective mustbe concluded individually for each case.Furthermore, it must be taken intoconsideration that structural equationmodels, in addition to statistical criteria,require certain content from the datamaterial being analyzed.924

The structural equation model to be

investigated is directly derived from thethesis model. A substantial difference is,however, present from the developmentof a structural equation model sought inChapter 4: The latter-named model set

out to validate the results of theinferential – statistical examination. Ittherefore relied upon Meta theses, andthe hypothetical constructs were derivedaccordingly. The consequence was thatone was dealing with a “bottom-up”approach, so to speak.925

Contrary to this, and within the

framework of the submitted suggestion,this should be a superior point of viewand the thesis model observed in itsentirety. For this reason, a “top-down”three-stage correlation of hypotheticalconstructs is given: Firstly, the (notdirectly measurable) PerformanceAttributes are mapped to the (also notimmediately measurable) Supply Chain

competences. Then, the (not directlymeasurable) Metrics Level 1 aremapped to the Performance Attributes.Finally, in the third stage, a mapping ofthe (measurable) Performance Measuresto the Level 1 metrics takes place asovert indicators. The following diagramrepresents the level of the hypotheticalconstructs for reasons of graphicalrepresentation and legibility. In theillustration following this, the mappingof the Level 1 metrics (as the level ofhypothetical constructs) to theperformance measures (as the overtindicator level) is represented.926

Diag. 6-2a: Research suggestion for a performance

indicator-based SCOR model represented in astructure-analytical form (Part 1)927

Diag. 6-2b: Research suggestion for a performance

indicator-based SCOR model represented in astructure-analytical form (Part 2)928

Table 6-1: Legend to Diag. 6-2b: Index of the applied

performance metrics929

Index Performance Measure

DP-1 Customer retention rate

DP-2 Backorders value

DP-3 On-time delivery percentage (inbound andoutbound)

FR-1 Percentage of purchased orders received on timeand complete

FR-2 Percentage of purchased lines received on time andcomplete

FR-3 Average MPS plant delivery performance (workorders)

FR-4 Cycle count accuracy percentage

FR-5 On-time delivery percentage (inbound andoutbound)

POF-1 Perfect orders rate

POF-2 Lines on-time fill rate

POF-3 Customer retention rate

OFL-1 Average purchase requisition to delivery cycle time

OFL-2 Transactions processed via web/EDI

OFL-3 Average manufacturing cycle time

OFL-4 Percentage of sales via web

RT-1 Backorders value

RT-2 On-time delivery percentage (inbound andoutbound)

RT-3 Lines on-time fill rate

PF-1 Inventory stockout percentage

PF-2 Average MPS plant delivery performance (workorders)

TSC-1

Inventory management cost as a percentage ofrevenue

TSC-2

Inventory management cost as a percentage ofinventory value

TSC-3

Inventory obsolescence cost as a percentage ofrevenue

TSC-4 Percentage of inbound or outbound cost

TSC-5 Inventory management cost per customer order

TSC-6 Percentage of inventory in transit

TSC-7

Transportation cost per mile (inbound andoutbound)

TSC- Transportation cost as a percentage of revenue

8TSC-9 Inbound transportation cost per supplier order

TSC-10 Outbound transportation cost per customer order

TSC-11 Premium freight charges

COG-1 Purchasing cost per purchase order

COG-2 Purchasing cost as a percentage of revenue

VAP-3 Percentage of purchases from certified suppliers

VAP-4 Manufacturing cost per FTE

VAP-5 Average first-pass yield rate

VAP-6 Scrap/rework cost as a percentage of revenues

VAP-7 Average throughput per FTE

VAP- Average machine availability rate

8VAP-9 Average number of customers orders per FTE

VAP-10 Customer service cost per FTE

VAP-11 Inventory management cost per FTE

VAP-12

Transportation cost per FTE

WC-1 Damaged shipments

WC-2 Customer disputes

CTC-1 Average received finished goods turnaround time

CTC-2 Inactive inventory percentage

DOS-1 Average order-to-shipment lead time

DOS-2 Average inventory turnover

AT-1 Average operating-equipment efficiency rate (OEE)

AT-2 Average plant capacity utilization

AT-3 Average warehousing space utilization

A modification introduced in

SCOR model Version 7.0 and continuedin Version 8.0 would enable, beyondthis, a (simplified) variant of theexamination design. The new versionsrepresent an initial attempt to makecalculation operations available, whichshould make the calculation of the Level1 metrics possible.930 These metricswill naturally still not be immediatelymeasurable, because they are ratioindexes.931 They could, however, beidentified as indicators in the structure-analytical analysis, as numerical valueswould already be present for them. Inthis case, one level of hypotheticalstructures would become obsolete. As a

result, it would be possible to performan additional comparison of the above-represented model with the alternativemodel, which would allow additionaland enhanced conclusions as to theexamined illustration of the SCORmodel’s structure. 6.4 Balance BetweenStandardization andIndividualizationIn the meantime, many companies haveintroduced the SCOR model intooperational planning and businesspractice. However, the expectationsfrom the SCOR model must not be settoo high, as it is neither a “panacea” nor

an end in itself. It is not intended to bethe sole means of realizing effectivemonitoring of the Supply Chain. Theapplication of SCOR by no meansreplaces management expertise.932

The SCOR model’s application for

the standardization of processes andstructures often also has an ambivalentcharacter. On the one hand, a number ofcompanies see a great potential foroptimization in standardization, asexpressed in the following interpretedquote.933

The largest potential withrespect to businessimprovements and results lies

in the harmonization,improvement andstandardization ofprocesses.934

On the other hand, however, thestandardization of structures andprocesses can mean that some strengths,which resulted directly from theprevious differences, become lost.935 Inthis case, the model’s application wouldnot contribute to the achievement ofcompetitive advantages. In an extremecase, it could actually lead tocompetitive disadvantages.936 Thus, acompany must find a suitable balancebetween standardization andindividualization in order to be able to

supportively improve and expand itscompetitive position.937

In a survey conducted in the year

2001 amongst so-called DAX-Companies938 to establish the value ofE-Business concepts and correspondingactivities within corporations, Schäfercame to the conclusion that E-Businesswas seen as being particularly relevant.In that case, it must be taken intoconsideration that Supply ChainManagement, and as a result also SCOR,fall within the E-Business frame ofreference.939 The activities going on inthis field were, however, comparativelylow-key at the time of this study, andcould certainly be strengthened and

given greater priority.

Furthermore, although an importantvalue was assigned to the concept ofcompany-spanning Supply ChainManagement, an evident backlog existed.In this case, it was the concrete necessityfor detailed information and assistancein addition to supporting standards andinterfaces required by companies inorder to introduce company-spanningconcepts. The increasing difficultyregarding the unequivocal“demarcation” of company boundarieswas pointed out in this case. Therequirement for adequate measures on aninformation technology andorganizational level was consequently

highlighted, which enables companies toflexibly arrange and integrate inter-organizational SC processes.940

Based upon the knowledge

accumulated, the SCOR model asintroduced within the course of the workrepresents a good aid for the analysisand optimization of Supply Chains bypresent day standards. Apart from this, itoffers standards for company-spanningprocess description and performancecomparison. It has, as indicated, clearadvantages, but does not represent aform of “universal recommendation.”941

Advanced approaches to the realizationand support of Adaptive Supply Chains(ASC), similar to the introduced Supply

Chain Design Management (SCDM)and the applications available for it,indicate possible ways in which futureSupply Chain models will be able tocontinuously adapt to marketrequirements. At present, due to thenovelty of the subject matter, there is alack of sufficient practical experienceand scientifically founded examinationsto produce a conclusive study of those.

Seibt collectively expresses therequirements that arise for companies inthe predominantly dynamic competitiveenvironment as follows:

Each organization must learnto adjust itself to a multitudeof flexible and quickly

changing cooperation with anever increasing number ofchanging partners, tocomprehend them as a chance.

Only in this way will itbecome a valuablyparticipating cooperationpartner, i.e., in the generationof value which brings dynamicchanges with it.942

This statement is particularly

noteworthy with regards to the presentsubject matter, and unequivocallyaffirms the need for further work in thefield of Adaptive Supply Chains and theprocesses and tools necessary for their

management. Such a field is not onlyguided by the need to generate value, butis concerned with continuouslyredefining this value throughout thewhole Supply Chain.943 The finalobjective is to manage an evolutionaryprocess, which enables a “balancing”between the (external) customerrequirements on the one side, and the(company-internal) competences or,more exactly in the present case, the SCcompetences on the other side.944

The SCOR model can make a

valuable contribution to the continuousanalysis and necessary optimization ofthe Supply Chain built upon it. This isreflected, amongst other things, by its

constantly rising usage. However, in itspresent formation it has clear limitationsas to its assignment possibilities withrespect to the Supply Chain’s design andredesign.

It has been shown that the SCORmodel initially originated from therequirement to create a universallanguage for the Supply Chain’sdescription in the sense of a“standardization of the SCnomenclature.” It therefore represents adescriptive model, and in its originalcondition does not claim to possess astructuring character.945

This, of course, does not mean that

incentives and suggested improvementscan and should arise from it. Theirimplementation must just be seen asexternal to the SCOR model’sapplication.946 This correlation is notalways made sufficiently clear bycompanies and consultancies.947 Thisstudy has highlighted the difference asmuch as possible, and deliberatelyrefrained from an appraisal of theimplementation successes in conjunctionwith formation measures.

Modern approaches with respect tothe further improvement of the model arevery promising and have the primaryintention of developing the modelfurther, namely into a combined

descriptive and structural model.948

However, firm proof of their applicationability and, furthermore, of theirquantifiable success is still absent.Hence, they require an expansion of theirapplication in corporate practice, aswell as further scientific foundation. Acontinuous improvement of the SCORmodel and its application possibilitiescould usefully be produced by a closecooperation between corporate practiceand academic research. It would also bea very valuable contribution to thecontinuous improvement and advancedapplication of the SCOR model.

References 1. (Latham, 1999, p. 91). The author quotes Gerry

Perez, Senior Vice President – TechnicalOperations of Boehringer-Ingelheim, Inc., theAmerican subsidiary of the companyBoehringer-Ingelheim (headquartered inDarmstadt, Germany).

2. The period between 1995 and 2007 is referred tohere.

3. For the usage of the term Supply Chain, cp., forexample, Werner (2002, p. 28). The termlogistical chain or logistics chain is alsosometimes found. The difference lies in the factthat the focal point of the logistics chain extendsto cover the (physical) activities in a narrowersense. In addition to this the Supply Chain alsocovers the accompanying cash and informationflows and has a substantially wider conception,cp., for example, Thaler (2003, p. 43) and vonSteinäcker and Kühner (2001, p. 45). The termsintroduced and their meanings will be dealt within more detail in the course of the submitted

work.4. For discussion on Supply Chain Management as a

management discipline in the context of businesseconomics, see the respective explanations insect. 1.2.

5. Cp. Supply & Demand-Chain (2005a, 2005b). Apersonal website and a respective magazineexist for executives from the field of SupplyChain Management with the title Chief SupplyChain Officer (CSCO) – Insights for theSupply Chain Executive, cp. CSCO (2005).

6. A method or a procedure is used in order to getfrom a defined starting condition to a definedend condition. In opposition to this, with a modelwe have an illustration of a defined startingstructure seen from certain points of view.Models are each designed for particular questionor problem conditions; they are moulded by thebasis forming question (i.e., by the uses requiredof the model) (cp. Kromrey, 2002, p. 204).

7. With reference to the terms Non-Profit Organisationand Not-for-Profit Organisation, cp., roughly,Kotler and Bliemel (1992, pp. 30, 42).

8. The respective terms or organizations (SCORmodel, SCC and so on) will be explained in moredetail at a later point in time.

9. Cp., for example, Bolstorff and Rosenbaum (2003,p. 1).

10. Cp., for example, Huan, Sheoran, and Wang (2004,p. 23), Lambert and Pohlen (2001, p. 1),Lockamy and McCormack (2004, p. 1192), andGardner and Cooper (2003, p. 37).

11. For the practical applications of the SCOR model,see chap. 2, sect. 2.4; for assignment within theframework of scientific examinations see chap.6, sect. 6.2.

12. For the discussion on an explorative advance toapproaching a research area, cp., for example,Wollnik (1977). For a respective example ofapplication, cp., roughly, Schäfer (2002).

13. This will be more closely dealt with in chap. 4,sect. 4.1.

14. This problematic will be dealt with more explicitlyin chap. 5, sect. 5.1.

15. For the term exploration see the explanations at thebeginning of chap. 3.

16. The subject matter will be dealt with in detail inchap. 3, para. 3.1.1.

17. We are dealing here purely with basic guidelines asto the methodical layout of the work. Detailedexplanations of the processes, testingprocedures and statistically acknowledgedvalues are found in association with thedisclosure of findings (see the explanations inand chap C, sect. 3.4).

18. Cp. Friedrichs (1990, p. 50).19. Modelled upon Friedrichs (1990, p. 51).20. The value generation resulting from the

performance process of businesses iscalculatively identified as revenue minusintermediate inputs; cp., for example, Böckerand Dichtl (1991, p. 169).

21. Cp. BearingPoint (2003a, p. 1).22. For the terms bottleneck and bottleneck monitoring,

cp., roughly, Goldrath and Cox (1992, p. 138)and Heinrich and Betts (2003, p. 14).

23. According to the Balancing Law of Planning(Ausgleichsgesetz der Planung) formulated byGutenberg, the coordinated course of

happenings within a business require thecontinuous mutual synchronization of marketingpossibilities, manufacturing capacities,procurement factors, etc. Resulting from this areareas which change in the course of time, andimpede as bottlenecks the full quantative and/orqualitative development of the other operationalpartial areas due to their entanglement. Forplanning purposes the result is that partial planshave to be aimed at this “minimum sector” viaparticular synchronization measures. Compareto this end, for example, Gutenberg (1979, p.164) and Schierenbeck (2003, p. 129).

24. Cp., for example, Thaler (2003, p. 19).25. The term Service Level can be defined as follows:

“The probability of being able to satisfy anyorder during the normal order cycle, from stockin hand” (Stephenson, 2004).

26. Cp. Kuhn and Hellingrath (2002, p. 45).27. In the past, a number of compositions considered

the basic question as to whether Supply ChainManagement represents a self-containedmanagement discipline in the context of business

economics, or moreover a type of fashionappearance of limited duration; cp. to this endroughly Eßig (1999, p. 106), Kieser (1996, p.21), Müller, Seuring, and Goldbach (2003, p.429), and Otto and Kotzrab (2001, p. 157).

28. In academic literature, there is an increasingrecognition that Supply Chain Managementactually constitutes a self-contained disciplinewithin business economics, which needs to betaken appropriately. Representatively, Ayerscomes to the following conclusion in this context:“SCM is a discipline worthy of a distinct identity.This identity puts it on a level with otherdisciplines such as finance, operations, andmarketing” (Ayers, 2002a, p. 8). For furthercompositions which advocate a similarviewpoint, cp., for example, Bechtel andMulumudi (1996, p. 1), Cooper, Lambert, andPagh (1997, p.1), Göpfert (1999, p. 19), and vonSteinäcker and Kühner (2001, p. 39).

29. In the following, the term Supply Chain and theabbreviation SC will be used synonymously.

30. Background information: “The Council of Logistics

Management (CLM) was originally founded asthe National Council of Physical DistributionManagement (NCPDM) in America’s St. Louis,in January, 1963. The NCPDM was formed bya visionary group of educators, consultants, andmanagers who envisioned the integration oftransportation, warehousing, and inventory asthe future of the discipline. At that time, physicaldistribution was just beginning to edge its wayinto the corporate lexicon and make itsconsiderable presence felt in the businesscommunity. These early founders believed thathigh-level executives within their owncompanies needed to be made aware of thecritical role that physical distribution could andshould play in improving marketing efficiencyand profits. They determined that there was anurgent need for an organization that wouldfacilitate continuing education and theinterchange of ideas in this rapidly growingprofession that came to be known as logisticsmanagement” (CLM, 2004a).

31. The original text reads: “The supply chain is as all

that happens to a product from dirt to dust”(CLM, 2004c).

32. The product life cycle comprises the main stagesfrom product evolution through production, rightup to reutilization. Throughout the life cycleconstant changes and improvements must beimplemented, which only then make a continualsuccess on the market possible; cp., forexample, Wöhe (1984, p. 626).

33. Cp. Ayers (2002a, p. 5) and Lambert and Pohlen(2001, p. 1).

34. Cp. Christopher (1998, p. 4) and Hugos (2003, p.40).

35. Cp. Banfield (1999, p. 3) and Daganzo (2003, p. 1).36. Cp. Chopra and Meindl (2001, p. 1).37. Cp. Lambert, Stock, and Ellram (1998, p. 14).

Aitken defines the Supply Chain in this sense asfollows: “A network of connected andinterdependent organizations mutually andcooperationally working together to control,manage and improve the flow of material andinformation from supplier to end user” (Aitken,1998, p. 19).

38. Cp. Govil and Proth (2002, p. 7) and Poirier (1999,p. 197).

39. Cp., roughly, Kuhn, Hellingrath, and Kloth (1998, p.7).

40. Cp. Jansen and Reising (2001, p. 197) andMarbacher (2001). Christopher advocates asimilar approach on the grounds that today’sSupply Chains are mainly driven by the market(Christopher, 1998, p. 18).

41. In the past the so-called push concept was themost widely spread. With this approach in thevarious stages of the Supply Chain, semi-completed and completed products aremanufactured and stored until such time as theycan be sold and delivered to the next stage in theSupply Chain on the basis of customer orders.Often, long delivery periods and high inventorystock result from this. In opposition to this, thedemand vacuum approach (pull concept) ischaracterized by the fact that the customerdecides to buy a certain product, whereby itquotes the exact requirements with reference tothe product and delivery time. Building upon this

the necessary resource amount is acquired. Theproduction and distribution process has then tolead to a delivery according exactly to thewishes of the customer (quality, time, etc.). Inorder to realize this approach, supply anddemand must be synchronized. Thissynchronization represents one of the tasks andone of the objectives of Supply ChainManagement (SCM), as will still be illustrated indetail in the course of the work; cp. Landvoigtand Nieland (2003, p. 4) and Poirier (2000, p.26).

42. Cp. Hoover, Eloranta, Holmstrom, and Huttunen(2001, p. 70).

43. Cp. Bovet and Martha (2000, p. 17). For the termprocess in the context of the Supply Chain, q.v.(Schönsleben, 2000, p. 22; Thaler, 2003, p. 17).

44. (Stephens, Gustin, & Ayers, 2002, p. 360).45. Cp. Premkumar (2002, p. 368).46. The concept of Collaborative Planning, Forecasting

and Replenishment (CPFR) enables a company-spanning cooperation for the buyers and sellersto make demand and marketing prognoses, as

well as a regular actualization of plans, which isbased upon a dynamic exchange of informationand has reduced supplier stock as its objective;cp., for example, Handfield and Nichols (2002,p. 298) and Schneider and Grünewald (2001, p.198).

47. Cp. Chakravarty (2001, p. 402).48. Cp. Govil and Proth (2002, p. 17).49. The term internet stands as a synonym for a global

association of local, country-specific andregional networks. The impression of a completenetwork is created via connection to networkcenters (backbones) for private, scientific andcommercial users. Due to this, the internet is aglobal-spanning, public net that connectsworldwide institutions, businesses and privateusers to the so-called Transmission ControlProtocol/Internet Protocol (TCP/ IP) viaGateway Servers; cp. Rebstock (2000, p. 6) andSchoder (2004, p. 60).

50. Cp. Kuglin and Rosenbaum (2001, p. 59). For theSupply Chain’s penetration of the internet andthe implications thereof, cp., for example, Coppe

and Duffy (1999, p. 521).51. In this context see the concept of Collaborative

Planning, Forecasting and Replenishment(CPFR) described earlier in this paragraph.

52. The concept of Vendor Managed Inventory (VMI)falls within this category and can be describedas follows: “Traditional responsibilities havechanged. Large retailers obtain more and moresending orders to their suppliers, i.e., thecustomer goods manufacturers. Instead theyinstall consignation stores whose contents areowned by their suppliers until the goods arewithdrawn by the retailer. A supplier isresponsible for filling up his inventory to anextent which is convenient for both the supplierand the retailer. Such an agreement is calledVendor Managed Inventory” (Meyr, Rodhe, andStadler, 2002a, p. 65).

53. Cp. Gensym (2001, p. 2).54. In the framework of a Supply Chain Strategy, the

company’s desired value generation must beclarified. This involves answering questions, asfor example: With which products and services

is the business going to compete? Is a standardproduct of a certain series going to be offered toall customers, or are customer-defined seriesproducts going to be on offer? Which magnitudeof numbers is strived for (few, many)? Is purelya product on offer, or is additional serviceperformance, such as inventory replenishment,also offered? To what extent does the depth ofproduction suffice? Cp. Geimer and Becker(2001a, p. 26).

55. Cp. Christopher (1998, p. 15) and Geimer andBecker (2001a, p. 26). The so-called ValueChain follows a similar approach in thought,already defined by Porter; cp. Kuglin (1998, p.106), Porter (1995, p. 126), and Porter (1999, p.59).

56. (Normann & Ramirez, 2000) Value Chain, p. 18657. This term allows itself to be operationalized in the

following way: “Customer satisfaction is thedegree to which expectations of attributes,customer service, and price have been or areexpected to be met” (Hilton, Maher, & Selto,2004).

58. Profitability in this context is understood to be thecapability of a business to attain a suitable rateof interest for invested capital (Return onInvestment, ROI); cp. Horváth (2001, p. 571),Schierenbeck (2003, pp. 6, 635), and Wild(2004).

59. The so-called Strategic Triangle describes threedecisive factors of competition: cost, time andquality; cp. Thaler (2003, p. 12). The StrategicSquare adds the further factor of flexibility; cp.Werner (2002, p. 10). Due to this, flexibility canbe defined as follows: “Flexibility is defined asthe ability of the process to handle changes in itsenvironment and in the requirements on theprocess” (Seibt, 1997a, p. 22).

60. The term Information Technology (IT) is to bedefined in the submitted work as “(…) denotingthe technologies used for processing, storing,and transporting information in digital form”(Carr, 2003, p. 12). For the term Digitalization,cp., for example, Negroponte (1995) and Rai,Patnayakuni, & Patnayakuni (2005, p. 2).

61. Cp. Bovet and Martha (2000, p. 4). For the term

Value Net, q.v. Andrews and Hahn (1998, p. 7)and Cartwright and Oliver (2000, p. 22).

62. A substantial development within the framework ofthe Supply Chain is the construction of so-calledvirtual networks. A virtual network describesthe temporary melting together of corecompetences (i.e., to be in command of specialcapabilities or success potentials in particularfields of the participating businesses – q.v.Prahalad and Hamel (1990, p. 79). The resultingconstruction represents itself to the customer asone unit. Inwardly, a virtual enterprise possessesno juristic and construction organisationalinterlocking; cp. Aldrich and Sonnenschein(2000, p. 35), Kaluza and Blecker (1999, p. 4),Schäfer (2002, p. 1), and Werner (2002, p. 12).

63. Nickles et al. describe the connection as follows:“Business strategies have traditionally driven ITdevelopment, but IT can now be used to enablenew business strategies.” (Nickles, Müller &Tabacs, 1999, p. 495).

64. Cp. BearingPoint (2004a, p. 2).65. Cp. Forbath and Chin (2000, p. 3) and Rayport and

Sviokla (1995, p. 75).66. For background information on the Dell Company

see chap. 3, para. 3.1.2.2.67. Cp. Dell and Fredmann (1999, p. 101).68. Cp. Seibt (2000, p. 11).69. The starting point of the description and

systemization of E-Commerce is the dimensionof business transactions, as for example markettypes, market services and people acting withinthem; cp. Klein and Szyperski (2004). For theconnection between SCM and E-Commerce,q.v. Drummond (2002, p. 29). For the integrationof E-Commerce within E-Business and for theboundaries of the two terms, q.v., for example,Schäfer (2002, p. 11).

70. Cp., for example, Kämpf and Martino (2004). Forthe term Electronic Supply Chain Management(E-SCM), q.v. Hillek (2001, p. 1) and KPMG(2001, p. 2).

71. For the integration of SCM solutions into theoperational architecture of information systems,cp., roughly, Gronau, Haak, and Noll (2002, p.385) and Kämpf and Roldan (2004).

72. Cp. Ross (2003, p. 18).73. Cp. Ayers (2002c, p. 245).74. Cp. Hughes, Ralf, and Michles (1998, p. 4). For the

direction of Strategy definition andimplementation, q.v. Fuchs, Young, andZweider-McKay (1999, p. 8).

75. The term Competitive Advantage can bedetermined as follows: “The challenge ofcompetitive strategy – whether it is overall low-cost, broad differentiation, best-cost, focusedlow-cost or focused differentiation – is to createa competitive advantage for the firm.Competitive advantage comes from positioning afirm in the marketplace so it has an edge incoping with competitive forces and in attractingbuyers” (Johnson & Strickland, 2004).

76. For the term fixed costs, cp., for example,Schierenbeck (2003, pp. 233, 654) and QM(2004a).

77. The fundamental competitive strategy is themanagement of costs. The common acceptanceof Porter’s strategy alternatives CostManagement (i.e., a strategy that focuses upon

the competitive advantage of lowest costcompared to competition), Differentiation (i.e.,the achievement of at least one of the uniqueperformance attributes) and Concentration (i.e.,concentration upon a market segment ordemand group respectively) means that acorporation can only be successful if itconcentrates upon one of the three fundamentaltypes of strategy and the competitiveadvantages resulting from the strategy.Otherwise the business is in danger of being“stuck in the middle”; cp. to this end, Porter(1995, p. 62) and Porter (1999, p. 19).

78. Cp. Stephens et al., 2002, p. 361). For achievementof competitive advantages via informationtechnology, cp., roughly, Porter and Millar (1988,p. 62).

79. (Christopher, 1998, p. 16). Cp. in this context also(Unknown Author, 2006, p. 14).

80. For the description of Supply Chain Managementas a unique discipline within BusinessEconomics, see the explanations at the end ofsect. 1.2. The fields of application demonstrated

in the further course of the work shouldmoreover highlight the present importance ofSCM for corporate practice.

81. In the further course, the term Supply ChainManagement and the respective abbreviationSCM will be used synonymously.

82. Cp. Evans and Danks (1999, p. 19). Forimplementation of SC strategies, q.v. Easton,Brown, and Armitage (1999, p. 446).

83. Cp. Beech (1999, p. 92).84. Cp. Tyndall, Gopal, Partsch, and Kamauff (1998, p.

65).85. Cp. Raman (1999, p. 171).86. (Hugos, 2003, p. 2).87. For background information on the Council of

Logistics Management (CLM) see sect. 1.3.88. Cp. Novack, Langley, and Rinehard (1995, p. 27).

Cp. also to this end CLM (2004c).89. Cp. Bowersox and Closs (1996, p. 1).90. Cp. to this Novack, Rinehard, and Wells (1992, p.

234) and Simchi-Levi, Kaminsky, and Simchi-Levi (2000, p. 1). A differentiation of theplanning levels can be undertaken as follows:

Operational: short-term (less than one year forthe running accounting or reporting periodrespectively) and mainly applicable to one partof the company or activities respectively.Tactical: mid-term (time horizon 1 to 3 years)and mainly for a larger part of the company oractivities respectively. Strategic: long-term (timehorizon longer than 3 years) and mainlyimpartial, pertaining to the substantial productareas, company activities or the company as awhole, and the aspects critical for success; cp.,roughly, Albach (2001, pp. 294, 329).

91. Logistics Service Providers can be described asfollows: “Third-party logistics involves the use ofexternal companies to perform logisticsfunctions that have traditionally been performedwithin an organization. The functions performedby the third party can encompass the entirelogistics process or selected activities within thatprocess” (Skjoett-Larsen, 2000, p. 112).According to this definition, every relationshipbetween a carrier and a logistic service provideris described as Third-Party Logistics.

92. Cp. CLM (2004b).93. Cp. Kajüter (2002, p. 36).94. Cp. Hellingrath (1999, p. 77). For the terms

effectivity and efficiency in the submittedcontext, see para. 1.7.1.

95. Cp. CLM (2004b).96. Cp. CLM (2004c).97. Cp. Stephens et al. (2002, p. 360). Werner

differentiates between the business-internal andthe business-integrated Supply Chain, wherebythe latter is directed at the intersections of acompany with its external partners; cp. Werner(2002, p. 6).

98. For an overview of various term definitions and aproposal of classifications, cp., for example,Müller et al. (2003, p. 419) and Schäfer (2002,p. 47).

99. (Schönsleben, 2000, p. 152). The term AdvancedPlanning System (APS) can be described asfollows: “An APS typically consists of severalsoftware modules (eventually again comprisingseveral software components), each of themcovering a certain range of planning tasks”

(Rohde, Meyr, & Wagner, 2000, p. 10). Cp. alsoto this end Meyr, Rodhe, and Stadler (2002b, p.99) and Poluha (2001, p. 314).

100. As to the various planning levels, especially in theinformation management areas, cp., forexample, Schoder (2004, p. 42).

101. For the core competence term, q.v., for instance,Prahalad and Hamel (1990, p. 79).

102. Cp. Schönsleben (2000, p. 54). “Lead timeexpresses the amount of time consumed foreach functional transaction. (…) The mainproperties of the lead time of a process are thelength and the predictability or precision of itsexecutions” (Seibt, 1997a, p. 21).

103. Cp. Bowersox and Closs (1996, p. 4).104. Cp. Ayers (2002a, p. 8).105. Cp. Premkumar (2002, p. 368).106. Cp. Kaczmarek and Stüllenberg (2002, p. 275).107. Cp. Handfield and Nichols (2000, p. 2).108. Cp. Ross (1997, p. 9).109. The terms Lean Production or Lean Management

respectively, describe the concept of theincrease of efficiency, often in the sense of

decentralization, production displacement(outsourcing), flat hierarchies, compression ofperformances and therefore less personnel. Thisconcept, as with other variants of Lean-concepts, stems back to an analysis by theJapanese car manufacturers in a study of theMassachusetts Institute of Technology (MIT) atthe end of the 1980’s (for backgroundinformation about the MIT, cp. the explanationsin para. 1.7.1). According to this, the Japanesecar manufacturers produced twice as efficientlyand flexibly as the European and Americancompetition, with simultaneously meaningfulbetter quality. The comparison with “lean” in thesense of decentralization, outsourcing, shallowerhierarchies, performance compression andtherefore less personnel is a simplification of theJapanese concept, which represents embracivequality management (Total QualityManagement, TQM) and only achieves theefficiency and flexibility advantages on the basisof this embracing concept, which are describedas externally visible and are attributed to

organizational changes in the “lean” sense.Differentiated interpretations comprise thereforethe substantial elements of quality management,however there are no unified meanings for thevarious concepts connected with “lean”; cp.Pollalis (2002, p. 333) and Thaler (2003, p. 113).

110. Distribution represents a partial area of the ordermanagement process, which focuses upon thefinal delivery to the customer. “For delivery, ordistribution, the products are issued from stock(commissioning) and prepared for shipment,required transportation means andaccompanying documents are provided, anddelivery is executed” (Schönsleben, 2000, p.139).

111. Safety stock in the framework of SCM can bedescribed as follows: “Safety stock has toprotect against uncertainty which may arisefrom internal processes like production leadtime, from unknown customer demand, and fromuncertain supplier lead time. This implies that themain drivers for the safety stock levels areproduction and transportation disruptions,

forecasting errors, and lead time variations. Thebenefit of safety stock is that it allows quickcustomer service and avoids lost sales,emergency shipments, and the loss of goodwill.Furthermore, safety stock for raw materialsenables smoother flow of goods in theproduction process and avoids disruptions due tostockout at the raw material level. Besides theuncertainty mentioned above, the main driver forsafety stock is the length of the lead time(production or procurement), which is necessaryto replenish the stock” (Sürie & Wagner, 2002,p. 41).

112. Cp. Hoover et al. (2001, p. 9).113. Cp. Mentzer et al., no year, p. 18).114. Cp. Hugos (2003, p. 3).115. The term indicator applies also to “pointer”; cp.

Wahrig-Burfeind (2004, p. 164).116. The business-external or business-integrated

Supply Chains are meant by this, which havebeen previously more closely explained, cp.Werner (2002, p. 6).

117. The connection between actually competing

objectives gives reference to the competitivestrategy of so-called Outpacing. Whilst – aspreviously explained in sect. 1.3.2 – Porterconsiders the strategic alternatives as unable tobe unified, Gilbert & Strebel assume that thesealternatives can be combined, if notsimultaneously, then in chronological order. Theso-called outpacing strategy is highlighted by thefact that a business changes between the twostrategy-alternatives during its strategicadjustment in order to gain an enduring leadahead of the competition. The reason is that if acorporation has gained a competitive advantage,it can be assumed that other corporations willattempt to position themselves on the marketand, under certain conditions, will imitate thestrategy. New bidders will push themselves ontothe market until no more advantages can berealized and the leaders in cost or differentiatingproducts can secure no further competitiveadvantage. Gilbert & Strebel however, criticallyindicate that only the best businesses are in aposition to overcome the conflict in objectives

between customer benefits and low costs andachieve a low cost level if a customer benefit isgiven; cp. Gilbert and Strebel (1987, p. 28),Hamel and Prahalad (2000, p. 125), and Ekholmand Wallin (2004, p. 4). Beyond the outpacingstrategy which assumes not a simultaneous, butmore a successive application of varyingstrategy alternatives, so-called hybridcompetitive strategies exist within which fallse.g. the hypothesis of simultaneousness, whichpresumes that a combined strategy usage is atleast temporarily possible; cp. to this endCorsten and Will (1995, p. 1).

118. Cp. Kuglin (1998, p. 3). Cp. also Kuglin andRosenbaum (2001).

119. Cp. Hoover et al. (2001, p. 48f).120. Cp. Hugos (2003, p. 40).121. Cp. Werner (2002, p. 10).122. Cp. Schäfer and Seibt (1998, p. 365).123. The competence with special reference to the

Supply Chain has great importance advancingtowards it within the framework of the SCORmodel and the pertinent performance indicators,

which will be dealt with dedicatedly at thebeginning of sect. 1.7.

124. For the term Competitive Advantage see para.1.3.2.

125. The cash flow is a indicative index of the flow ofcapital from the profit process, from whichinsights into the solvency and the financialdevelopment of the company can be gained; cp.Schierenbeck (2003, p. 316) and Wöhe (1984, p.740). In the context of the Supply Chain, thecash flow includes all financial transactionsoccurring as a result of the trading of goods,commodities and services; cp. Thaler (2003, p.45).

126. The Supply Chain strategy describes what abusiness wishes to achieve with the SupplyChain and which services are achievable with it.With its SC strategy a business defines how itcan contribute to its competitive capability usingits SC processes and SC infrastructure. Theobjective of the strategy definition is theidentification of relevant competitive factors andtheir implementation within the Supply Chain.

The SC strategy is subordinate to the respectivebusiness or competitive strategy, or respectivelyderived from them and must support them.Following this, a substantial characteristic of asuccessful SC strategy is the adjustment to thebusiness strategy and therefore to theorganization’s strategic core vision; cp. Geimerand Becker (2001a, p. 21).

127. The following definition of terms is to be used forOEM: “OEM is an acronym for OriginalEquipment Manufacturer. An OEM is acompany that builds components that are used insystems sold by another company called aValue-Added Reseller or VAR. The practice ofa VAR selling products with components fromOEMs is common in the electronics andcomputer industry. Typically an OEM will buildto order based on designs of the VAR. Incommon usage, a VAR is sometimes called anOEM, despite this being a complete reversal ofthe literal meaning of both terms. Thismisunderstanding arises from use of the termOEM as a verb. For example, a VAR might say

that they are going to OEM a new product,meaning they are going to offer a new productbased on components from an OEM. In recentyears, some OEM’s have also taken on a largerrole in the design of the product they aremanufacturing. The term ODM, Original DesignManufacturer, is used to describe companiesthat design and manufacture a product that isthen sold under other brand names” (WordIQ,2004b). Cp. also to this end Chakravarty (2001,p. 334).

128. Cp. Industry Directions (1998b, p. 2).129. Cp. Goldrath (1999, p. 4) and Heinrich and Betts

(2003, p. 14).130. For the description of structural and process

organizations, cp., for example, Wöhe (1984, pp.156, 171) and Grochla and Wittmann (1974, pp.1, 190).

131. Cp. Hines, Silvi, Bartolini, and Rachi (2002, p. 55).132. The term integrated stands for “integral, combined

or interlinked”; cp. Wahrig-Burfeind (2004, p.189). Consequently de-integrated can be definedas “non-integral, non-combined or non-

interlinked.”133. Self-representation of the business: “Smart GmbH

is a 100% daughter company ofDaimlerChrysler AG. Founded: April 1994,1,140 employees (as at: Jan. 2003),administrative central office: Böblingen(Germany), production location: Hambach(France), with a presence in 24 countries (as at:January 2003)” (Smart, 2004).

134. The term mass customization represents a furthervariety of the hybrid competitive strategiesmentioned in para. 1.4.2. In the core of this –expressis verbis – the customer-individual massproduction of products for a large trading marketis understood. In accordance with this, theproducts must meet the various needs of thedemanding. During this the costs should roughlyrepresent those of a mass production ofstandardized products. The approach seeks abalanced association between continuallyrunning mass production and non-continuousindividual production; cp. Piller (1997, p. 16),Piller and Schoder (1999, p. 3), and Chakravarty

(2001, p. 132). Cp. to this end also Pine (1993).135. Cp. van Hoek and Weken (2000, p. 3).136. Cp. Campbell and Wilson (1995, p. 14). Cp to this

end also Jarillo (1993).137. Cp., roughly, Schönsleben (2000, p. 22).138. Cp., roughly, Thaler (2003, p. 17). See to this end

also Nippa and Picot (1995), Kuhn (1995), andTurner and Thaler (1995).

139. For an embracing description of the structure, theelements and the monitoring principles of aSupply Chain, q.v. Stewens (2005).

140. Cp., for example, Hagemann (2004, p. 5), Heck(2004, pp. 1, 48), and Werner (2002, p. 16).

141. The process chain as a basis for the model can bedescribed as follows: “The process chain isdefined as a set of chronological and logicalrelated activities performed to achieve a definedbusiness outcome” (Beckman, 1999, p. 27).

142. Cp. Banfield (1999, p. 205) and Handfield andNichols (2002, p. 40).

143. Cp. Kaczmarek and Stüllenberg (2002, p. 275).144. In the general sense, the term productivity can be

defined as follows: “The rate of output per unit

of input.” Transposed upon the area of IndustrialManagement, this leads to the followingdefinition of the term: “In factories andcorporations, productivity is a measure of theability to create goods and services from a givenamount of labor, capital, materials, land,resources, knowledge, time, or any combinationof those. Since capital goods tend to decline invalue and wear out, most economists distinguishbetween gross capital productivity (total yield)and net capital productivity, which discountsdepreciation” (Mintzer, 1992, p. 20). Cp. also,for example, Schierenbeck (2003, pp. 107, 638).

145. The terms effectivity and efficiency, in addition tothe differences and connections between theterms, will be dealt with more closely in para.1.7.1.

146. Cp. Preißner (2003, p. 123).147. Whereby cost need to be distinguished from

expenses (for the varying terms q.v. Scherrer(2001, p. 629).

148. Cp. Wöhe (1984, p. 871).149. Cp. Kuglin (1998, p. 192).

150. Measurement of cost in the post-costing,estimation of the cost in the pre-costing (note ofthe author).

151. Cp. Stemmler (2002, p. 176).152. Incremental or marginal costs are defined as the

manufacturing costs of each of the previouslybrought out (produced) unit. As long as a thetotal cost curve of a product or cost area runslinearly, the incremental costs for everymanufactured unit are equal and represent theproportional costs or product costs respectively.The terms product costs, proportional costs andmarginal costs have the same meaning. Formarginal cost calculation, the terms proportionalcost calculation or Direct Costing aresynonymously used; cp. Schierenbeck (2003, pp.287, 676) and QM (2004b).

153. Expenses or payments respectively represent anegative payment flow in the sense of moneyoutlet. They are compared to respectiveincomes or incoming payments; cp. Scherrer(2001, p. 628).

154. Cp. Govil and Proth (2002, p. 87). For the term

cash flow see the footnote under para. 1.4.3.155. The term Key Performance Indicator, KPI, can

be defined as follows: “A statistical measure ofhow well an organization is doing. A KPI maymeasure a company’s financial performance orhow it is holding up against customerrequirements” (ASQ, 2004). Cp. also Sürie andWagner (2002, p. 32). The term is to serve as acollective term for other related terms within theframe of the work submitted, for exampleperformance attributes, metrics andperformance measures, which will be dealt within detail in the further course of the work.

156. A company’s success can be identified as thedifference between results and costs or as thedifference between revenues and costs. In profitand loss accounting it is evidenced by thedifference between earnings and expenses overa certain period, in the form of profit and loss;cp. Wöhe (1984, pp. 873, 1021).

157. Cp. Kerzner (2003, p. 63).158. Cp., roughly, SAP (2003, 2004, p. 2), Reiner and

Hofmann (2004, p. 1) and PMG (2002, p. 1).

159. Cp. BMA (2004b).160. Cp. Novack et al. (1995, p. 235).161. Ibid.162. As to the term customer satisfaction and the

relevant influencing factors, cp. also Seibt(1997a, p. 19).

163. For an overview of present developments in thefield of Supply Chain Cost management, q.v.Cooper and Slagmulder (1999).

164. Activity-based costing is highlighted by the factthat the allocation of general product costs in theindirect performance areas does not occur onthe basis of value-measured cost drivers, butaccording to the tasks necessary for production(processes, activities) with given considerationto the “cost drivers” influencing the processes;cp. Scherrer (2001, p. 655). Cp. roughly alsoHorváth and Mayer (1989, p. 214) and Cooperand Kaplan (1991, p. 130).

165. Cp. Dekker and Van Goor (2000, p. 41).166. Cp. Thaler (2003, p. 89). Cp. to this end also

Weber (1992).167. For an overview of operational performance

measurements as building blocks of aManagement Information System (MIS), q.v.Müller-Hagedorn (1999, p. 729).

168. See under para. 1.5.3. Cp. also Novack et al.(1995, p. 235).

169. The application possibilities and particulars of aquestionnaire within the framework of anexamination under inclusion of performanceindicators will be dealt with in detail in chap. 3.

170. Cp. Novack et al. (1995, p. 235).171. Self-representation of the organization: “Nolan

Norton Institute carries out research in the areasof business strategy, organization developmentand IT strategy and management. The insightsresulting from this are published and used in theconsulting practice of Nolan, Norton & Co. TheNolan Norton Institute was founded after theAmerican example in 1988 and since thenbecame a leader in investigating, creating anddisseminating knowledge on the management ofinformation age organizations. Nolan NortonInstitute designs and executes research oncorporate and business strategy, corporate and

business governance and management” (NNI,2004).

172. Self-description of the organization: “KPMG wasformed in 1987 with the merger of PeatMarwick International (PMI) and KlynveldMain Goerdeler (KMG) and their individualmember firms. Spanning three centuries, theorganization’s history can be traced through thenames of its principal founding members –whose initials form the name ‘KPMG’. KPMGInternational is a Swiss cooperational of whichall KPMG firms are members. KPMGInternational provides no services to clients.Each member firm is a separate andindependent legal entity, and each describesitself as such” (KPMG, 2004).

173. Cp. Kaplan and Norton (1997, p. VII). For anembracing overview of the Balanced Scorecard,q.v. Fitzgerald and Moon (1996).

174. Cp. Werner (2002, p. 269).175. Cp. to this end also Kaplan and Norton (1996a, p.

12). For the subject matter of the BalancedScorecard, q.v. Weber and Schäffer (1999) and

Horváth and Kaufmann (1998).176. Above all, the large Strategy Advisory Firms,

such as for example McKinsey or the BostonConsulting Group (BCG), have intensivelyassigned this procedure within the framework oftheir strategy projects, cp., for instance, Kollerand Peacock (2002, p. 1). Apart from this, thereare however smaller consultancies which havespecialized themselves in the concept’sapplication. One of the leading firms of this kindis the advisory company Balanced ScorecardCollaborative (BSCol), specializing in theapplication of the Balanced Scorecard andfounded by Kaplan and Norton, the creators ofthe Balanced Scorecard. The business describesitself as follows: “Balanced ScorecardCollaborative is a new kind of professionalservices firm that facilitates the worldwideawareness, use, enhancement, and integrity ofthe Balanced Scorecard (BSC) as a value-added management process” (BSCol, 2004).

177. Cp. Industry Directions (1998a, p. 5). For theterm Return on Investment (ROI) see the

explanations under para. 1.3.1. For thecalculation of the ROI, q.v. Preißner (2003, p.181).

178. Cp. Kaplan and Norton (1996b, p. 75) andWerner (2002, p. 169).

179. The strategic core vision describes a company’stask, core competences, competitive orientationfor achieving competitive advantages, futurecompetitive positioning, future product range,and financial objectives (growth, profits, etc.).From this it can be deduced in which areas abusiness must reach top performances. Thesuccessful implementation can be interpretedfrom the evaluation of the respectiveperformance on the market; cp. Geimer andBecker (2001a, p. 22).

180. Cp. Kaplan and Norton (1996b, p. 75).181. Cp. BMA (2004a). In this context it is important

to understand that the listed perspectives are notgenerally valid, but of company-specific nature.

182. Data Warehouses can be defined as follows: “Alogically consolidated store of data drawn fromone or more sources within the enterprise and/or

outside the enterprise” (Simon & Shaffer, 2001,p. 9).

183. Cp. Industry Directions (1998a, p. 5), Zeller(2003, p. 8), and Kummer (2001, p. 81). For ascientifically founded overview as to operationaland strategic management and controllingapproaches for the measurement of theperformance capability of Supply Chains,reference is made to Erdmann’s work; cp.Erdmann (2003).

184. Cp. Werner (2000a, p. 8; 2000b, p. 14).185. Cp. Sürie and Wagner (2002, p. 33).186. Cp. Zimmermann (2002, p. 413). Thaler speaks in

this context of the area of conflict betweenmanufacturer/customer and supplier, cp. Thaler(2003, p. 16).

187. Cp. for example, Geimer and Becker (2001b, p.128), Heck (2004, p. 13), and Kanngießer (2002,p. 9).

188. Self-representation of the organization: “TheSupply-Chain Council was organized in 1996 byPittiglio, Rabin, Todd & McGrath (PRTM) andAdvanced Manufacturing Research (AMR),

and initially included 69 voluntary membercompanies. The Supply-Chain Council now hascloser to 1,000 corporate members worldwideand has established international chapters inEurope, Japan, Australia/New Zealand, SouthEast Asia, and Southern Africa with additionalrequests for regional chapters pending. TheSupply-Chain Council’s membership is primarilypractitioners representing a broad cross sectionof industries, including manufacturers, services,distributors, and retailers. The site is divided intoa ‘public’ and a ‘member’s only’ section. Non-members are welcome to browse the publicsection information including the Supply chainOperations Reference-model (SCOR) Overviewmaterials, IT vendors, consultants, andresearchers that support SCOR, calendar ofupcoming events, links to other relatedorganizations, and general information on theassociation. For a nominal annual fee membershave access via password to the current versionof SCOR, complete contact information on allmembers, email access to committee dialog on

SCOR, given at Supply-Chain Councilconferences, and research study resultsconducted by members and others under theauspices of the Supply-Chain Council” (Supply-Chain Council, 2006c).

189. Cp. roughly Schönsleben (2000, p. 152) and Meyret al. (2002a, p. 45).

190. The SCOR monitoring processes will be dealtwith closely in chap. 2.

191. Cp. Schönsleben (2000, p. 152).192. Trommer combines all the facts as follows:

“Various departments are now talking the samelanguage (…) that’s a notable achievement. Theframework (the SCOR model is referred tohere; note of the author) helped to break downfunctional silos and allowed people to look atreal issues and practices holding back supplychain management improvements. It gavepeople the chance to look at the supply chainwith customer-wide needs in mind” (Trommer,1996, p. 59). The author quotes Vinay Asgekar,head of the Business Process Reengineering(BPR) group of Rockwell Semiconductors.

193. The term Benchmarking can be defined asfollows: “The process of measuring products,services, and practices against the toughestcompetitors or those known as leaders in theirfield. The subjects that can be benchmarkedinclude strategies, operations, processes, andprocedures. The objective of benchmarking is toidentify and learn ‘best practices’ and then touse those procedures to improve performance”(SQN, 2004). Cp. also Schäfer and Seibt (1998,p. 365), Schönsleben (2000, p. 95), andSchumann (2001, p. 102). See for this purposealso the explanations in chap. 3, para. 3.1.1.2.

194. Cp. Seuring (2002, p. 20) and Werner (2002, p.24).

195. Cp. Hellingrath (1999, p. 78).196. Cp. Welke (2003, p. 13). The subject matter was

also closely discussed in a personal conversationby the author with Welke; see Poluha (2004b).

197. The term best practices can be defined asfollows: 1. Exemplary solutions or methods ofprocedure that lead to top performances, are“best practices.” 2. The action taken in order to

identify such procedures and to use them forimprovements to one’s own processes, often asan extension to benchmarking. Best practice is apragmatic procedure. It systemizes existingexperiences of successful organizations (oftenalso competitors), users, and so on. It comparesvarious solutions which are assigned in practice,evaluates them with the aid of operationalobjectives, and determines on this basis whichformations and methods of procedure bestcontribute to the achievement of targets; cp.O’Dell and Grayson (1998, p. 167).

198. Cp. to this end Burnstine and Soknacki (1979, p.115). BIAIT originated within the framework ofan IBM research project and was furtherdeveloped within IBM. The range of applicationcomprised the process areas of planning andexecution of application development, marketingplanning and organization analysis. A planningapproach for application development which isfounded upon the BIAIT principles is the so-called Business Information Control Study(BICS). The BICS approach leads to an

immediate identification of problem areas thathave a high visibility for business executives anda large potential for the realization of advantagesin the usage of computers; cp. Carlton (1980, p.2).

199. Cp. Cremer (2005, p. 35). As to the KölnerIntegrationsmodell (Cologne Integration Model),see also Grochla et al. (1974).

200. See chap. 2, sect. 2.2.201. Cp. Fettke and Loos (2003b, p. 33).202. B2B is focused on the interactions of institutional

partner among themselves, B2C on the businesswith end customer, and G2C on the interactionsamong public sector and end customers; cp., forinstance, Stadler (2002, p. 15f), Kodweiss andNadjmabadi (2001, p. 75f), and Schäfer (2002,p. 16).

203. Cp. Werner (2002, p. 113).204. Cp. Premkumar (2002, p. 367).205. Cp. Fettke and Loos (2003b, p. 34).206. See to this end chap. 2, para. 2.4.1.3.207. (Razvi, 2002, p. 3).208. Schäfer and Seibt define competence as the

ability to be able to manufacture innovativeproducts in the highest quality at market capableprices faster than the competition. In order torealize this, the organization’s processes must becontinuously improved and be formed moreeffectively and efficiently by the integration ofnew, innovative ideas; cp. Schäfer and Seibt(1998, p. 365). In the existing context thiscapability, as well as the associated processesrefer specifically to the Supply Chain, which isemphasized by the term SC competence.Competence in turn determines theperformance.

209. Cp. Hugos (2003, p. 34).210. Cp. Hugos (2003, p. 37).211. Cp. Werner (2002, p. 10). For effectivity Seibt

synonymously uses the term effectiveness withthe following definition: “Effectiveness is theresult-factor that is frequently associated withoutput quality” (Seibt, 1997a, p. 20).

212. Profitability is defined as earnings in relation toexpenses, or results in relation to costs. Inopposition to this, productivity is the amount

produced in relation to the amount of factorassignment. Profitability is therefore alsodescribed as economic efficiency, andproductivity is described as technical efficiency;cp. Schierenbeck (2003, p. 593) and Bea, Dichtl,and Schweitzer (1991, p. 2).

213. Cp. FHTE (2004). For the term business successsee chap. 1, para. 1.5.3. Seibt defines efficiencyas follows: “Efficiency is a measure of processeconomy and indicates the degree to which theprocess is able to produce a higher value ofoutput with lower levels of cost” (Seibt, 1997a,p. 21).

214. In the general sense, Target Costing respectivelyrepresents a customer- or market-orientatedcost determination of which all relevant costflow measures are viewed as variables; cp.Scherrer (2001, p. 660). For the term SupplyChain-based marginal costing, cp., for example,Kajüter (2002, p. 35).

215. Cp. Goldbach (2002, p. 94).216. For the term Activity-based costing (ABC) see

the explanations under para. 1.5.3.

217. Cp. Slagmulder (2002, p. 86)218. Self-representation of the organization: “The

Massachusetts Institute of Technology – a co-educational, privately endowed researchuniversity – is dedicated to advancingknowledge and educating students in science,technology, and other areas of scholarship thatwill best serve the nation and the world in the21st century. The Institute has more than 900faculty and 10,000 undergraduate and graduatestudents. (…) A great deal of research andteaching takes place in interdisciplinaryprograms (…) whose work extends beyondtraditional departmental boundaries” (MIT,2004a).

219. The study named is described by MIT as follows:“The Integrated Supply Chain ManagementProgram (ISCM) is a consortium of non-competing companies that was started inJanuary 1995 by a group of faculty and stafffrom the Sloan School of Management and theCenter for Transportation & Logistics, wherethe Program is currently managed. The purpose

of the program is to accelerate theimplementation of supply chain managementprinciples within the sponsor companies, and toadvance the state of the art of supply chainmanagement. The ISCM Program enablessponsors to learn about the state-of-the-art andfuture supply chain practices in two main ways:1. Facilitating best-practice-sharing andexchange among sponsors. 2. Creating newsupply chain knowledge through ISCM and MITresearch projects” (MIT, 2004b).

220. Cp. Hoover et al. (2001, p. 7).221. Self-evaluation of the company: “PRTM works

closely with leading companies worldwide toachieve breakthrough business results. Since1976, we’ve delivered measurable value to ourclients, earning one of the highest levels ofrepeat business in the management consultingindustry. PRTM is also a recognized thoughtleader and innovator. There are 14 PRTMoffices and more than 400 consultants in theU.S., Europe, and Asia. Our provenmethodologies have become the industry

standard. The widely used Supply ChainOperations Reference (SCOR) model wasoriginally developed by PRTM in collaborationwith Advanced Manufacturing Research(AMR) and the Supply-Chain Council (SCC).PRTM has practices in product development,supply chain and operations, customer serviceand support, sales effectiveness, and strategicIT management, as well as a benchmarkingsubsidiary” (PRTM, 2004b).

222. Cp. PMG (2002, p. 1). Cp. to this end also PRTM(2001).

223. The following three types of inventory can bedifferentiated in association with inventorymanagement costs: 1. Cycle inventory: isnecessary in order to fulfil product demandamongst normally planned orders. 2. Seasonalinventory: represents produced and storedproducts in order to satisfy the expected demandthat arises due to seasonal needs. 3. Safetyinventory: is necessary in order to compensatefor uncertain and unpredicted demand and ordercycle times. With this, four fundamental

possibilities exist for the reduction of safetyinventory: Firstly, the lowering of demanduncertainty by increased prognosis exactness.Secondly, shorter cycle times, which lead to areduced safety inventory for coverage. Thirdly,a lowering in cycle time fluctuation. Andfourthly, the reduction of uncertain productavailability if demand exists; cp. roughly, Hugos(2003, p. 62).

224. Cp. Hoover et al. (2001, p. 50).225. Ibid.226. Cp. Stemmler (2002, p. 172).227. (Bovet & Martha, 2000, p. 43).228. (Hughes et al., 1998, p. 183).229. For the term shareholder, cp., for example,

Schierenbeck (2003, p. 86).230. Shareholder Value can be described as follows:

“At the end of the business cycle of a company,after all debts have been paid, money remains.This money, the free cash flow, is for theshareholder or shareholders. The free cash flowis the amount of money that is left after allcreditors are paid within the appropriate period.

The definition of Shareholder Value is the valueof the company (firm) minus the future claims(debts). The value of the company can becalculated as the Net Present Value (NPV) ofall future cash flows plus the value of the non-operating assets of the company” (Value BasedManagement, 2004). Cp. to this end also Albach(2001, p. 301) and Accenture (2004, p. 2).

231. Cp. Evans and Danks (1999, p. 20).232. For the term profitability see para. 1.3.1.233. Cp., roughly, Schierenbeck (2003, p. 635). The

invested capital plays a large role for theprofitability of an investment, which will be dealtwith more closely in chap. 2, para. 2.3.1.

234. See chap. 2, para. 2.1.4.235. Cp. Friedrichs (1990, p. 50). In the present

context we are dealing in analogue withproblems regarding business economics andSupply Chain Management respectively.

236. For background information on the Supply-ChainCouncil (SCC) see chap. 1, sect. 1.6.

237. Cp. Hellingrath (1999, p. 77).238. The process term in the present context allows

itself to be defined as follows: “A coordinated(parallel and/or serial) set of process activity(s)that are connected in order to achieve acommon goal. Such activities may consist ofmanual activity(s) and/or workflow activity(s)”(Hollingsworth, 1995, p. 52). The termWorkflow will be dealt with in sect. 2.2.

239. Cp. Supply-Chain Council (2006b, p. 2).240. For the term Best Practice see chap. 1, sect. 1.6.241. Cp. Bolstorff and Rosenbaum (2003, p. 11) and

Geimer and Becker (2001b, p. 116).242. For the term Key Performance Indicator see

chap. 1, para. 1.5.3 (note of the author).243. (Industry Directions, 2001a, p. 9).244. For background information on the copmany

PRTM see chap. 1, para. 1.7.1.245. Self-description of the organization: “As an

independent research analyst firm, AdvancedManufacturing Research (AMR) is committedto providing unbiased, frank analysis on theenterprise software sector, working with boththe users and providers of technology to ensurethat we have a clear, objective picture of a

market or industry before publishing ourresearch. Our research and advisory servicesare focused on the enterprise softwareapplications and infrastructure – includingEnterprise Resource Planning (ERP), CustomerRelationship Management (CRM), and SupplyChain Management (SCM)” (AMR, 2004).

246. Cp. Supply-Chain Council (2006). Cp. alsoWerner (2002, p. 15), Handfield and Nichols(2002, p. 67) and Meyr et al. (2002a, p. 45).

247. Cp. Kaluza and Blecker (1999, p. 21) andKanngießer (2002, p. 4).

248. Cp. McGrath (1996, p. 1).249. The SCC’s internet page may be found at

www.supply chain.org250. The current SCOR model, valid at beginning of

2007, represented SCOR version 8.0.251. Cp. Supply-Chain Council (2003a, p. 2), Schäfer

(2002, p. 48), and Hellingrath (1999, p. 77).252. Cp., roughly, Bolstorff and Rosenbaum (2003, p.

154) and Hugos (2003, p. 44).253. (Supply-Chain Council, 2006b, p. 2).254. Cp. Supply-Chain Council (2006b, p. 2).

255. Cp. Hagemann (2004, p. 6) and Geimer andBecker (2001b, p. 118).

256. Cp. Werner (2002, p. 21) and Hagemann (2004,p. 6).

257. Ibid.258. Cp. Hagemann (2004, p. 6) and Werner (2002, p.

21).259. Ibid.260. Cp. Meyr et al. (2002a, p. 48).261. (Supply-Chain Council, 2006b, p. 10). Detraction

in quality of the illustration result from extractionfrom the original document, which can, ifnecessary, be accessed from the original forbetter legibility.

262. Cp. Corsten and Gössinger (2001, p. 141) andBecker (2002, p. 68).

263. Cp., roughly, Klaus (2005, p. 79).264. Cp., roughly, Geimer and Becker (2001b, p. 123),

Werner (2002, p. 26), and Bolstorff andRosenbaum (2003, p. 225).

265. (Supply-Chain Council, 2005b, p. 13).266. Cp. Meyr et al. (2002a, p. 47) and Kanngießer

(2002, p. 6).

267. Cp. Kanngießer (2002, p. 4) and Supply-ChainCouncil (2006b, p. 4).

268. The performance terms will be dealt with moreclosely in chap. 3.1.1.

269. For an automated SC planning system the termAdvanced Planning System (APS) is often used;for definition of the term see chap. 1, para.1.4.2.

270. Cp. Heck (2004, p. 13).271. Cp. Meyr et al. (2002a, p. 50), Bolstorff and

Rosenbaum (2003, p. 60), and Kanngießer(2002, p. 9).

272. Ibid.273. Cp. to this end also Bolstorff (2004, p. 22) and

Geimer and Becker (2001b, p. 128).274. (Supply-Chain Council, 2003b, p. 9).275. A detailed representation of the various

performance terms and their connections can befound in chap. 3. para. 3.1.1.1.

276. Cp. Hugos (2003, p. 154) and Heck (2004, p. 14).277. Cp. Supply-Chain Council (2003b, p. 4).278. Ibid.279. For the term Electronic Business (E-Business),

q.v. Seibt (2001, p. 11) and KPMG (2000, p. 2).280. Cp. Supply-Chain Council (2003b, p. 11).281. Cp. Supply-Chain Council (2005b, pp. 1, 8).282. Cp. Supply-Chain Council (2005b, pp. 1, 9).283. Cp. Supply-Chain Council (2005b, p. 296).284. Cp. Supply-Chain Council (2005b, p. 319).285. As at beginning of 2007.286. Cp. Supply-Chain Council (2006a, p. 1).287. Cp. Supply-Chain Council (2006b, pp. 8, 368,

434).288. Cp. Supply-Chain Council (2006b, pp. 386, 511).289. The meaning and application of the ISA-95

standard can be collectively described asfollows: “Reconciling plant floor systeminformation with business information is thebiggest benefit manufacturers find in the ISA-95enterprise and control integration standard,especially those in the food and beverage andpharmaceutical industries, where Parts 1 and 2have been streamlining paper processes intoreal-time automated processes and skyrocketingproductivity. Now, with Part 3 in the mix, thesecompanies can reap the rewards of new

manufacturing execution systems (MES) to helpmeet FDA (Food and Drug Association; note ofthe author) requirements with tracking andtraceability” (ISA, 2006b).

290. Self-representation of the Organisation: “Foundedin 1945, ISA is a leading, global, non-profitorganization that is setting the standard forautomation by helping over 30,000 worldwidemembers and other professionals solve difficultproblems. Based in Research Triangle Park,North Carolina, ISA develops standards;certifies industry professionals; provideseducation and training; publishes books andtechnical articles; and hosts the largestconference and exhibition for automationprofessionals in the Western Hemisphere” (ISA,2006a).

291. Cp. Supply-Chain Council (2006b, pp. 9, 419).292. “BPM is a systematic approach to improving an

organization’s business processes. BPMactivities seek to make business processes moreeffective, more efficient, and more capable ofadapting to an ever-changing environment. BPM

is a subset of infrastructure management, theadministrative area of concern dealing withmaintenance and optimization of anorganization’s equipment and core operations”(Bitpipe, 2006).

293. The internet page is: www.supply-chain.org.294. Cp. Supply-Chain Council (2006a, p. 1; 2006b, p.

9).295. Cp. Supply-Chain Council (2006b, p. 9).296. By the formulations “representation of Supply

Chains” and “…to describe Supply Chains…”, itis indicated that the SCOR model is adescriptive model. It does not thereforerepresent a forming model, with the exceptionthat it can positively contribute to the structuringof the Supply Chain by identification andimplementation of necessary measures.

297. Cp. Supply-Chain Council (2005a, p. 2).298. Reference models can be counted as normative

models which are more closely described inchap. 1, sect. 1.6. The term referenceinformation model can also be found for thesemodels: An information model is a model which

represents an organization’s information system.A reference information model (referencemodel in short) is a concrete information modelfor a business, which is abstracted from a singlecase and is used to represent a standardizedsection of reality. Reference models support thedevelopment of an organization’s individualinformation model, cp., for example, Fettke andLoos (2003a, p. 35). For Supply ChainManagement implementation, company-spanninginformation systems are necessary in addition tothe businesses’ willingness to cooperate andreveal all processes that enable a promptexchange of information; cp. Scholz-Reiter andJakobza (1999, p. 7).

299. Cp. Migge (2002, p. 6) and Kämpf and Trapero(2004). For an exhausting overview of businessprocess reference models, cp. also Scheer(1997).

300. (Supply-Chain Council, 2006b, p. 3).301. Cp. Seuring (2002, p. 20).302. (Supply-Chain Council, 2005a, p. 1).303. Workflows and the associated system of

monitoring these workflows (WorkflowManagement System) can be defined in thecontext of business process reference models asfollows: Workflow: The computerised facilitationor automation of a business process, in whole orpart. Workflow is concerned with theautomation of procedures where documents,information or tasks are passed betweenparticipants according to a defined set of rules toachieve, or contribute to, an overall businessgoal. While workflow may be manuallyorganised, in practice most workflow is normallyorganised within the context of an IT system toprovide computerised support for the proceduralautomation. (…) Workflow ManagementSystem: A system that completely defines,manages and executes workflows through theexecution of software whose order of executionis driven by a computer representation of theworkflow logic”; cp. Hollingsworth (1995, p. 5).

304. Cp. Hollingsworth (1995, p. 20).305. For the subject matter of structure,

implementation and explanation of business

regulations for the determination of businesstransactions in and between organizations withinthe framework of SCM, as well as variousimplementation aspects of these businessregulations in information systems in support ofSCM, cp. Klaus (2005).

306. Cp. Holten and Melchert (2002, p. 207).307. Cp. Heinzel (2001, p. 51) and Thaler (2003, p.

48).308. The “Human Factor” is dealt with in chap. 6,

sect. 6.1.309. (Supply-Chain Council, 2006b, p. 4).310. Cp. Werner (2002, p. 6) and Supply-Chain

Council (2006b, p. 4).311. The submitted work follows the process-

orientated definition of E-Business according toSeibt, which was introduced at the end of chap.1, para. 1.3.1., cp. Seibt (2001, p. 11), becausethe submitted reference models in general andespecially the SCOR model are also consideredprocess models.

312. Cp. Fettke and Loos (2003b, p. 31). Cp. alsoFettke and Loos (2003a, p. 35).

313. Cp. Seuring (2001, p. 26), Kaluza and Blecker(1999, p. 22), and Hofmann (2004, p. 1).

314. Enterprise Resource Planning (ERP) Systemsfollow the primary objective integrating andmaking centrally available in one system theoften functionally adjusted solutions for diversebusiness areas already present within anorganization, such as procurement, production,sales and so on, as well as their relevant data.ERP systems, in that sense, representtransaction systems which mainly illustrate theactual condition of a business and administratehistorical data, cp. Kansky (2001, p. 205).

315. (Handfield & Nichols, 2000, p. 53).316. (Bolstorff & Rosenbaum, 2003, p. 9). Davenport

provides the following further description as tothe qualitative advantages: “Hundreds oforganizations (…) have begun to use the SCORmodel to evaluate their own processes; softwarevendors (…) have begun to incorporate SCORflows and metrics into their supply chainsoftware packages. Some companies havealready benefited greatly from a SCOR-based

analysis of their supply chain processes”(Davenport, 2005, p. 102).

317. For the term profitability see chap. 1, para. 1.3.1.318. The term project portfolio can be described as

follows: “Project portfolio management refers tothe selection and support of projects or programinvestments. These investments in projects andprograms are guided by the organization’sstrategic plan and available resources” (PMI,2000, p. 10).

319. Cp. Hughes et al. (1998, p. 97).320. Note of the author: The statement that quality

defects were able to be lowered by 100% mustbe considered doubtful, even if this representsthe objective of concepts such as Total QualityManagement (TQM). For the concept of TQM,cp., for example, Pfohl (1992) and Zink (1989).

321. Cp. Stephens (2000, p. 39). The calculation of thespecified performance indicators have still to bedealt with in detail in chap. 3, para. 3.1.1.

322. Cp. Stewart (1997, p. 62).323. Schäfer and Seibt speak, in conjunction with this,

of generic benchmarking and provide the

following description: The great advantage ofbenchmarking exists in the discovery andintegration of innovative practices, which are notfound in one’s own industry. Within theframework of generic benchmarking acomparison of business processes takes placethat involve diverse functions and evolves froma variety of industries; cp. Schäfer and Seibt(1998, p. 376).

324. Business parameters: “Year founded: 1968;Number of employees: 78,000; Revenues: $30.1bn. (2003); Products and services: over 450;Fortune 500 ranking: 65; Worldwide offices andfacilities: 294” (Intel, 2004).

325. Cp. Intel Information Technology (2002, p. 6).326. According to Seibt, the management of

information and knowledge processes representsone of four core tasks of organizations’information management (4-pillar-model); cp.Seibt (1993, p. 3).

327. Cp. Intel Information Technology (2002, p. 7).328. Background information of the company: “SAP

(Systems, Applications and Products in data

conversion) was founded in 1972 by five IBMcolleagues and meanwhile has about 30,000employees. In software-development alone,worldwide altogether 8,200 colleagues areemployed. Aside from their main developmentcenter at their administrative seat in WalldorfSAP holds, amongst other things, developmentlaboratories in Palo Alto (USA), Tokyo (Japan),Bangalore (India) and Sophia Antipolis (France)as well as in Berlin, Karlsruhe and Saarbrücken.With branches in over 50 countries SAP made aprofit of 7.0 bn. Euros in the financial year2003” (SAP, 2004).

329. For information on PRTM, see chap. 1, para.1.7.1.

330. Cp. SAP (2003).331. Notes pertaining to the company: “In their five

business segments BASF attained revenues of33,4 bn. Euros in the year 2003. It is thestrategic objective to continue to growprofitably. On five continents, roughly 87,000employees ensure BASF’s success” (BASF,2004).

332. Cp. SAP (2003).333. Self-description of the organization: “Meta Group

helps companies (…) as a leading provider of ITresearch, advisory services, and strategicconsulting since 1989. Publicly traded(NASDAQ: METG) since December 1995,Meta Group offers proven models to ensure thatorganizations are fully prepared to optimize theiruse of technology, respond to demand, managerisk, seize market opportunities, and avoidexpensive mistakes. Serving as each client’spersonal radar screen, Meta Group monitors theIT and business world to deliver an accurate,independent view” (Meta Group, 2004, p. 1).

334. Cp. SAP (2003, p. 3). The different performanceterms and their variations within the SCORmodel’s context will be dealt with in detail inchap. 3, para. 3.1.1 (only metrics are spoken ofin the above text for the purpose of simplicity).

335. As for example E-Business, as illustrated in chap.1, para. 1.3.1 and sect. 1.6.

336. Cp. Harmon (2004, p. 5). Cp. also Supply-ChainCouncil (2006b, p. 6).

337. Cp. Supply-Chain Council (2003a, p. 2).338. This argument can also be found e.g., with

Handfield and Nichols (2002, p. 68).339. (Harmon, 2004, p. 6).340. Cp. Carr (2003, p. 5) and Rai et al. (2005, p. 2).341. Cp., for example, Klaus (2005, p. 79).342. Cp. Werner (2002, p. 26).343. This opinion is for example also shared by Seibt;

see Poluha (2003a).344. Self-description of Hewlett-Packard (HP): “HP

delivers vital technology for business and life.The company’s solutions span IT infrastructure,personal computing and access devices, globalservices and imaging and printing for consumers,enterprises and small and medium business. HPhas a dynamic, powerful team of 142,000employees with capabilities in 170 countriesdoing business in more than 40 currencies andmore than 10 languages. Revenues were $73.1billion for the fiscal year that ended October 31,2003” (HP, 2004).

345. The Customer-Chain Operations ReferenceModel (CCOR) and Design-Chain Process

Reference Model (DCOR) were initiated by theSCC. In conjunction with the SCOR model theycan be assigned to improve organizations’transaction processes. The further developmenttakes place within their own specially purpose-initiated Special Interest Groups (SIGs) –Design-Chain Council (DCC) and Customer-Chain Council (CCC); cp. Supply-Chain Council(2004).

346. Cp. Harmon (2004, p. 3).347. A merger occurs when two corporations join

together into one, with one corporation survivingand the other corporation disappearing. Theassets and liabilities of the disappearing entityare absorbed into the surviving entity; cp. BFI(2004). Cp. also to this end, for example,Schierenbeck (2003, p. 427).

348. Additional background information: “Hewlett-Packard completed the largest technologymerger in history by acquiring CompaqComputer. Closure of the deal was valued at anestimated $19 billion. (…) The combinedcompany (with a combined work force of

150,000) will be the world’s biggest maker ofcomputers and printers, as well as the third-largest provider of technology services,ostensibly able to tackle IBM and SunMicrosystems, while becoming more competitiveagainst Dell Computer” (CNET, 2002).

349. Cp. Harmon (2003a, p. 7).350. The term thread is defined in this context as

follows: “Configuring a supply chain ’thread’illustrates how SCOR configurations are done.Each thread can be used to describe, measure,and evaluate supply chain configurations. 1.Select the business entity to be modeled(geography, product set, organization). 2.Illustrate the physical locations of: Productionfacilities (Make), Distribution activities (Deliver),Sourcing activities (Source). 3. Illustrate primarypoint-to-point material flows using ’solid line’arrows. 4. Place the most appropriate Level 2execution process categories to describeactivities at each location” (Supply-ChainCouncil, 2005a, p. 17).

351. The HP-approach to the First Generation

Business Process Change presupposed that theteams must fundamentally analyse and renovateeach Supply Chain process.

352. Cp. Harmon (2003a, p. 7).353. For the SCOR model’s limitations see

explanations under para. 2.3.2.354. Cp. Harmon (2003a, p. 7).355. The term normative model has been dealt with in

more detail under chap. 1, sect. 1.6.356. (Welke, 2003, p. 30).357. Information on the company Intel may be found in

para. 2.3.1.358. Cp. Intel Information Technology (2002, p. 6).359. Ibid.360. The ensuring of support and regulation of

responsibilities refers to the role of the so-calledproject sponsor. The meaning and tasks of thisrole can be described as follows: “Sponsor: Theindividual or group within or external to theperforming organization that provides thefinancial resources, in cash or in kind, for theproject” (PMI, 2000, p. 16).

361. Cp. Intel Information Technology (2002, p. 6).

362. Ibid.363. The mentioned approach has strong similarity with

the concept of Collaborative Planning,Forecasting and Replenishment (CPFR), whichcan be described as follows: CPFR enables thecompany-spanning cooperation of buyers andsellers for demand and supply prognoses as wellas a regular update of plans that is based upon adynamic exchange of information via theInternet and should lead to optimal customerinventory stock and reduced supplier’s inventorystock. Supplier, manufacturer and customerdevelop a communal business plan, whichcomprises upcoming events (i.e., promotions) forthe synchronization of supply plans andforecasts. Businesses achieve effectiveadvantages by lower inventory levels as well asthe resulting profits, improved financial flow andlower capital investment, which should enable anorganization to simultaneously improveprofitability and market share; cp. Schneider andGrünewald (2001, p. 198). Cp. to this end alsoHellingrath (1999, p. 83).

364. The term Postponement can be described asfollows: “Postponement refers to product andsupply chain design that allows for last-minutecustomizing of a product for delivery” (Ayers,2002b, p. 658). Cp. also Colehower et al. (2003,p. 1).

365. Cp. Harmon (2003b, p. 1) and Intel InformationTechnology (2002, p. 9).

366. “The Value-Chain Operations Reference(VCOR) model describes the business activitiesassociated with all phases of deliveringmaximum value to the end user of value chainnetworks. VCOR enables collaboration betweenall partners and supports the activities of projectengineers, product developers, marketers,suppliers, logistics, point of sale and aftermarketservice providers across the extended enterpriseof a product or service” (Value Chain Group,2005b).

367. Self-description of the organization: “The ValueChain Group (VCG) is a non-profit organizationthat develops and maintains the Value ChainOperations Reference (VCOR) model. The

Value Chain Group exists to promote VCOR tobe the Value Chain Reference framework ofpreference globally, enabling next generationBusiness Process Management” (Value ChainGroup, 2005a).

368. Cp. Value Chain Group (2005b).369. (Intel, 2005).370. For normative models see chap. 1., sect. 1.6. For

the term value chain see chap. 1., paras. 1.3.1& 1.4.3.

371. Self-description of the organization: “The SupplyChain Integration Office has primaryresponsibility within the Logistics and MaterialReadiness secretariat (of the Office of theSecretary of Defense) for the following: 1. Tofacilitate DoD Component implementation ofsupply chain management practice. 2. Toidentify business process changes which couldbe enabled or strengthened through theimplementation of E-Business capabilities. 3. Tolead the development of modern supply chainpolicies in DoD, including the integration ofacquisition logistics and e-commerce

capabilities. 4. To develop and maintain DoDcomponent implementation of supply chainmanagement and end-to-end distributioncapabilities required to meet 21st centurydeployment and sustainment requirements. 5. Todevelop and maintain DoD policy regardingMateriel Management and Supply Distribution,including supply depot operations, storage andissue processing. 6. To develop and maintainDoD policy for Inventory Control, including itemaccountability, physical inventories,reconciliations and security. 7. To develop andmaintain DoD policy regarding PetroleumResource Management. 8. To act as the DoDfocal point for DLA (Defense LogisticsAgency)” (DoD, 2004c).

372. 80 bn. US-Dollars represented about 65 bn.Euros, calculated as at July 2006; cp. TiagoStock Consulting (2006).

373. The DoD offers the following explanations for theterm Integrated Supply Chain Management:“Vision: The transformation of DoD’s logisticssystem into a fully integrated supply chain based

on assured accountability and timely, accuratesatisfaction of customer needs. Mission: To leadthe implementation of a modern, integratedmaterial supply chain process that fully supportsmilitary operational requirements. To promotecustomer confidence in the logistics process bybuilding a responsive, cost-effective capacity toprovide required products and services” (DoD,2004b).

374. Cp. Gintic (2002).375. Cp. DoD (1998, p. 7–1).376. For the various E-Business areas see chap. 1,

sect. 1.6.377. Cp. DoD (2004a).378. (DoD, 2004a).379. Cp. DoD (2000, p. 77).380. Cp. DoD (2000, p. 14).381. Cp. (DoD, no year, p. 7–1).382. (SCE, 2004).383. Definition of the so-called Big Five: “Traditionally,

the five largest Certified Public Accountants(CPA) firms in the world. They are: Andersen;PricewaterhouseCoopers; Deloitte & Touche

LLP; Ernst & Young LLP; and KPMG”(AICPA, 2004). Due to the bankruptcy of thefirm Andersen in the year 2002, one speakstoday of the four remaining organizations as theBig Four.

384. Self-representation of the company: “BusinessObjects has incorporated the Supply ChainOperations Reference model into BusinessObjects Supply Chain Intelligence” (BusinessObjects, 2002).

385. Background information: “mi services group is anIT and management consultancy offering a widerange of technical and professional expertise,combined with broad business knowledge. (…)mi Services is an international organisation withheadquarters in Reading, UK and a network ofoffices across the UK, USA and New Zealand.We currently employ more than 200 peopleworldwide, of whom the vast majority areConsultants involved in the analysis, design,development, implementation and support ofsoftware systems” (mi Services Group Ltd.,2004).

386. The applications named will be dealt with moreclosely in chap. 5, sect. 5.3.

387. In this context, a tool can be defined as acomputer-supported system or procedure, ofwhich at least parts are programmed and madeto run on computers; cp. Seibt (2004, p. 19).

388. The illustrated electronic Balanced SCOR e-Cardrepresents a respective further development orautomation of the Supply Chain Scorecard, asshown in chap. 1, para. 1.5.4.

389. The configuration of the Supply Chain is asubstantial component of the SCOR model:“SCOR must accurately reflect how a supplychain’s configuration impacts managementprocesses and practices” (Supply-Chain Council,2005a, p. 13). The tools introduced serve theautomation of the procedure.

390. Cp. mi Services Group Ltd. (2004, p. 2).391. For background information on PRTM, see chap.

1, para. 1.7.1.392. Self-representation of the organization: “The

Performance Measurement Group, LLC wasformed in 1998 to offer a pioneering

benchmarking service in core business processareas. Our service gives participantsconfidential, customized benchmarking analysisonline. Hundreds of companies use PMG’sbenchmarking services to measure theirperformance relative to best-in-class companiesand identify opportunities to improve theirbusiness practices and use of IT. PMG’sservices are based on PRTM’s leading thinkingfrom 25 years of work in core business processmanagement” (PMG, 2004).

393. Cp. PRTM (2004a).394. Self-description: “BearingPoint is one of the

world’s largest business consulting and systemsintegration firms. (…) BearingPoint providesbusiness and technology strategy, systemsdesign, architecture, applications implementation,network, systems integration and managedservices. (…) BearingPoint has four industrygroups in which we possess significant industry-specific knowledge. These groups are PublicServices, Communications & Content, FinancialServices, and Consumer and Industrial

Technology. We have existing operations inNorth America, Latin America, the AsianPacific region, and Europe, the Middle East andAfrica (EMEA). Our principal executive officesare located at 1676 International Drive,McLean, Virginia” (BearingPoint, 2004c).

395. Cp. BearingPoint (2004a, p. 8).396. Becker and Geimer are also of the opinion that

the formation of the Supply Chain spans fiveareas of action: Strategy development, processformation, performance measurement,organizational development, and technologyformation; cp. Becker and Geimer (1999, p. 25).

397. Cp. BearingPoint (2004b).398. Cp. to this end BearingPoint (2003b, p. 17).399. For business information on SAP see para. 2.3.1.400. Cp. SAP (2003, p. 1).401. Cash-to-cash cycle time is used to measure how

long a business requires from payment of thesupplier up to receipt of the invoice sum fromthe customer. Due to many studies, thisaggregate measurement is a good evaluationmeasure for the efficiency of order completion;

cp. Geimer and Becker (2001b, p. 130).402. Cp. SAP (2004, p. 2).403. Under application of the so-called Little’s Law,

the inventory equals the produced amount indaily product units, multiplied by the cycle time;cp. WordIQ (2004a).

404. The reorganization of the planning processesplays an important role within the framework ofSCM. The assignment of modern softwaresystems offers great potentials here, whichcannot be made accessible by the (successive)planning concepts of conventional andrespective PPS or ERP systems. Due to theintroduction of so-called Advanced PlanningSystems (APS), the Supply Chain’s participantsare integrated by communal data and planning,and the deficits of the PPS systems have beenremoved as extensively as possible, especially inthe capacity planning area; cp. (Rohde et al.,2000), pp. 10. For an overview of the interplayof various applications, as for example ERP,APS, etc., cp. also Ayers (2002d, p. 35).

405. Cp. SAP (2003, p. 11).

406. Additional information: “The Singapore Institute ofManufacturing Technology (SIMTech) (…)contributes to the competitiveness of Singaporeindustry through the development of high valuemanufacturing technology and human capital. Itis one of the research institutes of the Agencyfor Science, Technology and Research”(SIMTech, 2004).

407. Cp. to this end Gintic (2000, 2002) and SIMTech(2002, 2003).

408. Cp. SIMTech (2003, p. 2).409. Cp. Friedrichs (1990, p. 52).410. The terms thesis and hypothesis are being used

synonymously. Under the term hypothesis oneunderstands in conjunction with empiricalresearch a presumption which is to be verifiedwith the aid of empirical data. Within theframework of respective quantitative orstandardized social research one means aboveall a presumption which can be submitted to astatistical test. This presumption is mainlydirected towards the fact that a connectionexists between two attributes or that a

difference exists between two groups. But thereare numerous further hypotheses possible, suchas about a particular form of connection (linear,exponential, etc.); cp. Ludwig-Mayerhofer(2004).

411. Cp. Kromrey (2002, p. 48).412. Whilst under the term exploration the first

empirical and theoretical orientation within oneresearch area is often understood, the presentcase is to be based upon Wollnik’s definition, inwhich exploration can generally be considered tobe informational exhaustion of systematicallygained knowledge for the purpose of theoryformation; cp. Wollnik (1977, p. 42).

413. For a similar approach within the framework ofan empirical examination, cp., roughly, Schäfer(2002, p. 91).

414. Cp. to this end also Geimer and Becker (2001b, p.129), Meyr et al. (2002a, p. 50), Schary andSkjott-Larsen (2001, p. 18), and Grünauer,Fleisch, and Österle (2000, p. 177).

415. For the term competence or in the present casethe Supply Chain (SC) competence respectively,

see the explanations at the beginning of chap. 1,sect. 1.7.

416. This interpretation is also supported by Schoder;see Poluha (2005).

417. Hypothesis-investigative examinations are carriedout with the primary objective of developing newhypotheses in a relatively unresearchedscientific examination area, or to createconditional terms; cp., roughly, Bortz (1984, p.26).

418. Cp. Heck (2004, p. 15).419. This deficit and the requirement for a

scientifically-founded examination of themodel’s structure resulting from it are also seenas relevant by Seibt. He assumes, consistentwith the requirements of exploratory andhypotheses-testing examinations, that thesubmitted empirical examination is a first step inthis direction which may be grasped, deepenedand expanded by further studies; see Poluha(2003b).

420. Thereby the aspect of a variety of differentscientific and business practice objectives is

addressed.421. According to the so-called Freedom of Value

Judgement Posit (Werturteilsfreiheits-Postulat)of empirical science, evaluations pose a problemfor the scientific concept of critical rationalismfrom several viewpoints; cp. to this endKromrey (2002, p. 77). The Value NeutralityPosit (Wertneutralitätspostulat) refersexclusively to the association in reasoning whichdescribes the methodical steps with whose helpa problem is to be examined. To this end and tothe differentiation between the associations todiscovery, reasoning, as well as evaluation andeffects in research, cp. Friedrichs (1990, p. 50).

422. Cp. Bolstorff and Rosenbaum (2003, p. 49). SCEalso uses the term SCORcard and defines it asfollows: “The resulting SCORcard provides adirect connection to the balance sheet.Performance requirements are established withrespect to your competition and are prioritizedby both definitions of a supply chain – productand channel. These priorities will help in thedesign phase of a SCOR project. The

SCORcard also summarizes actual performanceagainst benchmark performance with a gapanalysis that defines the value of improvements”(SCE, 2004, p. 8).

423. See to this end chap. 1, para. 1.5.4.424. Cp. Supply-Chain Council (2003a, p. 7).425. Cp. Supply-Chain Council (2003b, p. 242), Geimer

and Becker (2001b, p. 129), and SIMTech(2003, p. 3).

426. As to the problematic and meaning of theconditions of adherence to delivery, cp., forexample, Geimer and Becker (2001b, p. 131).

427. For an overview of the term ManagementInformation System as well as related termssuch as Business Intelligence, etc., cp. e.g.,Kemper (1999, p. 25).

428. For the terms indirect and direct costs, thepreviously explained terms of fixed and variablecosts are also used; cp. for instance Albach(2001, p. 252) and Schierenbeck (2003, p. 233).

429. Cp. Supply-Chain Council (2003b, p. 6). See tothis end also Diag. 1-2 in chap. 1 and diag. 2-4in chap. 2.

430. Cp. Geimer and Becker (2001b, p. 128). Asopposed to these authors, the author of the workat hand uses the aforesaid term Supply Chaincosts instead of the originally quoted termLogistic costs. This change was necessary dueto reasons of consistency resulting from thedifferentiation between the terms Logistics andSupply Chain made in chap. 1.

431. Cp. Sürie and Wagner (2002, p. 33).432. Cp. Bovet and Martha (2000, p. 43). See to this

end also chap. 1, sect. 1.7.433. Cp. Heinzel (2001, p. 54), Supply-Chain Council

(2003b, p. 9), and Kanngießer (2002, p. 9).434. In this way, for example, the order completion

performance measures the total number ofproducts delivered complete and on-timely at thedesired time. Beneath this performance indicatorwe find, amongst other things, a performanceindicator which measures the backorders value.For the allocation of Performance Measures tothe Level 1 Metrics and furthermore to thePerformance Attributes, cp. for example,Bolstorff and Rosenbaum (2003, p. 51) and

Christopher (1998, p. 107).435. The exact definitions and calculative formulas of

the performance measures may be taken fromthe appendix (see sect. 2 and 3 for an overview,and sect. 4 for the respective detailedinformation).

436. A quantitative examination was given preference,because in the SCOR model’s case we aredealing with a relatively known subject matter inprincipal, and a basis for universally validknowledge and vocabulary was to be expected;cp. Lamnek (1995, p. 17) and Schnell, Hill, andEsser (1992, p. 389).

437. Reiner and Hofmann were faced with a similartask within the framework of an empirical studyand also decided to build upon the SCOR modelfor the following reasons: “Benchmarkingmethods for process improvements are mostlydeveloped and introduced by practitioners. Manypractitioners use simple techniques rather thananalytical solution methods. That is why a strongdemand for effective methods of analyzingbenchmarking results that can be used for

design, analysis and improvement of processesexists. (…) It is important to use an adequatePerformance Measurement System forempirical research. We use a widely acceptedindustry standard, the Supply Chain OperationsReference (SCOR) model developed by theSupply-Chain Council (SCC)” (Reiner andHofmann, 2004, p. 2).

438. Cp. BearingPoint (2003c, p. 5). The so-calledstrategic triangle describes three decisivefactors in competition: Costs, time and quality;cp. Thaler (2003, p. 12). The productivity isdetermined by the cooperation and prioritizationbetween these three factors, i.e., it is a result ofthose.

439. Under the term Diagnosis Zetterberg understandsthe description of an object or problem withinthe framework of a limited number of definitionsand dimensions; cp. Zetterberg (1967, p. 67).Cp. also Friedrichs (1990, p. 108).

440. In this context, a measure could be considered tobe the improvement in correlation betweenrequest date, commit date and delivery date; cp.

Supply-Chain Council (2003a, pp. 15, 20).441. The shortcomings mentioned are to be clarified by

several examples, whereby the list would enableitself to be further continued: Paul uses theterms Metrics and KPIs synonymously; cp. Paul(2002, p. 19). Christopher applies the termintegrated supply chain metrics for the measuresdescribed by the Supply-Chain Council asPerformance Attributes; cp. Christopher (1998,p. 106) and Supply-Chain Council (2003a, p. 8).Werner states that the respective procedures inthe standardized Supply Chains are measured bymetrics within the SCOR model; cp. Werner(2002, p. 16), whilst Thaler uses the termmeasures in the same connection; cp. Thaler(2003, p. 48). And Zeller applies the termsreference numbers, measurement numbers andmetrics synonymously, and uses the termreference number class to mean performanceattributes; cp. Zeller (2003, pp. 12, 19).

442. See Poluha (2004a).443. Cp. Ossola-Haring (2003, p. 167).444. Cp. Backhaus, Erichson, Plinke, and Weiber

(2003, p. 355). Possibilities of immediatecollection and analysis of statistical data for suchhypothetical constructions in conjunction withthe Meta theses will be dealt with later in chap.3 (see para. 3.4.2.3) as well as chap. 4 (seepara. 4.3.2).

445. Cp. Preißner (2003, pp. 178, 195). See to this endalso the explanations under chap. 2, para. 2.1.4.

446. Ibid.447. Cp. Schumann (2001, p. 105). See also chap. 1.

para. 1.5.3.448. Schäfer and Seibt describe the issue as follows: It

is not the metrics (stating how large theidentified distance from other companies) thatstand in the foreground of processbenchmarking, but rather the practices whichlead to exemplary results. Only by knowledge ofthe superior practices can changes be initiatedwhich lead to the improvement of ones ownbusiness processes; cp. Schäfer and Seibt (1998,p. 27).

449. See to this end also the statements in chap. 1,sect. 1.7 for the Supply Chain drivers and

Supply Chain competences.450. Cp. Ayers (2002b, p. 657).451. Cp. Geimer and Becker (2001b, p. 129).452. Cp. Ayers (2002b, p. 657).453. Background information: “Headquartered in

Round Rock, Texas, Dell is a premier providerof products and services required for customersworldwide to build their information technologyand Internet infrastructures. Dell’s climb tomarket leadership is the result of a persistentfocus on delivering the best possible customerexperience by directly selling standard-basedcomputing products and services. Revenue forthe last four quarters totalled $45.4 billion andthe company employs approximately 50,000team members around the globe” (Dell, 2004).

454. The cash-to-cash cycle time shows the timerequired by a business until a certain amount hasflowed back into it that was spent upon materialprocurement; cp. roughly, Supply-Chain Council(2003b, p. 262).

455. Cp. Ayers (2002b, p. 659).456. See to this end also chap. 1, sect. 1.7.

457. Cp. Hoover et al. (2001, p. 9).458. Cp. Hugos (2003, p. 37).459. Cp. Meyr et al. (2002a, p. 52).460. Cp. SIMTech (2003, p. 10).461. Ibid.462. Cp. PMG (2002, p. 2).463. (Schary & Skjott-Larsen, 2001, p. 18). Cp. to this

end also Grünauer et al. (2001, p. 177).464. For the method of procedure, cp., for example,

Rochel (1983, p. 143).465. Basically a differentiation can be made between

the following types of variable relationships:Deterministic (if X, then always Y) or statistical(if X, then probably Y); reversible (if X, then Y;if Y, then X) or irreversible (if X, then Y; if Y,then not X); sufficient (if X, then always Y) orconditional (if X, then Y, but only if Z ispresent); necessary (if X, then and only then Y)or substitutable (if X, then Y; but if Z, then alsoY); cp. Zetterberg (1967, p. 82) and Friedrichs(1990, p. 105).

466. For an overview of the approaches represented inthe following, as well as further approaches in

this respect, cp., for example, Barker (1993, p.4), Kiesel (1996, p. 55), Gleich (1997, p. 358),Link (1998, p. 185), and Erdmann (2003, p. 59).

467. Cp. Rummler and Brache (1990, p. 143).468. Cp. Neuhäuser-Metternich and Witt (1996, p.

266).469. Cp. Sellenheim (1991, p. 51).470. Cp. Beischel and Smith (1991). A very similar

approach is followed by Taylor and Convey(1993, p. 23).

471. If one attempts to systematize the possibleobjective ideas which can influence the objectivefunctions of a business, a fundamentaldifferentiation between monetary and non-monetary objective ideas is available. Under theterm monetary (or financial) objective ideas, oneunderstands objectives that enable themselves tobe measured in monetary units, i.e., the strivingtowards profit and turnover. Further monetaryobjective ideas are for example ensuring liquidityand maintenance of capital. In opposition to this,the non-monetary objectives contain figuresreflecting the achievement of certain growth or

productivity targets, the striving for increase inmarket share, or the insurance of particularrequirements in quality; cp. Wöhe (1984, p.110).

472. See to this end chap. 2, sect. 2.2.473. Cp. Fisher (1995, p. 195).474. Cp. Fickert (1993, p. 208).475. Cp. Fisher (1995, p. 196).476. Cp. Welz (2005, p. 6).477. Cp. Greene and Flentov (1990, p. 53).478. The Return on Assets (ROA) is identified by

setting the net profit against the total assets.Because the asset profitability represents thereturn of interest on the capital invested inassets into the business, it is more capable oftestimony than the Return on Equity (ROE); cp.,for example, Wöhe (1984, p. 48). An exactdescription of the distribution can be taken fromchap. 4, sect. 4.1.

479. Cp. Berliner and Brimson (1988, p. 161).480. Cp. Epstein and Manzoni (1997, p. 29; 1998, p.

181).481. Cp. Sellenheim (1991, p. 52).

482. Cp. Beischel and Smith (1991, p. 25). A verysimilar method of procedure is described byTaylor and Convey (1993, p. 22).

483. Cp. Hendricks et al. (1996, p. 21).484. Cp. Edvinsson and Malone (1997, pp. 68, 82). For

information about the Skandia-Navigator, q.v.Skandia (2005).

485. Cp. Brokemper (1995, p. 242) and Werner andBrokemper (1996, p. 165).

486. Cp. Keegan, Eiler, and Jones (1989, p. 45).487. Cp. McNair, Lynch, and Cross (1990, p. 28) and

Lynch and Cross (1995, pp. 66, 72).488. See chap. 1, para. 1.5.4.489. Cp. Kaplan and Norton (1992, p. 71). With

reference to the Balanced Scorecard see alsothe statements in chap. 1, para. 1.5.4. For asimilar categorization, cp. also Horváth andLamla (1995, p. 82) and Wuest and Schnait(1996, p. 101).

490. Jacob roughly states that at the end of 1999, about60 percent of all US-American businesses hadalready introduced the BSC; cp. Jacob (1999, p.44).

491. Cp. Hronec (1996, pp. 17, 32).492. See to this end chap. 1, para. 1.5.4.493. Cp. Heinzel (2001, p. 53), Hagemann (2004, p.

10) and Heck (2004, p. 13).494. See chap. 3, para. 3.3.5.495. Cp., roughly, Bolstorff and Rosenbaum (2003, p.

49).496. The 3Com Company has for example developed

and assigned a so-called Supply chain Scorecardfor 3Com, which is based explicitly upon theSCOR model, cp. Cohen and Russel (2005, p.213).

497. As at beginning of 2007.498. See chap. 6, sect. 6.2.499. For the general method of procedure for

hypotheses formulation, cp., for example,Friedrichs (1990, p. 103).

500. As to the derivation of the fundamental coherenceof effects see chap. 3, para. 3.1.2.1.

501. Ibid.502. Ibid.503. As to the derivation of the fundamental coherence

of effects see chap. 3, para. 3.1.2.1.

504. As to the derivation of the fundamental coherenceof effects see chap. 3, para. 3.1.2.2.

505. Ibid.506. Only an indirect coherence exists between the

performance measures in so far as a lowinventory level ties less capital, which concealsin itself a lower risk of inventory obsolescence.It can, however, lead on the other hand toinsufficient delivery readiness. This in turn canresult in dissatisfied customers and have as aconsequence that the cash-to-cash cycle timeincreases; cp. Schumann (2001, p. 99). Theassumption of such derived customer-orientatedbehavioral patterns within the Supply Chain’scontext are not accommodated by the SCORmodel and are therefore not to be researchedany further in the submitted work. For acoherence between these two fields of research,Supply Chain Management and customerbehavior, see e.g., Best’s fundamental work ondeterministic demand fluctuations in SupplyChains; cp. Best (2003).

507. See the statements under the previous footnotes,

which may be used in analogue in the presentcase.

508. As to the derivation of the fundamental coherenceof effects see chap. 3, para. 3.1.2.3.

509. Ibid.510. Ibid.511. The cash-to-cash cycle time represents by

experience a good measure for the evaluation ofthe efficiency in the order transaction process.However, no (direct) dependency allows itself tobe derived between the order transactionperformance and the asset turns; cp. Geimerand Becker (2001b, p. 130).

512. See the statements under the previous footnote,which may be applied in analogue in the presentcase.

513. Ibid.514. Ibid.515. As to the derivation of the fundamental coherence

of effects see chap. 3, para. 3.1.2.3.516. The cash-to-cash cycle time represents by

experience a good measure for the evaluation ofthe efficiency in the order transation procedure.

However no (direct) dependency allows itself tobe derived between the order transactionperformance and the asset turns; cp. Geimerand Becker (2001b, p. 130).

517. Cp. Schäfer and Knoblich (1978, p. 248).518. Cp. Klingemann and Mochmann (1975, p. 178).519. Cp. Rogge (1981, p. 49).520. Because in the case of a representative survey

the examination is in any case supposed toprovide characteristics about the population, theselection of the sample must take place in sucha way as to enable an exact and safetransposure of the results onto the entire mass.That is the case if the sample is representative,whereby representability is consideredsatisfactory with regards to the characteristicsrelevant to the examination. A partial mass isrepresentative if it conforms to the total mass asfar as the distribution of all characteristics ofinterest is concerned, i.e., represents albeit asmall, but true-to-reality illustration of thepopulation; cp. Berekoven, Eckert, andEllenrieder (1987, p. 42) and Kromrey (2002, p.

257).521. Cp., roughly, Selg and Bauer (1976, p. 87).522. In both cases the partial mass forming the

survey’s basis should represent a restricted,however proportionally true illustration of thepopulation (so-called pars pro toto); cp. Monkaand Voß (2002, p. 299).

523. As far as methods of non-probability sampling areconcerned, those are often inapplicable forstatistically controlled scientific statements. Inthe case of random sampling methods, theextraction of samples is completely arbitrary.Simple sampling and multi-stage sampling can benamed in this connection; cp. Schäfer andKnoblich (1978, p. 255) and Friedrichs (1990, p.130).

524. Hammann and Erichson thereby subdivide furtheras follows: 1. Types within random sampling aresimple random sampling, stratified sampling,cluster sampling and multi-staged sampling. 2.Non-probability sampling (or also procedures ofpurposive sampling) are e.g., quota sampling,snowball sampling and sampling of typical cases;

cp. Hammann and Erichson (1990, p. 108) andLudwig-Mayerhofer (2004).

525. Cp. Berekoven et al. (1987, p. 51).526. For the principle of the representation of similar

type cases, cp., roughly, Kromrey (2002, pp.257, 273).

527. See also to this end the questionnaire in theappendix, sect. 1.

528. The unsystematic surveys, also calledopportunistic surveys, mainly allow themselvesto be completed in conjunction with otherbusiness procedures. The systematic surveyshave an exactly defined examination objectiveand must be designed and prepared according toa plan. A division between a unique survey anda repeated survey is given by the characteristicsof frequency and consistency of the survey. Inthe case of single surveys, also known as ad-hoc surveys, a very particular topic is examined.They therefore have a kind of diagnosticcharacter, but also come under scrutiny toclarify relationships before important decisionsare made; cp. Schäfer and Knoblich (1978, p.

247) and Friedrichs (1990, p. 192).529. Cp. Hammann and Erichson (1990, p. 78).530. Cp. Bortz (1984, p. 180) and Berekoven et al.

(1987, p. 112).531. For an example of an online questionnaire see

section 5 of the appendix.532. See to this end also the questionnaire in section

1of the appendix.533. This topic will be reiterated in connection with the

course of the examination in para. 3.4.1.1.534. Cp. Schnell et al. (1992, p. 367).535. The so-called scale question is an exemplary sub-

form of the closed question. Here the personsinterviewed are supposed to express theiropinion about the intensity of a factual situation,which are provided in (verbally formulated)staged categories or as a continuum. In thisway, an ordinal measurement of amounts orfrequencies is enabled. Alternative or scaledquestions are also described as selectionquestions, since only one answer is possible. Thesimplest form of alternative question is the Yes-No Question. In the case of the so-called

catalogue questions, the selected personreceives a number of qualitatively varyinganswer possibilities, which allow themselves tobe allocated to a continuum (multiple-choice).As soon as they exclude each other, selectivequestioning is the case. Occasionally, more thanone answer is permitted, one speaks in this caseof multiple selection questions. In all the abovementioned cases, the suggested answers cantake the form of catch-words or wholesentences; cp. Müller-Böling and Klandt (1996,p. 42) and Friedrichs (1990, p. 236).

536. This refers to the handy format, the clear and yetoptimal use of space for the questions, and thereservation of enough free space for theprotocolling of the answers (as long as theprinciple of multiple-choice is not followed); cp.Schäfer and Knoblich (1978, p. 294).

537. The complete questionnaire can be taken from theappendix (see sect.1), in addition to an exampleof the online data entry screen (see sect. 5).

538. For information regarding the consultancyBearingPoint, see chap. 2, para. 2.4.2.3.

539. As to the changes since SCOR version 4 seechap. 2, para. 2.1.5.

540. See to this end the statements under para. 3.3.2.541. Guß and Walther for example quote a study

conducted on Supply Chain Management inGermany and Switzerland by the businessconsultancy Deloitte Consulting in cooperationwith the Fachhochschule (University of AppliedSciences) of Braunschweig/Wolfenbüttel inGermany in the period between 1999 and 2000.Within the framework of the study, around 70questionnaires were finally evaluated andinterpreted, whereby no further differentiationaccording to region, industry, business size andso on, were undertaken; cp. Guß and Walther(2001, p. 159). The study was identical to thework submitted with respect to the radius of therandom sampling, as well as the selectionmethod. For a similar study carried out in theNorth American region, cp. Deloitte (2000, p.2).

542. A description of the exact distribution is repeatedin chap. 4, sect. 4.1.

543. Ibid.544. For the division of companies according to size

classes according to HGB (GermanCommercial Code), cp. Schierenbeck (2003, p.37). A description of the exact distribution isrepeated in chap. 4, sect. 4.1. The differences innumbers result from the fact that in the case ofthe above listing, both criteria for the allocationto a size class (by revenue and number ofemployees) had to be simultaneously fulfilled.

545. For the term Return on Assets (ROA), see theexplanations under the corresponding footnote inchap. 3, para. 3.1.2.5.

546. A popular suggestion for the objective ROA forindustrial businesses lies on average around 10percent; cp. Controlling Portal (2005). Inaccordance with this for example a roughdefinition can take place: Companies with anegative ROA are “losers”, such with a positiveROA up to 10 percent are “average”, andcompanies with a ROA larger than 10 percentare “winners”; cp. Aktien-Portal (2005). Therange should accommodate for industrial or

regional fluctuations; cp. Deutsche Bundesbank(1997, p. 35).

547. The information results from research conductedby the author in July 2006.

548. Cp. Hinkelmann (2003, p. 14) and Supply-ChainCouncil (2005a, p. 21).

549. This is in accordance with the test conceptsrepresented by methodicians such as Rochel, bywhich – in the sense of a hierarchical structure– in the first instance statements as to thegeneral suitability of model images are to beextricated (main effects); cp. Rochel (1983, p.118). Adjustment was also strived for in thework at hand. All the same, in order to be just tothe exploratory character of the work, thepossible explanatory value of the additionalvariables – i.e., sales volume and number ofemployees – was examined. Variables with avery heterogeneous structure, e.g., industry-affiliation, were not suitable for this observation,because the respective case number would havebeen too low. Variables with certain stronglyoutweighing characteristics which – due to this

fact - did not allow a sufficient differentiation asin the case of Return on Assets (ROA), werealso only restrictedly suitable.

550. This does not make exempt from the necessity todeepen the examinations of restriction andparticularities of the model image by means offurther discriminating parameters in new and, ifnecessary, specific areas (see to this end alsothe statements within the following in addition tothe notes in chap. 6, sect. 6.3).

551. For the various Supply Chain strategies, cp., forexample, Werner (2002, p. 64). For the termmass customization see the explanation in chap.1, para. 1.4.

552. Cp. Supply-Chain Council (2005b, p. 2).553. See to this end the explanations in chap. 2, para.

2.1.3.554. See to this end also the aforementioned notes on

the subject of test concepts in context with thedifferentiation by strategy types and industry-affiliation supported by Rochel.

555. The company size based upon the aforementionedthree-stage classification by employee numbers

and revenues according to the HGB (GermanCommercial Code) lead in most cases toidentical allocation (see the detailed descriptivetable in chap. 4, sect. 4.1). In the case of theReturn on Assets it is to be restrictively notedthat this lay in the “average” area between 0and 10 percent for the greater majority ofcompanies and was therefore only conditionallyapplicable for the discrimination; see to this endin the same way the descriptive table illustratedin chap. 4, sect. 4.1. A differentiation accordingto the respective business area or region wasnot suitable as a discriminating variable, becausebusinesses in North America dominated withroughly three quarters of the sample, and therest of the companies stemmed from a varietyof regions. The SCOR model positions itselfabove this in a similar way to industry-affiliation,i.e. it follows the intention of staying on onelevel, upon which region specific factors are nottaken into consideration; cp. Supply-ChainCouncil (2006b, p. 2).

556. For background information on BearingPoint, see

chap. 2, para. 2.4.2.3.557. A method can be defined as a systematic

procedure or rules applied to complete a numberof activities in order to achieve certain structuralobjectives. A questionnaire or survey can beseen as a method in this sense. In opposition tothis, a process represents a more refined ormature method which regulates the approach tothe achievement of particular structuralobjectives down to the level of the individualstages of work; cp. Seibt (2004, p. 19).

558. Cp. BearingPoint (2003b, p. 4).559. Cp. BearingPoint (2003b, p. 21).560. As to the problematic of the return quota, cp., for

example, Bortz (1984, p. 184) and Friedrichs(1990, p. 241).

561. The questionnaire and the exact questions may betaken from the appendix (sect. 1).

562. The associated Key Performance Indicators(KPI) and their calculation may be taken fromthe appendix (sect. 4).

563. For an example of representation of the resultssee sect. 5 of the appendix.

564. For the varying graphical possibilities ofillustration, cp., for example, Wöhe (1984, p.1245).

565. Cp. BearingPoint (2003b, p. 26).566. Cp. Kromrey (2002, p. 405).567. Cp., roughly, Friedrichs (1990, p. 54).568. For this purpose refer to chap. 5 and chap. 6.569. Cp., for example, Berekoven et al. (1987, p. 162).570. For further information as to the product range in

general and in particular SPSS for Windows,cp., roughly, SPSS (2003, p. 2; 2004b).

571. With respect to the application of programs fordata analysis, such as SPSS, cp., for example,Allerbeck (1972). For the application of SPSS inparticular, cp., for example,Bühl and Zöfel(2002) and Backhaus et al. (2003, p. 15).

572. Cp. to this end Bortz (1984, p. 534), Friedrichs(1990, p. 136), Kromrey (2002, p. 424), Voelker,Orton, & Adams (2001, p. 23) and Ludwig-Mayerhofer (2004). As to the procedures ofstatistical data evaluation, cp. also Ehrenberg(1986).

573. No further consideration is given here to the

median as, above all, a suitable measure ofcentral tendency for ordinal scales.

574. Measures of location are supposed to giveinformation as to where the focal point of a one-dimensional data package lies. They aretherefore also known as the Measures of centraltendency; cp. Monka and Voß (2002, p. 98).

575. During a statistical test, a so-called null hypothesisis set-up which generally states that thepostulated correlation or difference does notexist. A test statistic is calculated whichconfirms whether a connection or differencefound within the data is compatible with the nullhypothesis. If the test statistic exceeds aparticular, previously determined value, the nullhypothesis is rejected and the actual researchhypothesis, i.e., the alternative hypothesis, isvalid as tentatively not proven to the contrary;cp. Voelker et al. (2001, p. 62).

576. In addition to the Type I Error, there is a Type IIError which describes the erroneous retention ofa null hypothesis. It therefore reflects the risk ofretaining a null hypothesis, even though in

actuality the alternative hypothesis is applicable;cp. Ludwig-Mayerhofer (2004). The type IIerror is not given any consideration within thefurther course of the work.

577. The letter P describes the confidence level(Probability), with which the hypothesis issupposed to be accepted as correct orapplicable. The probability of error is thencalculated as P(a) = 100% - P; cp. Gottwald(2000, pp. 51, 61).

578. The covariance describes the correlation betweentwo metrical characteristics. It is identified bythe fact that for each value of both variables, thedeviation from the respective arithmetic mean iscalculated (by subtraction of the same). Foreach case, the product of these two deviations isformed, the products added together and thendivided by N-1 (where N represents the numberof cases). In conjunction with this, it is to benoted that the calculation of the covariance isonly meaningful with metrical variables; cp.Backhaus et al. (2003, p. 340).

579. As to the approach, cp., for example, Rochel

(1983, p. 143).580. Cp., for example, Bortz (1985, p. 156) and

Friedrichs (1990, p. 389).581. Cp., for example, Ludwig-Mayerhofer (2004).582. Inferential Statistics, also known as investigative,

inductive or conclusive statistics, deal with thequestion as to how the results of a sample, i.e. achoice of examination units can be transposedupon a population from which the sample stems;cp. Monka and Voß (2002).

583. Descriptive Statistics deal with measurements tocharacterize data. They have the task ofanswering such questions as “how can thecentral tendency of a data package becharacterized in the same way as thediffusion?” or “ how can correlationscharacterized between two or more variables bepackaged like data?”; cp. Gottwald (2000, p. 4).

584. For the various possibilities of graphical illustrationof statistical results, cp., for example, Voelker etal. (2001, p. 8) and Wöhe (1984, p. 1245).

585. Cp., roughly, Ehrenberg (1986, p. 48). For furthergoing explanations as to the procedures used for

the evaluation and statistical principles, cp.Cramer (1999).

586. For reference to such a problematic, cp., forexample, Bortz (1985, p. 156).

587. Cp. Berekoven et al. (1987, p. 162).588. For example, it would play a role during a group

of thesis tests whether all the theses lie withinthe totally unsystematic R-range at an absolutevalue of 0.00 or all within the model-conformantrange with a “near” miss of the significancecriterion. If the significance criterion wasexclusively applied the conclusion would, though,be the same in both cases, namely that ofunsystematic diagnoses. Even in the latestbusiness administration essays whosehypotheses investigations are mainlycorrelatively directed a similar differentiation isquite positively undertaken by unequivocalhypotheses-conformant and hypotheses-tendencial diagnoses; cp. to this end Hannappel(2005, p. 129).

589. See to this end chap. 4, para. 4.2.3.3 and chap. 5,para. 5.1.1.

590. For an example of the assignment of the statedmethod see roughly Madeja and Schoder (2003,p. 4). For further going explanations ofStructural Equation Models (SEM), cp. alsoByrne (1998) and Kline (1998).

591. With reference to the AMOS procedure, cp., forexample, Byrne (2000) and SPSS (2004a, p.142).

592. Cp. Backhaus et al. (2003, p.11) and SPSS(2004c).

593. LISREL stands for Linear StructuralRelationships. With reference to the LISRELmodel, cp., for example, SSI (2000) and Hayduk(1987).

594. Cp. SSI (2004).595. Cp. Backhaus et al. (2003, p. 334). For the

subject matter of applicability of structure-analytical procedures, cp. also Hoyle (1995) andSchumacker and Lomax (1996).

596. See chap. 4, sect. 4.3.597. The stability index, also known as the

Determination Coefficient, measures so to speakthe compatibility quality of the regression

function to the empirical data (goodness of fit).The residual measures build the basis for this,i.e., the deviations between the values observedand the values estimated; cp. Gottwald (2000, p.114).

598. The Adjusted-Goodness-of-Fit Index (AGFI)additionally takes into consideration the numberof degrees of freedom in comparison to a so-called neutral model. The degree of freedom(df) is thereby the submitted number of data (N)within a row of data, lowered by 1. The numberof degrees of freedom represents the number ofindependent measurement values in a row ofdata. It must be noted that in the case of astructure-analytical method of approach, thedegrees of freedom are calculated according toa more complex scheme (case number- andvariable-related) than the conventional statisticsreferred to, for example, within the frameworkof variance analysis; cp. Gottwald (2000, p. 25).

599. Cp. Backhaus et al. (2003, p. 374).600. Cp. Friedrichs (1990, p. 53).601. Cp. Gottwald (2000, p. 3).

602. Cp. to this end Kromrey (2002, p. 405).603. The aspect of compatibility of theoretical

statements and empirical reality (in the presentcase more exactly: business management andcorporate practice respectively) will be returnedto in chap. 5, para. 5.1.3 within the context ofproximity to truth and the correspondencetheory.

604. The mitigation of this risk in the examination beingimplemented here has already been previouslydiscussed (see chap. 3, para. 3.3.5).

605. For a detailed list of various types of artifactsources during the construction of testingprocedures and their application, reference is forexample made to Wottawa (1980, p. 208).

606. Cp. Kromrey (2002, p. 359). Cp. also Alemann(1977, p. 209).

607. In this context see also the explanations in chap.3, sect. 3.3.2.

608. See to this end chap.C, para. 3.3.3.609. This conclusion also has implications for the

discussion of theses which are not able to beconfirmed. These types of “non-confirmability”

can arise due to (a) artifacts or respective lackin statement strength of the data or (b) aninsufficient coverage of company reality by thenot so extensively examined SCOR modelillustrations (i.e., content changes or necessityfor expansion of the model illustrations). In thework at hand, both possibilities were considered,whereby the position of origin was that – undercritical observation of the indicated restrictions(see to this end also the explanations in chap. 3,paras. 3.3.2 & 3.3.5) – the validity of the data ispresent and must therefore outweigh thediscussion about content.

610. For the term interaction within statisticalmethodology and for the various investigativeprocedures, cp., for example, Rochel (1983, p.60).

611. In the sense of critical rationalism of researchlogic, respective statements or hypotheses mustprincipally be able to fail by experience, i.e., be“falsifiable” (falsification criterion); cp. Popper(1989, p. 15). In accordance with this the termconfirmed can be equalled to tentatively not

falsified.612. The interpretation of “meaningfulness” of a

correlation coefficient depends substantiallyupon the content aspects of an empirical survey.Voelker et al. describe the connection asfollows: “Whether a correlation of a givenmagnitude is substantively or practicallysignificant depends greatly on the phenomenonbeing studied” (Voelker et al., 2001, p. 101).Therefore in the submitted case and in the faceof a level of R = 0.30, substantial correlationscould be assumed for graphical illustrationpurposes which were significant on the“advanced” P(a)-level. A critical reflection ofthe correlation-analytical findings of theinvestigation will in any case be undertaken atthe end of the chapter within the context of thediscussion on possible errors and interferinginfluences.

613. Calculated based on BearingPoint (2003f).614. Ibid.615. Calculated based on BearingPoint (2003f). For

the classification according to HGB (German

Commercial Code), cp. Schierenbeck (2003, p.37). The values quoted were calculated bymeans of the exchange rate as at July 2006; cp.Tiago Stock Consulting (2006).

616. Calculated based on BearingPoint (2003f). Forthe classification according to HGB (GermanCommercial Code), cp. Schierenbeck (2003, p.37).

617. Calculated based on BearingPoint (2003f). Forthe calculation of the Return on Assets (ROA),see chap. 3, para. 3.3.5.

618. UoM abbreviation for the Unit of Measure.619. x abbreviation for Arithmetic mean.620. s abbreviation for Standard deviation.621. Min abbreviation for Minimum.622. Max abbreviation for Maximum.623. V abbreviation for Variation range.624. By rounding up or down.625. Ibid.626. Ibid.627. Ibid.628. Ibid.629. Calculated based on BearingPoint (2003f).

630. By rounding up or down.631. Calculated based on BearingPoint (2003f).632. Due to concepts of displacing inventory

management and the associated costs, as forexample the Vendor Managed Inventory (VMI)previously mentioned.

633. By rounding up or down.634. Calculated based on BearingPoint (2003f).635. By rounding up or down.636. Calculated based on BearingPoint (2003f).637. Correct extreme values due to various company

adjustments.638. Ibid.639. Calculated based on BearingPoint (2003f).640. As to the Product-Moment Correlation (PM-

Correlation) and the associated Bravais PearsonCorrelation Coefficient (R), see the explanationsin chap. 3, para. 3.4.2.2.

641. N represents the number of total cases that havebeen examined, i.e., the sample size.

642. R represents the Bravais Pearson CorrelationCoefficient which has been explained in detail inchap. 3, para. 3.4.2.2.

643. P(a) stands for the probability of error and iscalculated as P(a) = 100% - P, with P for theconfidence level (Probability); for detailedexplanations see chap. 3, para. 3.4.2.2.

644. M abbreviates the SCOR model group that isassociated to the respective coherence. In thisconjunction, the following classifications aredifferenciated – analogues to the structure ofthe developed theses: I-P = Intra-PerformanceAttribute, I-C = Intra-Competence, I-CP =Inter-Competency/Performance Attribute; fordetailed explanations, see chap. 3, para. 3.1.2).

645. For the differing cases of variable relationshipssee the respective footnote under chap. 3, para.3.1.2.4.

646. The abbreviation n in this as well as the followinggraphics stands for number as a percentage ofthe respective group (as opposed to the totalamount of examined cases represented by thecapital letter N).

647. With this grouping – as also in the case of thefollowing illustrations – a similar occupation wasstrived for to the “detriment” of similar interval

widths. For detailed indications as to thegrouping and determination of the interval widthor the requirement for balance, cp. also Bortz(1985, p. 35).

648. The variance clarification of the criterion variablepercentage of purchase orders received on timeand complete was visibly increased into amultiple regression by the introduction of therespective predicates company revenue ornumber of FTE. More simply expressed, thisindicates a more meaningful correlation for thelarger companies whereby, however, criteria ofstatistical significance were not achieved (see tothis end also the respective notes as to theexamination of the incremental function underthesis 1).

649. The parameter on-time deliveries was lowered tothe ordinate here for reasons of clarity.

650. The variance clarification of the variablepercentage of purchase orders received on timeand complete was visibly increased into amultiple regression by the introduction of therespective predicates company revenue or

number of FTE. This indicates a moremeaningful bivariate (here negative) correlationfor the larger companies, whereby howevercriteria of statistical significance were notachieved (see to this end also further goingnotes as to the examination of the incrementalfunction under thesis 1).

651. Here the variance clarification of the criterionvariable percentage of purchase orders receivedon time and complete was also visibly increasedinto a multiple regression by the introduction ofthe respective predicates company revenue ornumber of FTE. In this respect, a moremeaningful (negative) bivariate correlation isagain reflected for the larger companies (see tothis end also further going notes as to theexamination of the incremental function underthesis 1).

652. The variance clarification of the criterionpercentage of available inventory materialpresented itself – in a model-conformantdirection – visibly more important for companieswith smaller revenue than for larger companies.

A “non-significant” effect was therefore presentin the case of multiple regression (see to this endalso the respective notes as to the examinationof the incremental function under thesis 1).

653. The variance clarification of the criterionpercentage of purchase orders received on timeand complete was visibly increased into amultiple regression by the introduction of therespective predicates company revenue ornumber of FTE. This indicates a moreunambiguous bivariate (in this case negative)correlation for the larger companies (see to thisend also notes as to the examination of theincremental function under thesis 1).

654. By the inclusion of the predicate Return onAssets (ROA) into a multiple regression it waspossible to better highlight the above-mentionedcoherence. More simply formulated, thisindicates a more unequivocal correlation for thecompanies with an increased ROA, wherebyhowever criterion of statistical significance werenot reached (see to this end also further goingnotes as to the examination of the incremental

function under thesis 1, which may be applied inanalogue).

655. See sect. 4.2 of chap. 4.656. In this case the parameter on-time deliveries was

lowered to the ordinate for reasons of clarity.657. See to this end sect. 4-2 of chap. 4.658. By rounding up or down.659. Or respectively in the sense of the critical

rationalism of research logic: Which theses(tentatively) do not have to be rejected? In thefollowing therefore, confirmed means theequivalent to tentatively not rejected (see to thisend also the comments at the beginning of sect.4.1).

660. As more closely explained in chap. 2, para. 2.3.2,sales and marketing (demand generation),research and technology development, productdevelopment and partially after-sales customerservice fall beneath this; cp. Supply-ChainCouncil (2005a, p. 3).

661. As to the detailed examination results see para.4.1.1 of chap. 4.

662. As to the detailed examination results see para.

4.1.2.663. As to the detailed examination results see para.

4.1.3.664. As to the strategic square in the context of Supply

Chain Management, cp. Ellram and Cooper(1990) and Weber, Dehler, and Wertz (2000, p.264). See to this end also the respectiveexplanations in chap. 1, para. 1.3.1.

665. Cp. Fleischmann, Meyr, and Wagner (2002, p.76). See to this end also the Supply ChainMatrix with the two dimensions of Planninghorizon and Supply Chain process by Rohde etal., whereby in the present context the SupplyChain process is above all important; cp. Rohdeet al. (2000, p. 10).

666. Cp. Norek (1999, p. 381), Raman (1999, p. 174),and Chakravarty (2001, p. 372).

667. Cp. Hugos (2003, p. 96). Copacino defines a so-called Customer Service Pyramid, within whichthe reliability of the Supply Chain purelyrepresents the basis upon which the Resilienceand Creativity or respective Innovation buildwith the associated service elements; cp.

Copacino (1997, p. 74).668. Cp. Christopher (1998, p. 12). The same direction

is indicated by a collective research projectimplemented by the Institute for Supply ChainManagement of the University of Münster inGermany and the consultancy McKinsey in theyear 2003, in which Supply Chain Managementwas examined by 40 of the 74 largest Germanconsumer goods manufacturers and 18 of the 40largest German retail companies. During this, itbecame apparent that low costs and goodservice must no longer exclude each other assuch. The authors use the term Supply ChainChampions for companies that perform betterthan the competition; cp. Behrenbeck andThonemann (2003, p. 1). For detailedexplanations see also Thonemann et al. (2003).

669. Cp. Miller (2002, p. 665).670. Cp. Christopher (1998, p. 195) and Raman (1999,

p. 182). Alt et al. presume that the concept ofthe previously described concept of VendorManaged Inventory (VMI) influences all SCORmain processes as a part of the Supply Chain

strategy; cp. Alt, Puschmann, and Reichmayr(2001, p. 106).

671. Cp. Copacino (1997, p. 15), Poirier (2000, p. 57),and Handfield and Nichols (2000, p. 7). Thalerdescribes the varying inventory stock strategiesas necessity-justified inventory logistics; cp.Thaler (2003, p. 212).

672. Cp. Kuglin (1998, p. 196).673. Cp. Miller (2002, p. 665). Copacino postulates a

customer service survey of a more generalcharacter, as he assumes that a continuouscustomer service strategy has a success-criticalrelevance; cp. Copacino (1997, p. 73).

674. Cp. Kuglin (1998, p. 256).675. Cp. Banfield (1999, p. 19). For an overview of the

strategic procurement concepts, cp., forexample, Arnold and Eßig (2000, p. 122).

676. Cp. Schäfer (2002, p. 29).677. Cp. Hoover et al. (2001, p. 27).678. Cp. Bovet and Martha (2000, p. 217).679. As to the detailed examination results see para.

4.1.1 of chap. 4.680. As to the detailed examination results see para.

4.1.2.681. As to the detailed examination results see para.

4.1.3.682. See the connection between on-time deliveries,

lines on-time fill-rate, backorders and inventorymanagement costs under customer ordermanagement.

683. Cp. Werner (2002, p. 65) and Wildemann (1992,p. 391).

684. Cp. Meyr et al. (2002a, p. 57).685. Cp. Hugos (2003, p. 168).686. Cp. Schönsleben (2000, p. 116). Ayers uses the

term production flexibility in conjunction withthis; cp. Ayers (2002e, p. 110).

687. As to the detailed examination results, see para.4.1.1.

688. Cp. Berger and Gottorna (2001, p. 73) andHandfield and Nichols (2002, p. 63).

689. As to the detailed examination results, see para.4.1.3 of chap. 4.

690. Cp. Dörflein and Thome (2000, p. 47).691. Cp. Schäfer (2002, p. 39).692. See to this end chap. 5, para. 5.1.2.

693. Without wanting to preempt further explanations,the relationship – measured upon the correlationprognosis – of applicable theses to theunsystematic and contrary theses moreoverindicates that the model is compatible with theempirical facts. Statistic special influences, asfor example the so-called Type I Error Inflation,may not be called upon in the case at hand inorder to question this fundamental compatibility;cp. to this end roughly Rochel (1983, p. 129).

694. Cp. Schönsleben (2000, p. 444) and Hugos (2003,p. 91).

695. Cp. Schäfer (2002, p. 39) and KPMG (1997, p.2).

696. Cp. Thaler (2003, p. 48).697. Cp. Ayers (2002c, p. 248).698. Cp. Geimer and Becker (2001a, p. 28).699. Cp. Werner (2002, pp. 54, 175).700. Cp. Hugos (2003, p. 144). Cp. also in this context

Hausman (2000).701. Cp. Schäfer (2002, p. 39).702. Cp. Supply-Chain Council (2003b, p. 8).703. The percentage lay (rounded off) at 61 percent.

704. The percentage lay (rounded off) at 33 percent.705. The percentage lay (rounded off) at 6 percent.706. The remaining percentage proved itself to be non-

significant (see to this end also the explanationsunder chap. 3, para. 3.4.2.2).

707. Cp. Kromrey (2002, p. 48).708. In this sense the term of a central assumption to

be examined (as opposed to a theory) wasdeliberately chosen.

709. See to this end the comments leading to this at thebeginning of chap. 3.

710. See chap. 5, para. 5.1.2.711. See to this end chap. 3, sect. 3.2.712. Details as to structural equation procedures in

general and AMOS, in addition to theirapplication specifically, may be taken from chap.3, para. 3.4.2.3.

713. Usually, for parameter estimation in conjunctionwith structure-analytical models, a sample sizeof N = 100 and partially even N = 200 aredeemed to be sufficient; cp., roughly, Loehlin(1987, p. 60). An insufficient sample size canlead to the fact that based on the structure-

analytical estimation procedure, even a modelwhich presents itself by respective dimensionalor dimensional indicator relationships is notaccepted. Ringle, in conjunction with this, pointsout a required minimum sample size of 200,which is also highlighted in methodical literature;cp. Ringle (2004, p. 16).

714. The so-called operationalization stands in theforeground here, i.e., the meaningful coverageof a total term by suitable indicators which – ifone assumes a homogeneous term orhomogeneous “construct” – must amongstthemselves logically also show closerelationships; cp. Wottawa (1980, p. 18).

715. Indicators are immediately measurable factualsituations which highlight the presence of themeant, but not directly recorded phenomena; cp.Kroeber-Riel and Weinberg (2003, p. 31).

716. It is to be conclusively noted that within theframework of the AMOS program, the approvalof the allocation of indicators (single measures)to respective postulated factors or structures orlatent dimensions is investigated in all cases.

This means that a model with inadequatestructural operationalization as a result of weakor unsystematic intercorrelation of the indicatorsin question is inevitably rejected; cp., roughly,Hair, Tatham, and Anderson (1995, p. 680).

717. For the diverse types of variable relationships seethe comments under chap. 3, para. 3.1.2.4.

718. Cp. Backhaus et al. (2003, p. 335), Wottawa(1980, p. 198), and Hodapp (1984, p. 47).

719. For closer comments on the Goodness-of-FitIndex (GFI) and Adjusted Goodness-of-FitIndex (AGFI) see chap. 3, para. 3.4.2.3.

720. It is recommended during an empirical survey toensure that as many indicator variables areaccumulated as are necessary in order to attaina positive number of degrees of freedom. Forthe solubility of a structural equation model it istherefore unconditionally necessary (mandatoryprecondition) that the number of degrees offreedom is larger, or equal to, zero; cp., forexample, Backhaus et al. (2003, p. 360). For theterm degrees of freedom in conjunction with theAGFI see also chap. 3, para. 3.4.2.3.

721. In the case of the GFI or AGFI respectively, theextent of the index lies similar to a correlationcoefficient, namely between 0 and 1. Values&60; 0.7 suggest a barely present coveragebetween model assumption and empirical data.As to the formation, relevance and controversyover these measures, cp., for instance, Hair etal. (1995, p. 686).

722. See to this end also chap. 3, para. 3.4.2.3.723. Cp., for example, Backhaus et al. (2003, p. 336).724. Cp. Hodapp (1984, p. 47).725. Cp. SPSS (2004a, p. 142).726. Single hypotheses 66 to 74 are assigned to this

Meta thesis (see chap. 3, para. 3.2.3.3). Thepartial models can be illustrated, in analoguewith further Meta theses, upon the basis of thetheses model.

727. In this case, the graphical overview orientatesitself by visualization recommendations aimed atimmediate ability to be proven by Wottawa(1980, p. 199). For a visual example of theillustration of various structure-analyticalvariables within a graphical representation, cp.,

for example, Madeja and Schoder (2003, p. 5).728. See to this end sect. 3 in the appendix.729. Cp., for example, Backhaus et al. (2003, p. 360).730. The method of Unweighted-Least-Squares

represents one of the most assigned estimationprocedures in market research in these parts;cp. to this end roughly Backhaus and Büschken(1997, p. 166).

731. Cp., for example, Backhaus et al. (2003, p. 363).732. Cp. Schewe (1996, p. 55). Cp. also to this end

Fritz (1984).733. Cp. to this end roughly Backhaus and Büschken

(1997, p. 13).734. Cp., for example, Backhaus et al. (2003, p. 408).735. See to this end chap. 6, para. 6.3.2.736. Cp., roughly, Kromrey (2002, p. 274) and Kops

(1977, p. 179).737. Cp. Friedrichs (1990, p. 335).738. The Supply-Chain Council describes the factual

situation as follows: “Level 1 Metrics areprimary, high level measures that may crossmultiple SCOR processes. Level 1 Metrics donot necessarily relate to a SCOR Level 1

process (Plan, Source, Make, Deliver, Return)”(Supply-Chain Council, 2006b, p. 8).

739. Cp. Heck (2004, p. 14).740. Cp. BearingPoint (2003b, p. 22).741. See to this end para. 4.4.2.742. Cp. Klingemann and Mochmann (1975, p. 178)

and Kromrey (2002, p. 526).743. See to this end sect. 1 in the appendix. Since it

was an online survey in this case, the interviewinstructions are restricted to exact instructionson the completion of the questionnaire.

744. See to this end sect. 3 (Personal Sources) in thebibliography.

745. Cp. Bortz (1985, p. 269). A further risk of thecorrelation-analytical procedure chosen here canmeasurement-theoretically exist in the fact thatstrongly represented bivariate-nonlinearcorrelations may, as it were, be “overlooked”.Implemented test calculations on the basis ofpolynomials, i.e., procedures unambiguouslyresting upon such non-linear processes,produced no sort of indication of relevantadditional gain in knowledge (with reference to

polynomial investigation, cp., for example, theconcrete description by Rochel (1983, p. 162).

746. Cp., roughly, Backhaus et al. (2003, p. 410).747. See to this end sect. 4.2 of chap. 4.748. See to this end chap. 3, para. 3.1.1.2.749. See Poluha (2004a).750. Cp., roughly, Moser (1975, p. 123).751. Cp. Friedrichs (1990, p. 54). In the present case,

the knowledge expansion refers to associationsrespective to business or operationsmanagement or, more precisely, Supply ChainManagement.

752. For the term Hermeneutics, cp. e.g., Ludwig-Mayerhofer (2004).

753. Cp. Kromrey (2002, p. 405).754. See to this end chap. 4, sect. 4.2.755. See to this end chap. 3, para. 3.1.2.1.756. In this context, it must be noted that the expected

parameter relationships for the performanceattribute assets were rather weakly represented.

757. The remaining percentage proved itself to be non-significant (see to this end also the explanationsin chap. 3, para. 3.4.2.2).

758. See to this end chap. 3, para. 3.1.2.2.759. The remaining percentage proved itself to be non-

significant.760. Ibid.761. See to this end chap. 3, Para. 3.1.2.3.762. For the reasons for the model-contrary

constellation see chap. 4, para. 4.2.3.763. For a possible approach at explanation see the

statements in the following para. 5.1.2. Theeffect is presumably based upon the samereason as for assets, more closely described inthe following, although it does not become sostrongly apparent with regards to the costs.

764. The remaining percentage proved itself as non-significant.

765. Ibid.766. The remaining percentage proved itself as non-

significant. Although 9 single theses were given,the presence of subdivided single theses, due tothe differentiation between an inbound andoutbound component, meant that there wereactually 12 result cases (see chap. 4, para.4.1.3.3). Of these in turn, one case proved itself

to be significantly model-contrary.767. The statistically unsystematic thesis can even be

seen as a threshold value, i.e., it lay within thearea of prognosis, but could not be considered tobe a tangible corroboration of the model in theface of the identified correlation level (see chap.4, para. 4.1.3.4).

768. The remaining percentage proved itself as non-significant.

769. See to this end the introduction to chap. 3.770. See to this end the explanations in chap. 3, para.

3.1.2.5 as to alternative approaches and modelsfor the illustration and measurement of SupplyChain performance.

771. See to this end also the explanations in chap. 1,para. 1.1.1.

772. See for this chap. 2, para. 2.3.2.773. Cp. Supply-Chain Council (2006b, p. 2).774. For support of this statement, cp. also Christopher

(1998, p. 35) and Hieber, Nienhaus, Laakmann,and Stracke (2002, p. 6). To the inclusion ofmarketing and sales into the strategy concept,cp., roughly, Kotler and Bliemel (1992, p. 453).

775. The Supply Chain strategy as a component of asuperior respective company or competitivestrategy can be defined as follows: “Supplychain strategies, which are part of a level ofstrategy development called functionalstrategies, specify howpurchasing/operations/logistics will (1) supportthe desired competitive business level strategyand (2) complement other functional strategies”(Handfield & Nichols, 2002, p. 247).

776. In the case of the order- or customer-controlledSC process (pull process), customer orderspresent do in fact initialize the planning andexecution of the partial processes within theSupply Chain, beginning with procurement rightup to delivery. The description market economymodel or demand chain management may befound in some places for this. Contrary to this, inthe case of the preview or necessity-controlledSC process (push process), the predicteddemand development is the trigger for the SCpartial processes. The term network economymodel may sometimes be found for this; cp.

Schönsleben (2000, p. 30), Reddy and Reddy(2001, p. 192), and Hoover et al. (2001, p. 13).

777. Cp. Geimer and Becker (2001a, p. 34). See to thisend also the principle of the so called risk spiralaccording to Christopher and Lee, whereby alack of trust of the network participants in theirpartners, and the inherent insecurityaccompanying it, can lead to a build-up instocks. Due to this, the inventory levelsinevitably increase, which in turn leads to a lackof transparency and the spiral is run throughagain; cp. Christopher and Lee (2001, p. 3). Inthis context, cp. also Markillie (2006c, p. 18).

778. Cp. Thaler (2003, p. 15) and Aldrich (2002, p.155).

779. Cp. Bovet and Martha (2000, p. 2) and Kuglin(1998, p. 106). For the term Value Chain, cp.also Porter (1999, p. 59). See to this end also theexplanations in chap. 1, sect. 1.3.

780. Cp. Werner (2002, p. 13).781. Cp. Supply-Chain Council (2006b, p. 2).782. See to this end chap. 4, para. 4.2.3.783. Cp. Supply-Chain Council (2006b, p. 2).

784. See to this end chap. 4, para. 4.2.3.785. Cp. Supply-Chain Council (2006b, p. 2).786. See chap. 6, para. 6.3.1.787. See to this end chap. 3, para. 3.1.2.788. For the term Hermeneutic, see the footnote at the

beginning of sect. 5.1.789. With regards to the proximity to truth, reference is

made to the truth criterion in the sense of theconcurrence of theoretical statements, and inthis case: business economics or corporatereality (theory of correspondence). With this, acontinuous comparison of theoretical statementand observed reality is assumed; cp. Esser,Klenovits, and Zehnpfenning (1977, p. 167). TheSCOR model accounts for this, as it representsan evolutionary model, so to speak, which isadjusted to the (changed) reality at regularintervals; cp. Geimer and Becker (2001b, p.117) and Kanngießer (2002, p. 4). See to thisend also the respective explanations under chap.2, paras. 2.1.2 & 2.1.5. The indicated intrusionalinfluences and errors highlighted with regards tothe scientific proof of the SCOR model’s

suitability or respective proximity to truth, whichwas strived for within the framework of thework submitted, must by no means be ignored(see to this end also chap. 4, sect. 4.4).

790. See to this end the introduction to chap. 3.791. This point of view is also supported e.g. by

Schoder; see Poluha (2005). It must be notedthat a special illustration of the model wasdeveloped and investigated. Generalizinginferences to the model must therefore be seenunder this premise.

792. The model concept stands for the theoreticalstatement, the empirical reality for therespective economical or company reality.Therefore and in turn, reference is made to thetheory of correspondence described in one ofthe previous footnotes in para. 5.1.3; cp. to thisend also Friedrichs (1990, p. 27).

793. It has been indicated several times that the SCORmodel represents, in the original sense, adescriptive model; cp. Supply-Chain Council(2005a, p. 3; 2006b, p. 2). See to this end alsochap. 2, sect. 2.2, as well as para. 2.3.3.

Continuative thoughts lead in the direction of itspossible further development into a formingmodel.

794. See to this end chap. 1, para. 1.1.1.795. See chap. 6, sect. 6.3.796. Adaptability can be described in the present

concrete case as: “… capability to adapt or beflexible amid changing conditions” (Heinrich &Betts, 2003, p. 205). Christopher uses the termAgile Supply Chain, whereby the maindifference lies in that in his case (and thereforein the case of Agile Supply Chains), the aspectof information technology or penetration of theSupply Chain by E-Business is onlyrudimentarily expressed; cp. Christopher (2001,p. 1). For the term Adaptability, cp. also Seibt(1997a, pp. 23, 32).

797. Cp. HP (2003, p. 2).798. Cp. SAP (2004, p. 5).799. (Industry Directions, 2003, p. 1).800. Cp. CapGemini (2004).801. Cp. Stephens et al. (2002, p. 361). As to the gain

in competitive advantages by means of

information technology, cp. also Porter andMillar (1988, p. 62). For the importance ofinformation technology in the formation ofcorporate networks, cp. also Klein (1996).

802. See to this end the close of chap. 1, para. 1.3.1.The named criteria represent necessaryconditions, i.e., the listed criteria are urgentlyrequired in order to constitute an AdaptiveSupply Chain (ASC). As to the necessity forconditions, cp. also Zetterberg (1967, p. 82).

803. Modelled upon Stephens et al. (2002, p. 361) andSeibt (2000, p. 11).

804. Cp. Heinrich and Betts (2003, p. 79).805. A market place is described as a platform or

portal if it enables users to execute electronicbusiness transactions. Portals are used withinthe framework of SCM in various objectivedirections or application scenarios, as forexample order execution and for theprocurement process; cp. Lawrenz andNenninger (2001, p. 329). Relevant companyscenarios supported by portals represent the E-Business areas explained earlier elsewhere, as

for example B2B, B2C and G2B (see to this endchap. 1, sect. 1.6).

806. The concept of Collaborative Order Planning thatdescribes the exchange of order and planningdata between various SC participants, forinstance, fall beneath this; cp. Thaler (2003, p.133).

807. See to this end for example the previouslydescribed concept of Collaborative Planning,Forecasting and Replenishment (CPFR).

808. See to this end roughly the previously describedconcept of Vendor Managed Inventory (VMI).

809. With regards to the optimal fulfillment of orders, adifferentiation must be made betweenquantitative and qualitative components. Whilstthe quantative perspective focuses upon thepure amount or percentage of on-time orderdeliveries, the qualitative perspective includesthe classification of the customers. In this way,strategic customer management could mean thatpreference is given to the delivery to a keyaccount customer over the delivery to several“B-rated” customers; cp. Industry Directions

(2001b, p. 7).810. Cp. to this end Radjou (2001, p. 1).811. (Heinrich & Betts, 2003, p. 152).812. Cp. Lawrenz and Nenninger (2001, p. 335).813. Cp. Schäfer (2002, p. 452).814. See to this end also chap. 2, para. 2.4.1.3.815. Cp. Gensym (2001, p. 2).816. (Radjou, Orlov, & Porth, 2003, p. 3).817. Cp. Segal (2003).818. See chap. 2, sections. 2.2 and 2.3.819. The term Business Process Reengineering (BPR)

can be defined as follows with a view to SCM:“The terms transformation and business processreengineering are used rather loosely to describethree main types of business change efforts: 1.Business design, 2. ’Big’ business processreengineering, 3. ‘Little’ business processengineering. When translated into supply chainterms, these three change efforts have variousmeaning: Business design = Restructuringbusinesses, ‘Big’ business process reengineering= Transforming the supply chain order-to-cashcycle, ‘Little’ business process reengineering =

Transforming logistics functions (for example,transportation or warehousing)” (Kuglin, 1998,p. 99).

820. As stated in chap. 2, para. 2.1.3, enablerepresents one of the three types of a SCORmodel process. It is noticeable that this type ofprocess was formerly named infrastructure; cp.for example, Bolstorff and Rosenbaum (2003, p.154).

821. Cp., roughly, Hammer and Champy (1993, pp. 44,83), Davenport (1993, p. 16), and Venkatraman(1991, p. 127). Information technology alone,however, is not sufficient in order to change orimprove company processes. Next to this,organizational and “human” factors also play arole; cp. Schäfer (2002, p. 2). These will bedealt with in chap. 6, sect. 6.1.

822. (Hofmann, 2004, p. 86).823. Cp. Supply-Chain Council (2005a, p. 12; 2006b, p.

2). See to this end also chap. 2, sect. 2.1.3.824. Cp. Heinzel (2001, p. 53).825. For the term tools in the previous sense and as to

the definition of other terms (procedures, etc.),

cp. roughly, Seibt (2004, p. 19).826. Cp., roughly, Holcomb, Manrodt, and Ross (2003,

p. 2) and Boyer, Frohlich, and Hult (2004, p.171).

827. Cp. Goetschalckx (2002, p. 105).828. Cp. Metcalfe (2003, p. 32) and Radjou et al.

(2003, p. 14).829. Cp. Industry Directions (2001b, p. 7). For the

diffentiation between strategic and operationalplanning, cp. e.g. Schierenbeck (2003, p. 128).

830. Seibt assumes that companies must continuouslyadjust their E-Business systems to marketdevelopments and changed requirementsthroughout the whole system life cycle due todynamic market development and the speed ofdevelopment in the field of technology; cp. Seibt(1997b, p. 393).

831. Company representation: IBS, InternationalBusiness Systems, is a worldwide leadingsoftware-supplier for (…) integrated IT-solutions for distribution and Supply Chain-orientated companies. (…) Our internationallyactive customers comprise for example, ABB,

Cartier, Ciba Vision, Galenica, General Electric,Honda, Maxell, Miele, Pioneer and Volvo. Intotal, more than 5000 customers in over 40countries have already chosen IBS’s softwaresolutions. Since over 25 years ago our globalnetwork of branches and business partners hasprovided future-orientated companies withsoftware, hardware and services. 2000 IBSemployees work in 90 branches in 26 countries.In a further 10 countries IBS works successfullywith business partners. IBS is noted at theStockholm Stock-Exchange; cp. IBS (2004a).

832. Self-representation of the company: “BusinessObjects helps the world’s leading organizationstrack, understand, and manage their business inorder to improve enterprise performance. Withmore than 26,000 customers in 80 countriesworldwide, Business Objects is the clear marketleader in the business intelligence industry.Founded in: 1990. 2003 Revenue: $560.8 million.Employees: 3,900. Headquarters: San Jose,California and Paris, France” (Business Objects,2004).

833. Cp. IBS (2004b) and Business Objects (2002).The available work does not have the intentionof verifying (or falsifying) the correctness of thisstatement. Moreover, purely the respectivecategory of applications is to be highlighted.

834. An overview of the varying applications forSupply Chain Management (SCM) IT supportcan for example be found with Richmond,Burns, Mabe, Nuthall, and Toole (1999, p. 509).For an example as to how the variousapplications can be supported on the hardwareside, cp., roughly, Sun and CGE&Y (2003, p. 2).

835. Self-portrayal: “Gartner, Inc. is the leadingprovider of research and analysis on the globalIT industry. Our goal is to support enterprises asthey drive innovation and growth through the useof technology. We help clients make informedtechnology and business decisions by providingin-depth analysis and actionable advice onvirtually all aspects of technology. This year(2004; note of the author) marks the 25thanniversary of Gartner and the founding of ourindustry. We take pride in our pioneering work

to assist our clients and our industry in benefitingfrom the use of technology. Gartner clients trustin our rigorous standards that safeguard theindependence and objectivity of our researchand advice. With $858 million in revenue in2003, and more than 10,000 clients and 75locations worldwide, we are the clear marketleader” (Gartner, 2004).

836. Cp. Gartner (2002, p. 1). In Gartner’sterminology, the term SCDM defined and usedwithin the framework of the available work is afar-reaching combination of the automatedexecution of business events (Business RulesEngine, BRE) and the management of companyprocesses (Business Process Management,BPM); cp. Sinur (2003, p. 2).

837. Cp. Hinkelmann (2003, p. 27). Under“applications to be taken seriously”, the authorunderstands those that have already been widelyassigned within larger and big companies – as tothe classification of company sizes, cp.Schierenbeck (2003, pp. 34, 540). Next to this,other comparable applications exist, as for

example ProSCOR by Proforma; cp. Proforma(2004) or PowerChain by Optiant; cp. Optiant(2004), which up until now have only beenengaged in the application of SCOR within arelatively narrow scope and by smaller SWvendors.

838. For an overview and an estimate as to thepresently available SCDM manufacturers andapplications, cp., roughly, Gartner (2002, p. 1)and Hall and Harmon (2005, p. 13).

839. Self-description of the Organisation: “Founded in1986, Gensym Corporation (Burlington,Massachusetts) is a provider of softwareproducts and services that enable organizationsto automate aspects of their operations that havehistorically required the direct attention ofhuman experts. Gensym’s product and serviceofferings are all based on or relate to Gensym’sflagship product G2, which can emulate thereasoning of human experts as they assess,diagnose, and respond to unusual operatingsituations or as they seek to optimize operations”(Gensym, 2004a).

840. Cp. Gensym (2001, p. 1).841. Cp. Gensym (2004b).842. Cp. Industry Directions (2001b, p. 9). See to this

end also chap. 3, para. 3.1.2.843. Company description: IDS Scheer was founded in

1984 by Prof. Dr. Dr. h.c. mult. August-Wilhelm Scheer as a small consultancy companywith employees of the University of Saarland inGermany. Today, the international company isrepresented by partners in 50 countries and aGlobal Services Partner of SAP. At IDS Scheer(…) over 2.000 employees are engaged ascompetent contact persons in all importantquestions about process organization – in thefields of Customer Relationship Management,Supply Chain Management and EnterpriseManagement, as well as the fields of ApplicationManagement, Outsourcing and TechnologyConsulting. IDS Scheer is represented inGermany in Saarbrücken Berlin, Düsseldorf,Frankfurt, Hamburg, Munich, Freiburg andNuremberg and has branches in 20 countries,including Great Britain and France, the States of

Middle and Eastern Europe, Brasil, Canada andthe USA, in addition to Japan, China andSingapore; cp. IDS (2004a).

844. Cp. IDS (2004b).845. Ibid.846. Cp. IDS (2003, p. 1).847. Cp. Gunther (2003, p. 4).848. Cp. IDS (2004c).849. (Gunther, 2003, p. 8). Detraction in quality of the

illustration result from extraction from theoriginal document, which can, if necessary, beaccessed from the original for better legibility.The consistency of the application with theSCOR model is primarily to be visualized bymeans of the illustration.

850. Cp. Sydow (2003, p. 18).851. Company information: BOC Information

Technologies Consulting GmbH, was founded in1995 in Vienna, as a spin-off of the BPMS(Business Process Management Systems) groupas part of the Knowledge EngineeringDepartment of the University of Vienna. Thanksto the speedy expansion of the German market,

the first independent company was founded inBerlin in 1996. Originating from the headquarterin Vienna, further companies emerged in Madrid(1997), Dublin (1998), Athens (1999) andWarsaw (2002). BOC Information TechnologiesConsulting GmbH is an internationally operatingadvisory and software house, which hasspecialized itself in strategy, business processand IT management. At present, BOC employsover 100 people; cp. BOC (2004).

852. In the sense of the analysis of operational cross-sectional functions (for the various operationalorganization forms, cp., for example,Schierenbeck (2003, p. 115).

853. Cp. BOC (2002a, p. 1).854. Cp. BOC (2002b, p. 1).855. Cp. BOC (2002c, p. 1)856. Cp. Sinur (2003, p. 2) and Heinrich and Betts

(2003, p. 14).857. For the term Supply Chain Competence in the

present context see chap. 1, sect. 1.7.858. Cp. Schäfer and Seibt (1998, p. 365). In the

submitted case, the focus lies upon the Supply

Chain and its management. In analogue to this,the factual situation refers to the competencewith respect to the Supply Chain, which wasreferred to as Supply Chain competence in thework at hand.

859. (Lippitt, 1982, p. 1).860. See chap. 2, para. 2.3.2.861. See chap. 5, para. 5.1.2.862. The Supply-Chain Council offers the following

explanation of the model description inconjunction with this: “The Model is silent in theareas of human resources, training, and qualityassurance among others. Currently, it is theposition of the Council that these horizontalactivities are implicit in the Model …” (Supply-Chain Council, 2006b, p. 3).

863. See sect. 1 of the appendix.864. The term can be defined as follows: “Human

resources cover all process related humanfactors that are relevant to the improvement ofthe capabilities and motivation of the employeesinvolved” (Seibt, 1997a, p. 13). Cp. to this endalso Lawrence and Lee (1984, p. 54).

865. Development planning also covers on the onehand assignment planning that is supported byTraining on-the-job, and on the other handfurther educational planning that is assured bymeans of Training off-the-job; cp. Schierenbeck(2003, p. 352).

866. An organization can be fundamentally describedas follows: “Organizations are composed ofindividuals and groups, created in order toachieve certain goals and objectives, operatedby means of differentiated functions that areintended to be rationally coordinated anddirected, in existence through time on acontinuous basis” (Lawler & Rhode, 1976, p.32).

867. Leavitt uses the term four-variable conception oforganizations; cp. Leavitt (1965, p. 1144). Seibtalso describes the system variables asDimensions for Information System Structuring;cp. Seibt (1991, p. 252). For a thoroughdiscussion of the subject matter, q.v. Seibt(1997a).

868. Cp. Schäfer (2002, p. 99).

869. Leavitt describes the special meaning of thePeople approach as follows: “By changinghuman behavior, it is argued, one can cause thecreative invention of new tools, or one cancause modifications in structure (…) By eitheror both of these means, changing humanbehavior will cause changes in task solutions andtask performance and also cause changestoward human growth and fulfillment …”(Leavitt, 1965, p. 1151).

870. Cp. Galbraith (1977, p. 26).871. Cp. Guest (1962).872. Cp. O’Shaughnessy (1976).873. The approach already becomes apparent in the

definition of an organization which the authorssubmit as follows: “An organization is thecoordination of different activities of individualcontributors to carry out planned transactionswith the environment” (Lawrence & Lorsch,1969, p. 3).

874. Cp. Leavitt (1965, p. 1153).875. Cp. Lippitt (1982).876. Cp. Likert (1961).

877. Litterer assumes that two substantial factors ofinfluence exist for organizations: members andtechnology, cp. Litterer (1973, p. 104).

878. For the subject matter of organizational design,cp. also Grochla (1982, 1983).

879. For the meaning of the system variable people,especially within the context of organizationalchanges with regards to the Supply Chain, cp.also Cohen and Russel (2005, p. 229).

880. Cp., roughly, Becker and Geimer (1999, p. 25).881. This is mainly in accordance with the Supply

Chain competences fundamental to the SCORmodel: Customer-facing performance capabilityon the one side, and company-related efficiencyon the other side.

882. Alternatively expressed, the performanceindicators serve to measure, assess and monitorthe two SC competences that constitute theSCOR model.

883. Human Resources Management (HRM) is seen,as already represented in Porter’s Value Chainapproach in chap. 1, para. 1.3.1, as an essentialcomponent of Supply Chain Management.

Porter assumes that HRM-related activities takeplace in various parts of the company, and thatmanagement of the human resources supportsnot only the individual primary and supportiveactivities, but also the total Supply Chain; cp.Porter (1999, p. 68).

884. In this context it is relevant to see the role oftechnology as an Enabler, as stated in chap. 5,sect. 5.3. Collins describes this as follows:“Technology can accelerate a transformation,but technology cannot cause a transformation”(Collins, 2001, p. 11).

885. Cp. Geimer and Becker (2001a, p. 22).886. See to this end the statements in chap. 1, para.

1.3.1.887. Cp. Schäfer (2002, pp. 358, 370). On the basis of

empirical studies within the context of companyprocess optimization, Kajüter quotes thefollowing result: “Reengineering conceptsconfirmed the findings of earlier researchidentifying human factors (e.g., resistanceagainst organizational change) as the majorreason for the failure of corporate restructuring”

(Kajüter, 2002, p. 15). Handfield and Nicholscome to a similar conclusion: “Organizations arecontinually faced with the challenge of managingthe ‘people’ part of the equation. (…) A numberof supply chain initiatives fail however due topoor communication of expectations andresulting behaviors that occur. (…) Themanagement of interpersonal relationshipsbetween the different people in the organizationis often the most difficult part” (Handfield &Nichols, 2000, p. 67).

888. (Kuglin & Rosenbaum, 2001, p. 170).889. Hamel describes the renewal as follows:

“Renewal is the capacity to reinvent not onlyprocesses and systems, but purpose and missionas well” (Hamel, 2002, p. ix).

890. Based upon Leavitt (1965, p. 1145).891. Schumann for example describes Change

Management as a “process of permanentchange”; cp. Schumann (2001, p. 102). For theimportance of Change Management inconjunction with Supply Chain Management, cp.also Poluha (2001, p. 317).

892. Cp., roughly, Hieber et al. (2002, p. 6).893. For example, a project with the name ProdChain

was carried out by the Forschungsinstitut fürRationalisierung (Research Institute forRationalization) at the RWTH Aachen (fir) inGermany during the period from March 2002until August 2004 under Promotion No. IST-2000-61205. Its objective was the developmentof a procedure for analysis and improvement oflogistic performances within productionnetworks, and it was based upon the Supply-Chain Council’s SCOR model. The project didnot, however, investigate the SCOR model assuch. It rather assumed its “suitability” andapplied it to develop logistic metrics as indicatorsfor the continuous improvement in logisticperformance in production networks, by meansof which targeted structural suggestions couldbe made according to the analyzed logisticperformance; cp. Forschungsinstitut fürRationalisierung an der RWTH Aachen (fir)(2004).

894. Cp. Bolstorff and Rosenbaum (2003, p. 1).

895. For the term exploration, cp., roughly, Kromrey(2002, p. 67). See to this end also the statementsunder chap. 1, para. 1.1.2.

896. The first version of the SCOR model waspublished in November 1996; cp. for example,Hagemann (2004, p. 4).

897. Cp. Heck (2004, p. 15).898. Cp. Göpfert and Neher (2002, p. 37).899. Cp., for example, Heinzel (2001, p. 50). This

opinion is also shared by Welke, who assumesthat the development in the European region isbeing increasingly hastened by multinationalcompanies which have possibly accumulatedexperience of the SCOR model in theirAmerican branches, as for example Daimler-Chrysler and Siemens, or have theirheadquarters in America and are also active inEurope, as for example the companies HP andIntel already mentioned in conjunction withSCOR’s application; see Poluha (2004b).

900. See to this end chap. 4, sect. 4.4.901. Cp. Supply-Chain Council (2003b, p. 1) and

Hieber et al. (2002, p. 6).

902. In this context cp. also chap. 5, para. 5.1.2.903. Cp. Kromrey (2002, p. 3).904. As to the relevance of comparable data upon

regional level, cp., for example, SIMTech (2002,2003) and Gintic (2000, 2001).

905. See to this end the statements made in chap. 3,para. 3.3.5.

906. A possible approach at explanation for this wouldbe that these companies invest increasingresources into the SCOR model or haverespectively adjusted themselves more stronglyto corporate-policy or logical decisions.

907. See chap. 4, para. 4.2.2.908. Cp. Schäfer (2002, p.2). The concept of a new,

expanded company, highlighted by strongrelationships to external partners, and a shiftfrom physical to virtual exchange in whichinformation is primarily transfered along thevalue chain, was already introduced by Tapscottin 1996; cp. Tapscott (1996, p. 97).

909. For example, Radjou et al. (2003, p. 31) namepractical application cases for Adaptive SupplyChains. However, the statements are of a purely

qualitative nature and are not based uponscientific examinations.

910. So that modern concepts like SCDM can beestablished within company practice, they mustprove their strategic importance for thecompany success. In this case, the samerequirements and present-day challenges applythat apply to information technology in general:“As information technology’s power andubiquity have grown, its strategic importance hasdiminished. The way you approach ITinvestment and management will need to changedramatically” (Carr, 2003, p. 5).

911. For an empirical study of the influence ofCustomer Relationship Management (CRM) inthe context of E-Business, cp. Madeja andSchoder (2003, p. 9). For an overview ofselected E-Business concepts, cp., roughly,Schäfer (2002, p. 11).

912. The suggestions listed in the following forexpansion of the SCOR model’s structure in the“human factor” area do not immediately stemfrom the results gained within the framework of

the work submitted, because inevitably no datawas collected for this purpose. They are ratherbased mainly upon present developments in thefield of Supply Chain Management, as they areshown to do in chap. 5 (see to this end chap. 5,sect. 5.2 & 5.3) Furthermore, they are to bedifferentiated from such suggestions which aimat a further “supplementing” of the existingmodel structure (see to this end chap. 5, para.5.1.2).

913. See sect. 6.1; cp. also to this end Davenport(2005, p. 102).

914. Cp., roughly, Dhavale (1996, p. 50). Sharman(1995, p. 34) introduces the term employee orteam performance in conjunction with this. As tothe Supply Chain scorecard, see also thestatements under chap. 1, para. 1.5.4.

915. Cp., roughly, Kaplan and Norton (1997, p. 121).916. See to this end the explanations under chap. 3,

para. 3.1.2.5.917. Cp. Welz (2005, p. 17).918. Cp. Ziegler (2005, p. 20).919. As highlighted in sect. 6.1, the system variable

people represents a substantial factor during theimplementation of organizational changes.

920. See to this end chap. 3, para. 3.1.2.5.921. A distinguishing characteristic of the BSC is

represented by the monetary (finance-related),as well as non-monetary performanceindicators; cp., for example, Kaplan and Norton(1992, p. 71) and Horváth and Kaufmann (1998,p. 41).

922. Cp. to this end roughly Wollnik (1977).923. See to this end chap. 4, sect. 4.3.924. Cp. to this end roughly Backhaus et al. (2003, p.

408).925. See to this end chap. 4, para. 4.3.2.926. The graphical overviews also orientate

themselves upon the recommendations forvisualization by Wottowa, which target theimmediate ability to be proven; cp. Wottawa(1980, p. 199).

927. Diags. 6.2a & 6.2b represent an entirety which,for graphic representation reasons, could not bereproduced in an illustration.

928. Ibid.

929. See to this end sect. 3 of the appendix.930. Cp. Supply-Chain Council (2005c, p. 1); Supply-

Chain Council, 2006a, p. 1). For a detailedoverview of the concerned performanceindicators and their calculation cp. also Supply-Chain Council (2005b, p. 296; 2006b, pp. 368,434).

931. This means that two (or more) absolute figuresare placed into relation with each other (see tothis chap. 3, para. 3.1.1.2).

932. Cp. Geimer (2000, p. 56).933. The advantages of such a standardization are

seen not only within a company (between thefunctional departments), along the SCOR-specific Value Chain (VC) and in cooperationbetween the partners within VC, but alsobetween companies in varying industries(manufacturers, Logistics Service Provider, andso on); cp. Heinzel (2001, p. 49). See to this endalso the explanations in chap. 1, sect. 1.6 andchap. 2, sect. 2.2.

934. Cp. Schmelzer and Sesselmann (2004, p. 162).The authors quote Johannes Feldmayer, member

of the central Board of Directors of SiemensAG.

935. Davenport, in conjunction with this, introduces theterm Commoditization of processes; cp.Davenport (2005, p. 100). In this way, orexample, the previously-mentioned DellCompany has selected velocity in associationwith a high multitude of products as adifferential characteristic. The company hasoptimized and specially suited the relevantprocesses to such an extent that they runnoticeably faster than the competition and havetherefore created, and continuously built upon, acompetitive advantage; cp. Geimer and Becker(2001a, p. 28) and Markillie (2006b, p. 4). As afinal consequence, such a standardization –compatible to a competition benchmarking –would lead to stagnation, as at the end allcompetitors would have reached the sameperformance level, and a differentiation wouldno longer take place; cp. Schäfer and Seibt(1998, p. 376). For the term competitivebenchmarking, cp. also Watson (1992, p. 5).

936. Cp. Heck (2004, p. 16).937. Cp. Göpfert (2002, p. 38).938. The DAX100 includes 100 large German stock

corporations or public companies respectivelycomprising 30 DAX and 70 MDAX companies;cp., for example, Deutsche Börse (2004).

939. See to this end the statements in chap. 1, para.1.3.1 and sect. 1.6.

940. Cp. Schäfer (2002, pp. 346, 381).941. Cp. for example, Latham (1999, p. 91).942. Cp. Seibt (1999, p. 56).943. Cp. Normann and Ramirez (2000, p. 187). Abell

combines the factual situation in a more generalform under the term Dual Nature ofManagement and provides the followingdescription: “Running the business and changingit are not sequential but parallel pursuits” (Abell,1993, p. 3).

944. The fundamental dynamic process of continuouslysearching for new value creation and businessopportunities, as well as realizing those withinthe framework of organizational change, wasdescribed by Schumpeter as the process of

creative destruction; cp. Schumpeter (1976, p.81). Analogue to this, Lee states threefundamental conditions for an optimal SupplyChain according to his terminology: flexibility,adaptability and a balancing-out of interests; cp.Lee (2005, p. 72).

945. See to this end roughly chap. 2, sect. 2.2 & para.2.3.3 in addition to the introduction to chap. 5,sect. 5.3.

946. Davenport describes the connection as follows:“The SCOR model is only a catalyst for changeand a framework for analysis. As with anyapproach to process improvement, firms muststill make difficult changes in how they do theirwork and to associated systems and behaviors”(Davenport, 2005, p. 102).

947. See to this end the practical examples under chap.2, sect. 2.4.

948. By these means and amongst other things, theincreasing demands upon the Supply Chain areto be taken into account, which in turn resultfrom the changed competitive conditions; cp.Markillie (2006a, p. 3). Based upon Cohen and

Russel, the present connection allows itself to bedescribed as follows: “Like its business, (theoptimal) supply chain is flexible, agile andevolving constantly. What doesn’t change,though, is the company’s view of its supplychain as a key source of competitive advantage– one well worth the ongoing investment”(Cohen & Russel, 2005, p. 257).

949. Taken from BearingPoint (2003d).950. Ibid.951. Based on BearingPoint (2003d).952. Taken from BearingPoint (2003e). For

explanations concerning the graphicalrepresentation in the following diagrams, refer topara. 5.5 of the appendix. There is no directcorrelation between the data used in theexamples and the data used in the empiricalstudy outlined in chapters 3 & 4. The data usedin the following examples are only intended forexemplary and illustrative purposes.

953. Taken from BearingPoint (2003b, p. 23).954. For detailed results refer to chapter 4, sect. 4.1;

for a summary of the results refer to chapter 5,

para. 5.1.1. The existence of sub-cases for asingle theses (e.g., 1a & 1b) is a result of thedifferentiation in an inbound and an outboundcomponent.

955. For the abbreviations used, see the abbreviationslist at the beginning of the book.

956. For each of the digitalized sources, the day of lastaccess is given under the abbreviation DolA,i.e., the date when the internet site mentionedwas last accessed.

957. Unpublished, available from the author.

Appendix One Quantitative Survey (KPIQuestionnaire)949

Appendix Two Overview of the PerformanceMeasures of thequestionnaire950

Appendix Three Connection between SupplyChain Competences andPerformance Indicators951951

3.1 Customer-facingcompetence (customerservice and flexibility)

3.2 Internal-facingcompetence (cost and assets)

Appendix four Details and EvaluationExamples of thePerformance Measures9524.1 Source

4.2 Make (Produce)

4.3 Deliver: Store

4.4 Deliver: Transport

4.5 Deliver: Sell

Appendix five Representation of theResultsof the Questionnairefor the Acquisition ofPerformance Measureswithin the KPI BenchmarkingTool9535.1 Example for onlinesurvey (KPI Questionnaire)

5.2 Results of SCOR on thefirst level

5.3 Results within a SCOR

process

5.4 Details of an exemplaryperformance measure andsuggested measures forimprovement

5.5 Clarification of details

of an exemplaryperformance measurerepresentation

Appendix six Detailed Analysis Results ofthe Single Hypotheses9546.1 Results of SCOR modelgroup Intra-PerformanceAttribute (I-P)

6.2 Results of SCOR modelgroup Intra-Competency (I-C)

6.3 Results of SCOR modelgroup Inter-Competence/Performance

Attribute (I-CP)

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Table of Contents Application of the SCOR Model inSupply Chain Management Copyright Dedication Foreword Preface Acknowledgments Abbreviations 1. Objectives, methodology, approachand definition of terms 2. The Supply Chain OperationsReference Model (SCOR model) of theSupply-Chain Council 3. Empirical study based upon aquantitative questionnaire 4. Comparison of work hypotheses and

acknowledged results of the empiricalstudy 5. Summary of conclusions andinnovative assessments 6. Limitations of the presently availableSCOR model References Appendices Bibliography