Transcript
Page 1: Pipeline information survey:a UK perspective

Pipeline Information Survey:

a UK Perspective

D BERRY

University of Wales, Cardi�, UK

GN EVANS

University of Wales, Cardi�, UK

MM NAIM

University of Wales, Cardi�, UK

(Received June 1996; accepted after revision August 1997)

Experiential and analytical research output from a number of sources suggests that the inclusion of`pipeline' information in an inventory and production (or supply) ordering rule leads to a reductionin production and supply on-costs and improved customer service levels. The evidence is provided byconsideration of the dynamics induced in an ordering system due to existence of delays between anorder being placed and receipt of that order, namely, the `pipeline'. While the utilisation of pipelineinformation in such Decision Support Systems as the `To-Make' model is admirable the resourceimplications to a manufacturing enterprise have to be considered. This paper outlines a surveyundertaken of a sample of UK companies in order to determine their understanding of the pipeline,their practices in acquiring pipeline information and their application of such pipeline information.The survey concentrated on interviews with master production schedulers, but also included discus-sions with `logistics' managers, and was supported by plant visits. Clustering analysis of the surveydata gathered provides evidence that industry realises the importance of monitoring pipeline states,such as the level of orders in the pipeline or the pipeline lead-time, particularly where long and vari-able lead-times are encountered. The particular pipeline of interest to a given enterprise may be in-ternal (production) or external (supply) or both. It is noted that where the pipeline is monitoredthere is often an unful®lled opportunity for utilising the pipeline information within a robust order-ing strategy. # 1998 Elsevier Science Ltd. All rights reserved

Key wordsÐpipeline management, material ¯ow survey, master production schedule

1. THE RESEARCH AREA

A FOUR MAN-YEAR UK publicly sponsored

research project, `Dynamic Analysis of an

Adaptive `To-Make' Model for exploitation in

Supply Chains and Individual Business

Policies,' has con®rmed and reinforced the

experiential results of Sterman [1] that the in-

clusion of `pipeline' information in a master

production schedule (MPS) ordering rule can

have a dramatic impact on improving dynamic

behaviour. The pipeline may be de®ned as the

delay between generating an order and the

receipt of that order into stock.

The main output of the research has been

the development of a structured control engin-

eering and simulation methodology to analyse

and optimise the `To-Make' model. The `To-

Make' model is an in-house industry devel-

oped ordering algorithm that utilises pipeline

informationÐnamely lead-times and the order

Omega, Int. J. Mgmt Sci. Vol. 26, No. 1, pp. 115±131, 1998# 1998 Elsevier Science Ltd. All rights reserved

Printed in Great Britain0305-0483/98 $19.00+0.00PII: S0305-0483(97)00041-8

115

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in the pipeline (OPL). The theoretical researchwork has shown that incorporation of pipelineinformation within an ordering algorithmleads to improved dynamic behaviour [2] and

can lead to increased customer service levelswhile minimising stock holding requirements[3, 4]. The research has also shown the import-ance of such pipeline information in minimis-ing the demand ampli®cation e�ect within a

supply chain [5±8].

Although the theoretical research results aresigni®cant the ability for manufacturing enter-

prises in general to generate and properly uti-lise pipeline information has to be considered[9]. Although the utilisation of pipeline infor-mation is admirable the resources to gathersuch information need to be considered.

The research therefore undertook a surveyof a small sample of UK industry in order todetermine their:

(1) Understanding of the pipeline: Agreedor at least a substantially common ter-minology would be likely to emergewhich would facilitate the feedback of

theoretical research results to industrial-ists.

(2) Practices in acquiring pipeline infor-mation: From the outset of the researchwe wished to test the hypothesis that

pipeline information was readily avail-able and was being utilised, althoughnot necessarily for MPS purposes.

(3) Application of such pipeline infor-

mation: If a company did acquire pipe-line information to what use was itbeing put?

(4) Industrial environment: We wished todetermine a `niche' for an ordering al-

gorithm utilising pipeline information.

The paper focuses on reporting the pipelinesurvey methodology and its ®ndings. Prior tothis the theoretical implications on dynamic

behaviour of introducing pipeline informationwithin ordering algorithms is summarised.This is important as it forms the backgroundto the survey itself and also depends on thesurvey output to determine appropriate ``what

if?'' questions.

A general outline of MPS requirements isalso introduced as it is the MPS function thatis being addressed by the pipeline survey.Also, the interpretation of the survey results isundertaken by drawing on the outputs ofother researchers, in particular Burcher [10, 11]who undertook a survey examining the capa-bilities of 400 companies' MRPII systems.

2. THE THEORETICAL CONTEXT

Sterman [1], through his behavioural studiesof the well known supply chain `Beer Game',advocates two pieces of pipeline informationto be utilised in an ordering rule;

(1) The lead-time between placement andreceipt of orders: To ensure that su�-cient orders are received into stock theamount of OPL should be proportionalto the lead-time.

(2) The amount of OPL within the pipelinewhen reordering: Ignoring the pipeline

Fig. 1. Block diagram representation of a multi-productcompany incorporating the `To-Make' ordering control

model [2].

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OPL leads to instability and it is there-fore far better to compensate for thedi�erence between desired and actualOPL.

A lack of consideration of the pipeline inthe generation of orders can lead to costlyswings in supply, production and inventory.The consequences can be periods of poor cus-tomer service levels and/or excessive stockkeeping units.

Figure 1 gives a block diagram showing themajor features of a formalised computer baseddecision support system known as the `To-Make' model. The model was conceived withthe speci®c objective of achieving high custo-mer service levels without requiring excessivestocks. The model was developed in-house bya South Wales manufacturing company usingsteady-state control theory principals. A num-ber of authors have advocated the use of con-trol theory in the design of production andinventory management [12±15]. Control theoryalso formed the basis of the simulation analy-sis undertaken to study and `optimise' themodel in a dynamic sense.

The `rules' within the model incorporate anumber of features which we consider to be

advanced. Desired stock levels are determined

via a service level requirement based on a stat-

istical analysis of sales. Production orders are

calculated according to a rule that utilises a

stock replenishment policy and a forecast of

future demand. Furthermore, the ordering rule

also makes use of information about the level of

orders still being processed in the `pipeline' and

a frequently updated estimate of the `pipeline'

delay.

A typical rule generated by a MPS may be:

The order placed on the production shop ¯oor

is equal to a fraction of the ®nished goods

inventory de®cit (taken as the di�erence

between a target and the `free stock' in Fig. 1)

plus a fraction of the OPL de®cit (taken as the

di�erence between a target and the actual

OPL, or WIP in Fig. 1) plus the average fore-

cast customer demand (which may be deter-

mined via exponential smoothing).

Typical questions, among others, that may

be asked of such a rule are;

(i) Should the customer demand be used in

the rule?

Fig. 2. Response of a pipeline based ordering algorithm to a step change in demand (a) Factory com-pletion response (b) Free stock response.

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(ii) If so, how much smoothing should beapplied to average demand?

(iii) What fraction of the inventory de®citshould be utilised?

(iv) What fraction of the OPL de®cit shouldbe utilised?

Example outputs from the theoreticalresearch are shown in Fig. 2 [2] and Fig. 3

[16]. Figure 2 shows the impact on the

dynamics of production completions and stock

by varying the proportion of OPL error within

an ordering rule. As seen in Fig. 2a, increasing

the fraction of the OPL error smooths the

production response. The extreme case where

there is no OPL feedback yields the fastest re-

sponse but at the expense of a higher degree

of demand ampli®cation. A smoother pro-

duction completions response unfortunately

Fig. 3. Customer service levels for a pipeline based ordering algorithm for various operating scenarios.

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yields a slower stock recovery as seen inFig. 2b, with the greater risk of stock out.This dichotomy is overcome by careful designin choosing a compromise value of the frac-tion of OPL error.

Figure 3 shows a greater degree of sophisti-cation in simulation analysis. The three graphsshow the relationship between customer ser-vice level (PIC rate) and weeks worth of stock

for di�erent scenarios. Two di�erent pipeline

representation are shown (®rst order and third

order) and the pipeline lead-time is ®xed or

variable throughout a simulation run. Best

and worst case designs are shown for di�erent

average pipeline lead-times (Tp = 4 and 8

weeks) with proportional (P) and pro-

portional/integral (P and I) control. Under

these scenarios the OPL and a lead-time esti-

mate is fed back into the ordering rule. The

latter is depicted under two extreme cases; the

proactive system feeds back any lead-time

change as it happens, while the reactive system

waits for a product to come out of the pipeline

before it feeds back the estimate.

As an example the analysis has shown that,

for a given target customer service level of

97.5%, steady-state analysis (the baseline case

in Fig. 3) misleadingly predicts low stock hold-

ing requirements at 1.63 weeks worth of stock.

Dynamic analysis on the other hand shows

that by considering the pipeline delay a higher

stock holding is required, although careful de-

sign minimises requirements [16].

The theoretical research has shown that

pipeline information does improve dynamic

behaviour. The theoretical research has gone

further than Sterman's experiential work by

also showing the potential for using control

theory and simulation in an integrated fashion

Fig. 4. Relationship of MPS to other manufacturing plan-ning and control activities [26].

Fig. 5. The essential requirements of an MPS [26].

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so as to design suitably robust ordering algor-ithms that incorporate pipeline information.

3. MPS REQUIREMENTS

The need to incorporate the present state ofthe pipeline into ordering rules/decisions hasalso been expressed in the UK by twoComputer Aided Production Management(CAPM) surveys [3, 17]. The surveys indicatethat there is a need in UK industry to developsuitably robust (in the dynamic sense) orderingalgorithms. Such ordering algorithms shouldmake the best use of all available informationwith regards to both exogenous and endogen-ous factors. For example, information aboutthe current order book, forecast demand andthe manufacturing systems state, when coupledwith suitable stock and replenishment policies,aid in the decision making process for pro-duction ordering.

Within the context of CAPM a theoreticalmodel, shown as Fig. 4, developed byVollman et al. [18] was found to be adequatefor a general framework for the project. Themodel describes a range of planning and con-trol activities that are typically found in manycompanies to varying degrees. In the contextof this model a number of pipelines can beidenti®ed: A production pipeline and a ma-terial supply pipeline. In addition, althoughonly implied in Fig. 4, is a capacity acquisitionpipeline which is the focus of much ofBurcher's studies [10].

In the context of the present research, themajor pipeline under consideration is the (in-ternal) production pipeline, which is con-sidered in MPS activities; the (external)material supply pipeline became an issue oncethe survey was underway. The MPS is a man-agement commitment to produce certainvolumes of ®nished goods in particular timeperiods in the future. Key principles are out-

Fig. 6. The focus of the company pipeline ¯ow survey.

Fig. 7. Example of a process ¯ow analysis model developed during company survey.

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lined in Fig. 5. The MPS is created for each®nished goods part number using known cus-tomer orders, sales forecasts and knowledge ofcapacity [20]. According to APICS the successof MRP systems depends greatly on the man-agement of the MPS process; ``The MPSdrives MRP and consequently the entire pro-duction and inventory management system''[19]. Successful management of the MPSdepends on the adoption of a number of keyprincipals as described in Fig. 5.

The theoretical studies summarised inSection 2 are of considerable importance informalising the MPS process. The MPS pro-cess in many companies is often undertaken inan ad-hoc manner, and often relies totally onthe accumulated experience of a master sche-duler. A number of authors have suggestedthat there is considerable scope for computerbased DSS [11, 20] of which the algorithmsdescribed in Section 2 are an example of.Improvements in company performance as aresult of a DSS have also been described else-where [21].

4. COMPANY MATERIAL FLOW SURVEY:

METHODOLOGY

The methodology adopted for the companysurvey initially involved drafting a list of com-panies with whom the research team had pre-vious contact and categorising the companiesaccording to their market sector, production

strategies, product range and logistic pipeline

structure. It should be noted that the previous

contact with these companies were in colla-

borative programmes unrelated to the research

described in this paper. Thus, although the

companies were known to the research team,

they were a random sample from the UK

industry base with a wide geographical spread.

The basis of the sample selection was simply

to determine whether there were any immedi-

ate bene®ciaries from the research.

A trial interview with company executives

was undertaken utilising an in-depth question-

naire developed in-house addressing key issues

in order to obtain industrial feedback on

issues such as type and style of questions,

interview length and whether the categories of

information exist within one person's knowl-

edge or with several cognate executives. Based

on the interviewees' recommendations it was

decided to use a short list of points of discus-

sion (as given by the headings in Appendix A)

and to base the interview around the develop-

ment of a series of simple diagrammatic ma-

terial ¯ow models.

The essential ideas behind the interviews

can be summarised in Fig. 6. The main output

from the interviews was to understand the ma-

terial and information ¯ows that constitute a

particular manufacturing environment and the

control structure that manages those ¯ows.

Information and material ¯ow characteristics

Fig. 8. Example of a logistics planning and control model developed during company survey.

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Table

1.Summary

ofpipelineinform

ationsurvey

Company

Market

AB

CD

EF

GH

IJ

Characteristics

type:

Healthcare

Construction

Electronic

Electronic

Construction

Electronic

Automotive

Electronic

Automotive

Healthcare

Externalenvironment1

No.ofem

ployees

200

300

1200

200

250

2000

N/A

700

N/A

120

Turnover

£M

25

70

80

50±70

14

N/A

N/A

52

32

N/A

Customer

type

Distributor

hospitals

Builders

merchants

Retailers

OEM

Retailers

OEM

Contractors

PC

dealers

OEM

spares

distributors

Subassem

bly

OEM

OEM

spares

distributors

LHA

distributors

Supplier

relationship

Towards

partnership

Partnerships

Multi-source

commercial

Multi-source

commercial

Towards

partnership

Towards

partnerships

Towards

partnerships

Partnerships

Awayfrom

partnerships

Adverse

Supply

lead-tim

es4±6weeks

1±2days

4±24weeks

4±24weeks

4weeks

6h±6months

1±2weeks

0.5±1.5

weeks

1±2weeks

1±2days

Product

Product

range

High

Low

High

High

High

High

Medium

High

Medium

Low

Materialinputcomplexity

Medium/high

Low

High

High

Medium

High

Medium

Low

Medium

Low

Internalenvironment1

Manufacturingprocess

lead-tim

es1±8weeks

1days

2±3weeks

2±3weeks

1±3days

1days

1±2weeks

1.5±10days

1weeks

2days

Process

type

Jobshop

Process

Functional

Functional

Cellular

Flow

line

Cellular

Functional

Functional

Functional

Productionstrategy

Mainly

Mto

S75%

Mto

S25%

Mto

OM

toO

Mto

O50%

Mto

O50%

Mto

SM

toO

Mto

OM

toO

Mainly

Mto

O70%

Mto

S30%

Mto

OPlanningsystem

MPSMicross

Manual

MRPII

MRPII

MPSMRP

Global

SC

MRP

MPSMRPII

MRP

ManualMPS

Comp.Sched

ManualPlan

Pipelinede®nition2

Internal

WIP

andOIP

None

WIP

WIP

KanbanWIP

None

WIP

WIP

WIP

WIP

External

LTFixed

None

LT

OIP

LTforMRP

TotalSCWIP

LTforMRP

LT

Inform

alLT

Pipelinemeasured3

Internal

Level

&LT

None

WIP

weekly

WIP

daily

Realtime

None

N/A

Realtime

Weekly

Daily

External

Fixed

None

Fixed

LTFixed

Fixed

Monthly

Fixed

Fixed

Inform

aland

variable

Fixed

Pipelineutilised4

Internal

MPS/scheduling

None

Expediting

Expediting

Minim

isestocks

None

Strategic

stock

Expediting

Strategic

stock

Process

checking

External

Materials

ordering

None

Materials

ordering

Materials

ordering

None

Expeditingin

GlobalMRP

Material

ordering

None

Material

ordering

None

Key:LT,leadtime;

LHA,localhealthauthority;M

toO/S,maketo

order/stock;MRP,materials

requirem

ents

planning;MPS,master

productionscheduling;MRPII,manufacturingresource

planning;OEM,originalequipmentmanufacturer;OIP,ordersin

progress;PC,personalcomputer;SC,supply

chain;WIP,work

inprogress;N/A

,notavailable.

1Thisresearchoutputrelatesto

Objective2asde®ned

inSection1.

2Thisresearchoutputrelatesto

Objective3asde®ned

inSection1.

3Thisresearchoutputrelatesto

Objective4asde®ned

inSection1.

4Thisresearchoutputrelatesto

Objective1asde®ned

inSection1.

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were required not only through internal pro-cesses but also across the interfaces with im-mediate customers and suppliers. Internalpipeline characteristics were obtained via theiterative development of a process ¯ow analy-sis of material ¯ow as shown in Fig. 7. Theprocess mapping technique is not dissimilar tothat adopted by Hoekstra and Romme [22].This allowed the interview not only to cover`pipeline' concepts but areas such as supplierrelationships, supply lead-times, stocks, manu-facturing and supplier lead-times, productionstrategies and planning control structures.

The ®nal diagram described as a logisticsplanning and control analysis, focused on theinformation based planning and control struc-tures existing in the company, as shown inFig. 8. It can be seen that the format looselyfollows the model developed by Vollman et al.[17] described in Fig. 4. This methodologyallowed the interview to focus on issues sur-rounding the nature of the MPS process, it'sinteraction with the wider system, and es-pecially the presence or absence of informationfeedback paths. Group interviews were under-taken with a variety of company personnel, ina range of di�erent functions. Material ¯owdiagrams were developed in each of the com-panies surveyed and supplemented with writ-ten data. Tours were also made of companyproduction facilities whenever possible.

Each company was written up as a casestudy report; an example (from which Figs 7and 8 are taken from) is given in AppendixA1. A survey summary, suitably amended toensure company con®dentially, is given inTable 1. The following survey results are madewith reference to Table 1 but concentrate onlyon the most signi®cant ®ndings relating to thefour propositions given in Section 1.

5. COMPANY PIPELINE SURVEY: RESULTS

Question 1: Is there an understanding of thepipeline? (indicated by Footnote 4 in Table 1)

Answer: Yes, although there is no standardde®nition.

Prior to the start of the survey, the focus ofthe study was on the internal pipeline. It soonmaterialised that a major issue for the many

of the companies was the external pipeline:That is, the delay between an order beingplaced on a supplier and the delivery of theorder into raw material stocks. It was appar-ent during the survey that the intervieweeswere aware of the implications of supplier per-formance on their business and the need tomove towards more integrated supply chains.

Only one company (B) failed to de®ne anypipeline. When forced by the interviewers toestimate internal and external pipeline lead-times, it became apparent that the lack of ade®nition was due to the fact that the com-pany had relatively very small and consistentlead-times for both supplier deliveries and pro-cess lead-times. Company F also failed to de-®ne an internal pipeline again due to relativelyshort lead-times.

It is interesting to note the de®nitions thathave resulted from the survey. All of thosecompanies de®ning the internal pipeline do sojust in terms of work in progress (WIP). OnlyCompany A de®nes the internal pipeline interms of WIP and orders in progress, that is,OPL. In contrast, seven of the nine companieswho de®ned the external pipeline do so interms of lead-time with Company D de®ningthe level in the pipeline as a function of thelead-time and order placed and Company Finterested in the total supply chain WIP.

Question 2: Is the pipeline measured? (indi-cated by Footnote 1 in Table 1)

Answer: Yes, but in di�erent ways.The pipeline de®nitions given above predo-

minantly de®ne the preoccupation of the com-panies with what they perceive as important tomonitor and hence control in the pipeline.With regards to the external pipeline where itis de®ned as a lead-time it is this variablewhich is measured. Seven of the eight compa-nies do so in terms of ®xed, standard times:That is, the pipeline lead-times are not up-dated on a regular basis. Only Company Icontinuously up-dates its lead-times estimatesbut this is done on an informal basis.Company F monitors total supply chain WIPand up-dates its estimates on a monthly basis.

With regards to the internal pipeline thevariation in practices is more varied. All thecompanies measure what they de®ne as thepipeline; that is, nine of the 10 companiesmonitor their WIP or OPL levels. The periodof monitoring varies between companies from

1The full set of reports is available from the authors onrequest.

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`real time' for Company E to weekly forCompanies C and I. Although not part oftheir de®nitions, Companies A and C domonitor their internal pipeline lead-times.

Question 3: Is the pipeline information uti-lised? (indicated by Footnote 2 in Table 1)

Answer: Yes, but in most cases not as part ofa formal ordering policy.

Despite many of the companies having for-malised MPS, MRP and MRPII systems themain reasons they gave for taking internalpipeline WIP measurements was to ensureexpediting (Companies C, D and H) and toensure that there was su�cient strategic stockwithin the pipeline to ensure customer servicelevels (Companies G and I). Company E usesthe WIP information (classi®ed as theKanbans within the system) in order todevelop strategies for stock minimisation.Company J uses the WIP levels to keep acheck on the process.

Only Company A states the reason forusing the internal pipeline OPL and lead-timeinformation was for a formal MPS procedure.This is not too surprising at it was inCompany A that the `To-Make' model wasdeveloped so it could be classi®ed as a biasedsample representative.

With regards to the external pipeline allcompanies that took measurements used theinformation in a formalised way for materialsordering although this is in the form of duedate assignments. In addition, Company Fuses the information within a global supplychain MPS.

Question 4: What is the general industrial en-vironment of the companies surveyed? (indicatedby Footnote 3 in Table 1)

Answer: It varies from Make-to-Order toMake-to-Stock.

Within the survey, a range of productionenvironments and associated MPS processeswere encountered. Figure 9 highlights thespread of ordering strategies of the companiesinvolved in the survey. At one end of thescale, some companies had relatively simple,¯exible production processes, with a limitedrange of products or products that could becustomised late in the supply chain process.These companies had internal pipeline lead-times ranging from hours to a couple of days.In these situations simple planning and controlprocesses were employed. From an MPS per-

spective, the pipeline is regarded as a blackbox. Orders are placed on the pipeline andproducts arrive after a short delay. In somecases pipeline feedback paths back to MPSprocesses were present, to either indicate ordercompletion or, in a more informal sense, to¯ag production problems which might a�ectorders in the future. In this type of environ-ment there was no perceived need to use pipe-line feedback in MPS processes since thepipeline is short and consistent. There isgreater certainty in the system and hence ahigh probability of planned and actual pipe-line lead-times being met. This is a scenariothat all the companies surveyed were strivingfor.

At the other end of the scale, some com-pany environments had relatively complexproduction processes and a wide productrange. Pipeline lead-times were measured interms of weeks/months. In these situationsCAPM systems were implemented (such as,MRP, MRPII) in an attempt to better managethe complexity of the operating environment.

6. CLUSTERING ANALYSIS

In order to accurately categorise the compa-nies in terms of their operating environmentsand their level of pipeline information gather-ing a binary clustering techniques wasadopted. The two variables of interest werethe average pipeline lead-time and the pipelinelead-time variability. The lead-time data isgiven in Table 1 as the `Supplier lead-times'and `Manufacturing process lead-times' head-ings indicated by Footnote 3. The headings in-dicate the range of lead-times with an averagetaken using this data for use in a binary clus-tering routine. It is interesting to note thatthose companies who do not formally measureeither of these two variables were still able toeither quote and/or check their records forvalues. It was also possible for the researchteam to cross-check the values given againsttheir own experiences and/or documentationthat had been archived from previous colla-borative studies.

The clustering analysis (using a binary al-gorithm developed by the authors on a spread-sheet package) was undertaken for bothscenarios of the internal pipeline and the exter-nal pipeline. Figure 10 shows the clusters

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developed for the case of internal pipelineanalysis. Where high lead-time length andhigh lead-time variability exist some form ofpipeline information is utilised although notnecessarily for a planning function. The clus-tering is based on the information taken fromthe `Pipeline Utilised' and the lead-time head-ings given in Table 1; indicated by Footnotes1 and 4, respectively.

A similar binary clustering analysis onexternal pipeline lead-time characteristics pro-duces similar results as shown in Fig. 11.Where high average external lead-time lengthand highly variable external lead-times existthis information is utilised for some form ofplanning.

The clustering analysis indicates that com-panies that experience high average lead-timesand high lead-time variability are already col-lecting and utilising pipeline information albeitin an informal manner. Thus, for these compa-nies there would be no additional resource im-plications if they were to utilise the pipelineinformation within a formal ordering policy.Due to their lead-time characteristics thesecompanies would also bene®t the most fromsuch a formal system.

7. RELATIONSHIP OF SURVEY RESULTS TO

OTHER STUDIES

Although those companies monitoring theinternal pipeline measured WIP lead-time esti-

mates were nevertheless still available.Although not directly measured the internalpipeline lead-times were often informallymeasured based on the proportional relation-ship between WIP and lead-time [23]. In cer-tain cases pipeline lead-times for the MPSprocess are determined by gut feel or work-study methods. Thus, lead-time information,although often `stale' due to infrequent moni-toring (and therefore to all intents and pur-poses ®xed), were used for companies' MPS.The scheduling was often undertaken usingMPS modules with MRP/MRPII systems orin separate PC based systems and then input.

Therefore, although feedback paths to theMPS do seem to occur due to their `staleness'and lack of formalisation they cannot be con-sidered to e�ectively `close the loop'. This con-clusion is reinforced by Burcher's studies into400 UK companies [10]. Although Burcherwas more focused on the capacity resourcefeedback loop described in Section 3 thesource of information (that is, what is happen-ing in the pipeline) is still the same. 81% ofcompanies used standard (or ®xed) lead-timeestimates while only 29% of the companies inthe sample formally fed back capacity resourceinformation in a formal manner to the MPS.

Results from the survey suggest that thecompanies involved can be categorised into anumber of groups. In some companies, the en-vironment has always been relatively simpleand as a consequence the planning and control

Fig. 9. Inventory and ordering strategy of the companies included in the survey.

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systems are also relatively simple. In othercompanies the environment was once complexbut attention has focused on re-engineeringtowards a simpler environment and in parallelthis has resulted in a movement from complexCAPM systems to simple planning and controlsystems. The ®nal group of companies arecharacterised by relatively complex productionenvironments. These are typically planned andcontrolled using complex CAPM systems suchas MRP/MRPII. These type of systems arethe most commonly used in UK manufactur-ing. Unfortunately, studies suggest that mostimplementations are considered failures andthat management of the MPS is a critical fac-tor in the successful implementation of thesesystems [24].

8. INDUSTRIAL POSITIONING OF THE `TO-

MAKE' MODEL

The overriding conclusion from the surveyis that companies are collecting pipeline infor-mation usually either in terms of the level ofgoods in the pipeline or in terms of the pipe-line lead-time. It is those companies experien-cing uncertainty in their pipeline who ®nd itnecessary to collect pipeline information.

Those companies with short and consistentpipeline lead-times are in the enviable positionof facing certainty and therefore pipelinemonitoring is not a necessity.

The obvious route for any company is tomove towards greater simplicity and re-engin-eer processes so as to reduce lead-times andincrease consistency. It then follows that plan-ning and control systems can be simpli®ed.However, the reality is that a thorough andfar reaching re-engineering strategy ismeasured in years and `quick hits' are oftenrequired early on to bring the present systemunder control before re-engineering can beginin earnest. This concurs with the IBM stagedapproach to business process re-engineering[25]. Dewar suggests that much can be gainedby focusing on production and inventory man-agement policies prior to or in parallel withe�orts to reduce lead-times and increase ¯exi-bility. Evidence suggests that implementing anordering algorithm utilising pipeline infor-mation in a typical MRP/MRPII environmentcan achieve the goals of improving inventorycontrol management [16].

Without carefully designed management sys-tems, the bene®ts of pipeline lead-time re-ductions may not be fully achieved.

����

Companies explicitlyutilising internal pipeline

information

E

J

BF

I

G

H

C

D

A

LOW HIGH

LO

WH

IGH

Pip

elin

e le

adti

me

var

iab

ilit

y (

log

sca

le)

Average pipeline leadtime (log scale)

Fig. 10. Internal `long and variable' pipeline clustering of the companies surveyed.

Berry et al.ÐPipeline Information Survey126

Page 13: Pipeline information survey:a UK perspective

Investigations suggest that in at least one com-

pany in the survey, considered to have a com-

plex environment and MRPII type systems,

pipeline lead-time reductions of 25±50% have

brought about no increase in stock turn/custo-

mer service level performance. Ironically, this

is the company in which the original `To-

Make' model was developed and implemented

before being abandoned in a drive towards

system standardisation.

Despite the small survey sample size, the in-

dication is that companies are beginning to get

their own houses in order in terms of ensuring

their internal pipelines are simpler and hence

more robust. The evidence of Fig. 10 indicates

that ®ve of the ten companies have both low

average and low variability internal lead-times.

These ®ve companies do not feel it is necessary

to monitor their internal pipeline. In contrast,

as indicated in Fig. 11, only two of the compa-

nies have both low average and low variability

external lead-times. This suggests, despite the

fact that many of the other companies claim

to have or to be moving towards partnering

arrangements, the relationships between the

companies and their suppliers are still not that

well established. This is reinforced by the fact

that six of the remaining eight companies have

both high average and high variability externallead-times and explicitly utilise pipeline infor-mation.

9. CONCLUSIONS

The methodology adopted in the surveyworks well in that it provided a great deal ofinformation in a short period of contact timewith companies, although these were compa-nies known to the research team. The develop-ment of aggregate process ¯ow charts andsubsequent discussions around these diagramswere particularly useful in helping to describepipeline concepts. There was a considerabledegree of similarity between the general con-clusions generated from the survey and thosedescribed by the Burcher survey [10, 19].

. The MPS process was distinguishable andpresent in one form or another in all of thecompanies surveyed. However, variations inunderstanding and de®nitions meant that ittook some time during the interviews toagree to terminology. The survey certainlyindicated the words and phrases that wouldbe best used in dissemination of the projectresults to industry [26, 27].

����

Companies explicitlyutilising external pipeline

information

E

J B

H

C

A

LOW HIGH

LO

WH

IGH

Pip

elin

e le

adti

me

var

iab

ilit

y (

log

sca

le)

Average pipeline leadtime (log scale)

F

IG

D

Fig. 11. External `long and variable' pipeline clustering of the companies surveyed.

Omega, Vol. 26, No. 1 127

Page 14: Pipeline information survey:a UK perspective

. The methods used in the MPS generationprocess varied considerably in terms ofsophistication; from simple manual aggrega-tions of customers orders to the use of soft-ware modules in MRPII systems.

. In most companies there was a lack of for-mal feedback mechanisms and true closedloop systems for production scheduling.

As the clustering analysis has shown it isstill common to ®nd companies operating inhigh degrees of uncertainty. Despite such com-panies striving to re-engineer their supply and/or production pipelines, until they do so, theyare currently experiencing protracted and vari-able lead-times. As these companies arealready gathering pipeline information, usuallyin order to expedite orders, they are ideal can-didates types for incorporating that infor-mation into more robust ordering rulesutilising pipeline information. As the theoreti-cal research suggests, these companies will thenbe able to maintain high service levels whileminimising their stock holding requirements.

APPENDIX A

A1. CASE STUDY 4: COMPANY D2

A1.1. Background.

. The Company is part of the UK basedHoldings Group of companies.

. The site consists of two production units,designated Y and Z undertaking sub-con-tract manufacture of electronic sub-assem-blies.

. The site employs around 200 people.

A1.2. Material ¯ow input±output analysis

Very similar to Company C, a direct competi-tor.

A1.3. Production outputs

Manufacturing outputs consist of assembledand tested electronic assemblies. These consistpredominantly of printed circuit board sub-assemblies (PCB's). However, the site alsoundertakes some ®nal assembly and test ofproducts which are badged for customers.

A1.4. Production process

. The site adopts a make-to-order productionstrategy based on schedules from customers.

. The market place tends to be very dynamic.In general demand in the marketplace isincreasing. Customers are unwilling to investin expensive technology themselves. Sub-con-tractors can also achieve discounts from sup-pliers through pooling material requirementsand bulk purchase. In periods of high endcustomer demand, sub contract manufac-turers are used by OEMs to cope withpeaks. However, when demand slows theyare usually the ®rst to be cut by OEMs, toprotect their own capacity. In periods ofdemand slump, OEMs often move into thesub-contract marketplace themselves tocover overhead costs, increasing competition(e.g. Sony, Panasonic, ICL).

. Of the 250 contract manufacturers inEurope, top 100 are UK based. Company D,along with Company C, U and V constitutethe four main players in the UK andEurope. All having a turnover of between50±70 m.

. Demand can be seasonal depending on theportfolio of customers at any one time.Products destined for the consumer elec-tronics market tend to have a very largeincrease towards Xmas (TVs, Hi-Fis).Products destined for the computer indus-try tend to have end of period ramping.Products for the automotive market vary.

. Capacity can be increased by increasingshifts, and using temporary labour.Production output tends to be limited bythe availability of high technology assem-bly and test equipment rather than labouravailability.

A1.5. Production inputs

. Material inputs consist of a range of printedcircuit cards, active electronic componentsand passive electronic components and arange of electronic sub-assemblies, and plas-tic mouldings.

A1.6. Supply chain material process ¯ow analysis

Very similar to Company C.

2Capital italic letters are used for company and similarnames (this is to preserve anonymity).

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A1.7. Supply

. The supply base varies depending on therange of contracts that are presently beingrun.

. Materials may be sourced from UK, Europeor globally, at any one time the size of thecurrent supply base may run into the 500's.

. Depending on the nature of the contractand the customer concerned, Company Dmay be left to choose appropriate suppliersor be instructed to choose the most appro-priate source from a customer approvedlist.

. In some contracts material is provided toCompany D on a `free issue' mechanism.In e�ect, the customer purchases the ma-terial, which is then supplied to the sitedirectly from the supplier or via the custo-mer.

. Supply lead-times can vary between 4±24weeks. However, the supply of electroniccomponents is notoriously prone to sur-plus±shortage behaviour. In the event of aworld shortage (either real or possibly en-gineered), the company can be put on ansupplier allocation list which shares outavailable supply. The only other possibilityin such scenarios is to seek supply fromcomponent distributors who may charge3±5 times the cost.

A1.8. Material stock

. Goods undergo inspection.Raw materials arestocked according to contract, for simplicity.

. This can lead to a considerable degree ofduplication of part numbers across con-tracts.

. Around 3±4 weeks of Raw material Stockare present at any one time.

. Materials are kitted into batches, and enterthe shop¯oor.

A1.9. Production

. PCBs may be assembled by a variety ofdi�erent techniques including SMT, AutoInsert and Manual Insert.

. In general the process begins with the ad-dition of surface mount components to araw card using SMT lines.

. Components may be added on both the top-side and bottom side of the card insequence.

. Large batches of cards are processed at onetime, because change-overs are lengthy, upto a shift, and SMT is regarded as a bottle-neck.

. In some cases PCBs have further com-ponents inserted using automatic insertionmachines and manual insertion methods onassembly lines.

. Components inserted by the latter twomethods are secured in place on ¯ow soldermachines or by hand soldering in situationswere components would be damaged by the¯ow solder process.

. PCBs are tested using both In-circuit andFunctional testing equipment.

. PCBs may pass into the ®nished goodsstores. Alternatively they may form part oflarger assembly which is assembled togetherby hand and tested as appropriate.

. WIP tends to accumulate after SMT due tothe large batches being processed at any onetime. Material then tends to trickle throughthe remaining operations in smaller batches,often accumulating before test operationswhich are suggested as being another shop-¯oor bottleneck.

. Due to the contract nature the business, arange of di�erent products may be beingassembled at any one time, with varyingdegrees of complexity.

. Consequently manufacturing lead-times varyfrom product to product (contract) with anaverage of around 2±3 weeks on average. Oneweek to get over SMT, and one week to getover the rest of the process.

A1.10. Finished goods stocks

. At any one time there is little ®nished goodsstocks, since products tend to be shipped tocustomers within days.

A1.11. Distribution channels

. A variety of possibilities exist. Company Dmay be acting as a ®rst tired supplier or anOEM. Therefore products may be shippedto manufacturing sites, distribution ware-houses or retail outlets.

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. Products are shipped by third party distri-bution ®rms.

A1.12. Logistics control analysis

. Sales people chasing contracts.

. Often compete in tendering process againstthe likes of Company C.

. OEMs often have 2±3 suppliers supplyingsame type of products, will play one o�against another.

. Contracts tend to be open ended, customerwants so many a month for a year, etc.

. Orders from Company F are input directlyinto the Operations Planner (equivalent tothe MPS). Orders from customers who arenot from Company F are input into theCommercial Dept., and then fed on to thePlanner.

. Demand schedules input on daily, weekly,monthly frequency depending on customer.

. The degree of changeability varies.

. Often schedules consist of a period of ®rmdemand (often 1 month) and a period offorecast demand (extending out for 2±3months) to allow the ordering of material.

. Customer usually committed to take the pro-duct produced for the one months ®rmdemand. Liability for material purchased inthe period beyond this period is usuallynegotiated, especially when materials havelong lead-times.

. Demand schedules may adopt a number ofdi�erent forms including EDI, Fax, mail andtelephone (needs written con®rmation).

. In a number of cases, Company D arerequired to commit themselves to supply thisdemand or suggest an alternative plan andfeed this information back to the customer.

. In all cases order acknowledgements arerequired.

. The Operations Planner inputs orders onto aPC to develop a MPS.

. Experience is used to decide the best pro-duction plan in terms of aggregate concernsand interactions in the mix. Schedules aresmoothed and batched as appropriate.

. The MPS provides the focus of attention forweekly Commit/Production meeting.

. The meeting involves the Operations Plannerand representatives from a variety of di�er-ent functions who provide information con-cerning orders, production capacity and

materials availability. It decides the weeklymanufacturing plan for production.

. In general the shop¯oor attempts to pushbatches to a post SMT stage of production.Material ¯ow then tends to be expeditethrough the remaining process operations.

. In general production will make the ®rmmonths demand. In many cases customerswill be employing JIT control, thus variationsmay occur between ®rm demand and what iscalled o�. Products that are not called o� arestored or may be shipped regardless.

. Products may be called o� daily, weekly ormonthly.

. The MPS provides the focus for the MRPmeeting held monthly.

. The meeting involves the Operations Plannerand representatives from a variety of di�er-ent functions, though primarily from ma-terials. The meeting decides what is to beplaced into materials planning systems andwhich system is to be run.

. Two materials planning systems are used onthe site.

. A standard MRP system on a mainframe isrun every two weeks which generates newpurchase orders across an entire customer(s)product range.

. In parallel, a PC based system MRP systemis also used, and run daily if required. Thisis used for products that tend to havechangeable bills of material, or for contractswith a limited product range.

. Within these MRP systems, manufacturinglead-times and material supply lead-timestend to be ®xed and not updated.

. The MPS input into the MRP system andgenerates a series of raw material purchaseorders which input into Purchasing.

. Orders are faxed to suppliers and con®rmed.

. An agreement to supply material formalisesthe ordering procedure and subsequentlygenerates a purchase order which corre-sponds to an o�cial order to supply ma-terial. This is transmitted to the supplier.

. Along with the meetings that control pro-duction and material supply, the companyalso undertakes a monthly forecast meeting.This meeting is attended by a variety ofdi�erent functional personnel. The meetingproduces a monthly manufacturing forecastand ®nancial budget which is sent to theparent company.

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. WIP is recorded on a daily basis using physi-cal counting and manual calculations to gen-erate a WIP scan report.

. This tends to be used as a method for track-ing the progress of batches and as an aid forexpediting rather than an information sourcefed back to the MPS level to limit over-ordering on the production system.

. Shop¯oor and supplier lead-times areregarded as ®xed for planning purposes.There is little, if any update of planned lead-times against actual lead-times. In essenceWIP and Pipe levels are regarded asWIP = M/F Lt�Av Comrate andPipe = Supplier Lt�Av `Orate'.

ACKNOWLEDGEMENTS

The authors would like to thank the UK Engineering andPhysical Science Research Council for sponsoring theresearch under grant GR/H 88930, `Dynamic analysis ofan industrial adaptive `To-Make` ordering model forexploitation in supply chains and individual business pol-icies'. We would also like to thank the Director of theresearch team Prof. D. R. Towill for his support andadvice, and our former colleague Dr. K. Hafeez who wasinvolved in the data collection. The advice from the refer-ees and the Editor has also been extremely constructiveand helpful.

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ADDRESS FOR CORRESPONDENCE: Dr MM Naim, LogisticsSystems Dynamics Group, Department of MaritimeStudies and International Transport, Faculty ofEngineering and Environmental Design, University ofWales, Cardi�, P.O. Box 924, Cardi�, CF1 3TS, UK.

Omega, Vol. 26, No. 1 131


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