JWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Excel Models forBusiness and OperationsManagementSecond EditionJohn F. BarlowiiiJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0viJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Excel Models for Business and OperationsManagementSecond EditioniJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0About the authorDr Barlow holds degrees in mathematics, computer science, and mechanical engineering. Aswell as extensive teaching experience previously at the Universities of Cape Town, SouthAfrica and Wollongong, Australia he has held various positions in computer consultancy andthe petroleumindustry. He has published numerous papers in the areas of computer applicationsand systems management.iiJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Excel Models forBusiness and OperationsManagementSecond EditionJohn F. BarlowiiiJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Copyright C 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester,West Sussex PO19 8SQ, EnglandTelephone (+44) 1243 779777Email (for orders and customer service enquiries): cs-books@wiley.co.ukVisit our Home Page on www.wileyeurope.com or www.wiley.comAll Rights Reserved. No part of this publication may be reproduced, stored in a retrieval systemor transmitted in any form or by any means, electronic, mechanical, photocopying, recording,scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 TottenhamCourt Road, London W1T 4LP, UK, without the permission in writing of the Publisher.Requests to the Publisher should be addressed to the Permissions Department, John Wiley &Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailedto permreq@wiley.co.uk, or faxed to (+44) 1243 770620.This publication is designed to provide accurate and authoritative information in regard tothe subject matter covered. It is sold on the understanding that the Publisher is not engagedin rendering professional services. If professional advice or other expert assistance isrequired, the services of a competent professional should be sought.Other Wiley Editorial OfcesJohn Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USAJossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USAWiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, GermanyJohn Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, AustraliaJohn Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1Wiley also publishes its books in a variety of electronic formats. Some content that appears in printmay not be available in electronic books.Library of Congress Cataloging-in-Publication DataBarlow, John F.Excel models for business and operations management / John F. Barlow. 2nd ed.p. cm.ISBN-13 978-0-470-01509-4ISBN-10 0-470-01509-81. Microsoft Excel (Computer le) 2. BusinessMathematical modelsComputer programs. 3. BusinessDecision makingMathematical modelsComputer programs. I. Title.HF5548.4.M523B37 2005658

. 05554dc22 2005003239British Library Cataloguing in Publication DataA catalogue record for this book is available from the British LibraryISBN-13 978-0-470-01509-4ISBN-10 0-470-01509-8 (PB)Typeset in 10/12pt Times by TechBooks, New Delhi, IndiaPrinted and bound in Great Britain by Biddles Ltd, Kings Lynn,This book is printed on acid-free paper responsibly manufactured from sustainable forestryin which at least two trees are planted for each one used for paper production.ivJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0To Maryand the Gang of 4vJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0viJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0ContentsPreface xiii1 A systems view of business 1Overview 1A systems view of business operations 2A manufacturing business model 3Finance and cost accounting 3The marketing function 5The production function 5Management decision-making 12Enterprise resource planning (ERP) 15References and further reading 162 Model-building tools 17Overview 17Modelling characteristics 18Risk and uncertainty in decision-making 20Linear programming (LP) 21Using Excels Analysis ToolPak 26Statistical methods 27Decision analysis 34Simulation 42Excel functions used in model-building 46Exercises 49References and further reading 51viiJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0viii ContentsPART 1 BUSINESS MODELS 533 Financial models 55Overview 55Financial statements 56Ratio analysis 56Net present value (NPV) 59Investment appraisal 61Portfolio management 64Capital budgeting using decision trees 68Cash ow analysis 69Investment nancing: a simulation model 74Financial planning 78Commercial add-in products for Excel 82Excel functions used in model-building 82Exercises 86References and further reading 884 Investment analysis models 89Overview 89Risk preference attitudes 90Utility theory 91Portfolio theory: the Markowitz model 94Portfolio analysis: the efcient frontier 97Single index model (SIM) 101The capital asset pricing model (CAPM) 106Bond valuation 108Duration and bond volatility 113The BlackScholes option pricing model 117Excel functions used in model-building 120Exercises 124References and further reading 1265 Worksheet applications in cost accounting 127Overview 127Cost-volume-prot analysis 128Depreciation 130Equipment replacement 132Statistical replacement analysis 136Simulation model for replacement/repairs 140Comparison between simulation and statistical results 144Budgeting 144JWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Contents ixJob costing 150The learning curve 155Checking the accuracy of learning curves 158Excel functions used in model-building 160Exercises 163References and further reading 1666 Marketing models 167Overview 167Organising and presenting data 167Correlation analysis and linear regression 170Forecasting time series and exponential smoothing 174Forecasting exponential smoothing 178Salesforce models 186Goal programming 190Excel functions used in model-building 200Exercises 201References and further reading 2057 Purchase order processing: a database application 206Overview 206Creating a simple macro 207Purchase order processing 209Creating the title screen 210Products and suppliers worksheets 214Creating the purchase order form 215Creating the database and its associated macros 219Macros for transferring data into the database 221Adding macros to buttons 224Amending purchase orders 225Printing purchase orders 230Protecting the POP database application 232Excel functions used in model-building 236Exercises 237References and further reading 239PART 2 MODELS FOR OPERATIONS MANAGEMENT 2418 Statistical applications in quality control 243Overview 243Probability distributions 244Acceptance sampling 249JWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0x ContentsEstimation drawing conclusions from samples 253Hypothesis testing checking out a claim! 257Analysis of variance (ANOVA) 258Statistical process control 263Excel functions used in model-building 272Exercises 275References and further reading 2789 Inventory control models 279Overview 279Glossary of inventory terms 280Characteristics of inventory models 281Deterministic models 282Production order quantity model 284Inventory models with constraints 291Probabilistic models 293Inventory control: a simulation approach 304Material requirements planning 307Lot-sizing methods 315Just-in-time (JIT) approach to inventory management 318Excel functions used in model-building 318Exercises 319References and further reading 32310 Models for production operations 324Overview 324Logistics models 325Other network ow applications 331Production planning and scheduling 339Queuing models 353Excel functions used in model-building 362Exercises 364References and further reading 36911 Project management 370Overview 370Project management techniques 371The project network 371Simulation model for project management 392Exercises 396References and further reading 399JWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Contents xiAppendix Excel refresher notes 400Basic Excel commands 400Drawing charts with ChartWizard 405Object linking and embedding (OLE) 407Index 409JWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0xiiJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0PrefaceThe popularity of spreadsheets in both academia and the business world has increased consid-erably over the past six years. Microsofts Ofce, which includes Excel as a core element, isnowone of the most widely used software products around. It is therefore no surprise to see thatOfce has undergone several revisions since the rst edition of this textbook was published.The objective of the newedition, however, remains the same: to help readers develop their ownworksheet (Excels term for a spreadsheet) models.The book takes a structured view of management decision-making by integrating the ac-tivities of a manufacturing organisation. Everyday examples from nance, marketing, andoperations management form the basis of the books hands-on development models. The textis entirely assignment-based and uses Microsofts Excel software to develop over eighty mod-els. As in the previous edition, the emphasis is on the practical implementation of real-worldmodels rather than traditional theoretical concepts. The books active learning approach en-courages the reader to focus on developing skills in how to build a model while summarisingthe mathematical logic as to why the model is so constructed.The books primary objective is to help the reader put theory into practice. In order to createan effective spreadsheet, a student must understand what variables should be included and whatrelationship exists between the variables, i.e., the models formulation. Allowing students tothink through the question of why a model is built in a particular way helps to increaseanalytical skills. If students are encouraged to use their own initiative through groundworkassignments and class discussions, they are better placed to transfer decision-making andproblem-solving skills into the workplace.The books format is subject-focused following standard business/operations managementtexts. The mathematical concepts of management science/operations research provide thetools for model building. There are two introductory chapters, the rst showing how thebooks chapter topics are interrelated while the second chapter explains the main model-building techniques that are used throughout the book. The rest of the books nine chapters aredivided into two main parts, each containing models for business and operations managementrespectively. End-of-chapter assignments (with answers) allow the student to develop theiranalytical skills by (i) modifying existing models, and (ii) building new applications.The text is ideallysuitedtobusiness studies, nance, accounting, andoperations managementcourses which offer practical computing skills as an integral part of the course syllabus. ModelsxiiiJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0xiv Prefacethat are developed cover the areas of nance, accounting, marketing and forecasting, statisticalquality control, logistics and distribution, production planning, job scheduling and sequencing,inventory control including material requirements planning (MRP), and project management.In addition, a chapter is devoted entirely to the development of an internal database applicationusing Excels Visual Basic for Applications (VBA) language. This chapter includes a completelisting of the eleven macros used to build the purchase order processing (POP) system.Formulae templates for all models are provided throughout the text. While it is assumed thatthe reader is already familiar with Excels basic concepts, brief refresher notes are included asan appendix. Where Excels functions are introduced for the rst time, a full description foreach function is given at the end of the relevant chapter. For readers who want to pursue themathematical techniques behind specic models in greater depth, a bibliography is providedat the end of each chapter.There is an accompanying website at http://www.wiley.com/go/barlow2e containing allworksheet models developed throughout the text. The website contains ten workbooks, eachworkbook le corresponding to a chapter in the book. The workbook les are named asChapter2, Chapter3, . . . Chapter11. (Note: Chapter 1does not containanyspreadsheets.) Withineach workbook, worksheets represent the gures that appear in that chapter, e.g., Chapter3contains worksheets Fig. 3.1, Fig. 3.2, Fig 3.3, etc. Solutions to all end-of-chapter exercisesare available to adopters of the text on the website.NEW FOR THE SECOND EDITIONr A new chapter entitled Investment Analysis Models has been added. As well as expandingthe subject of portfolio theory introduced in Chapter 3, asset pricing models including thecapital asset pricing model (CAPM) and single index model (SIM) are covered. Other topicsdiscussed are bond valuation, duration, and the BlackScholes option pricing model. Thisnew chapter should be of particular interest to both students and practitioners in the area ofnance.r The Time Series and Exponential Smoothing section in Chapter 6 now provides a modelfor the Holt-Winters triple-parameter method for multiplicative seasonal effects.r The Production Planning and Scheduling section in Chapter 10 has been expanded to coverpriority job sequencing. A VBA routine is included for Johnsons Rule for sequencing aseries of jobs through two facilities.r All models and Appendix notes have been upgraded to reect the latest versions of appli-cations software and operating systems, namely Microsofts Ofce 2003 and Windows XP.r New topics such as enterprise resource planning have been introduced where appropriate,and end-of-chapter bibliographies have been updated.ACKNOWLEDGEMENTSI am grateful to the reviewers for their many helpful comments and suggestions for improvingthe book. These views have been incorporated wherever practicable. It is also encouraging toJWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0Preface xvsee that other authors nd merit in adopting an active learning approach to problem solving,and their important contributions have been recognised in the end-of-chapter bibliographies.I would like to thank editors Sarah Booth, Rachel Goodyear and David Barnard for all theirsupport throughout the books production. Finally, with a text involving so many formulae, theresponsibility for any errors or omissions that remain undetected is mine alone. Feedback oncorrections, questions, or ideas is always welcome and will be duly acknowledged.JWBK022-FM JWBK022-Barlow March 18, 2005 7:52 Char Count= 0xviJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 01A systems view of businessOVERVIEWThis textbook develops models for various aspects of business and operations management.In order to illustrate how the activities of a typical manufacturing business are interrelated,a systems approach is now presented. The systems approach provides an overall view of anorganisations activities whereby an organisation is separated into identiable subsystems ordepartments. All such departments are interdependent and perform specic tasks of workwhich contribute to the organisations goals. The simplest model of a business system consistsof three basic elements, namely inputs, processes, and outputs (Figure 1.1). Output informationis used as a control mechanism normally called a feedback loop to correct deviations fromplanned performance.For example, consider a bakery. The inputs consist of the raw materials that are used tomake bread, namely our, yeast, and water. The actual process is the baking, performed in anoven in which the ingredients are converted into outputs, i.e., bread. In this case, the processis controlled by monitoring the ovens output, i.e., temperature. A temperature feedback loopthus ensures that the correct baking conditions are maintained.Outputs can be either nished goods or services. A manufacturing business produces atangible output such as a car or furniture whereas service-orientated businesses, e.g., a bank orhospital, are more customer focused. Generally, service operations use more labour and lessequipment than manufacturing. The word throughput is sometimes used to refer to items thatare still in the process stage and have not yet been converted into nished goods.INPUTSLabour, materials, equipment,capital, managementPROCESSESConversion of inputsinto outputsOUTPUTSFinished goodsand servicesFeedback loopFigure 1.1 Systems view of business.1JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 02 Excel Models for Business and Operations ManagementA SYSTEMS VIEW OF BUSINESS OPERATIONSIn most organisations, departments are formed by grouping similar functions together. While allorganisations do not have the same functions, one common approach to functional organisationis to have four main divisions, namely human resources, marketing, nance/accounting andproduction. Within each of these four divisions, there are further functional subsystems ordepartments, creating a hierarchical structure as shown in Figure 1.2. In this textbook, the termbusiness management is interpreted in the wider sense of covering all four divisional areaswhile operations management is concerned specically with the activities of the productiondivision.The human resources division deals with the human aspects of an organisation such aslabour relations, the work environment, and in-house training. Because personnel activities arepeople-orientated, the human resources division use computers chiey as information-retrievalsystems, utilising records which are stored in a database. Mathematical applications relatingto personnel actions are restricted here to staff planning models in marketing.The objectives of the marketing division are twofold (i) to identify and (ii) to satisfy customerneeds at a prot to the organisation. In other words, the marketing division not only helpsto sell protably what the production division produces but it also helps to determine whatthose products should be. Market research is used to identify what consumers actually want,including their preferences and behaviour patterns. The product development department thentranslates these consumer preferences into general specications for a newor modied product.At a later stage, the general specications are rened into detailed product design by the pro-duction division. Product development is an example of an interfacing activity requiring inputfrom both marketing and production. Other marketing subsystems which contribute to cus-tomer sales are sales forecasting, advertising, salesforce allocation and scheduling, and productpricing.Because the nance/accounting function focuses on the organisations economic well-being, accounting activities extend into all business operations. Financial accounting provideseconomic information for managerial decision-making within the company. Strategic andMarketingForecastingOthersubsystemsProductionMarketresearchOperationsManaging directorFinance/AccountingCostaccountingOthersubsystemsHumanresourcesFinancialaccountingTrainingFigure 1.2 Typical functional organisation of a manufacturing business.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 3operational planning, performance measurement and control, and product costing are com-mon accounting functions that must be performed by management. Another important aspectof managerial control is budgeting. Budgeting is the practical outcome of business planningand is used to ensure that expenditure does not exceed income, i.e., to ensure that the companymakes a prot.The main objective of the production division is to convert raw materials into nishedgoods. The rst step in the manufacturing process is to decide what is to be made and whenit is needed. After customer requirements have been established, a product is then designed.This product may be a modication of an existing product or it may be completely new.The resources required to manufacture the product are specied and, where appropriate, thenecessary amounts quantied. These resources include raw materials and spare parts, labour,and production facilities.After the product design and manufacturing processes are determined, the product must bescheduled along with any other products made by the organisation. An effective productionschedule is a vital element of production operations. It involves checking production capacity,i.e., is there sufcient labour and materials available, is machine capacity adequate to performthe job, etc.? Having established that there is adequate capacity, the next step is to order thenecessary raw materials and parts. Machines and personnel are then scheduled to perform thenecessary manufacturing steps. During certain stages of production, the product is examinedand compared to its design specications for performance and quality, i.e., quality control isperformed. If the nished product meets all the original design specications, it is shippeddirectly to the customer or stored in the warehouse.A MANUFACTURING BUSINESS MODELThe interrelationships between the various business functions outlined above can be bet-ter understood by examining a model of a manufacturing organisation. The model ofFigure 1.3 is not a mathematical model, but a diagram showing the basic data ows betweenthe main subsystems in the organisation. The shaded area contains the key subsystems withinthe production division.FINANCE AND COST ACCOUNTINGThe two main categories of accounting are nancial accounting and management or costaccounting. The terms management accounting and cost accounting are often used inter-changeably. Strictly speaking, cost accounting refers to a manufacturing business, and focuseson the costs of raw materials, labour, and overhead expenses incurred in the production ofnished goods. Management accounting is a more generic term referring to the managerialfunctions of organisational planning and control. Performance measurement and control, prod-uct costing, and purchasing decisions are common managerial accounting functions that mustbe performed by a companys management.The nance function covers both nancial accounting and nancial management. Finan-cial accounting is concerned primarily with providing information for parties external to theJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 04 Excel Models for Business and Operations ManagementMARKETING SalesforecastingCUSTOMERSNew product Customised orderInventory statusSUPPLIERSCUSTOMERSACCOUNTS PurchasingDistributionStandard orderEngineeringdesignProduction planningand controlMaterialrequirementsplanning (MRP)Cost accountingand controlProductionoperationsStoreskeeping(materials/parts)Quality control InventorycontrolWarehousing(finished goods)Figure 1.3 Manufacturing business model.business such as investors, banking institutions and government agencies. If a company wantsto borrow money, it must provide the lending institutions with information to show that thecompany is a sound investment. Likewise, shareholders want to see nancial statements indi-cating how well the company is doing and how much their investment is worth!The objective of nancial management is twofold (i) to ensure that there are sufcientfunds available to meet the organisations nancial obligations and (ii) to maximise the returnson invested funds that are not needed to meet current commitments. In order to maximiseprot, nancial managers must decide whether the benets accruing from any investmentare sufcient to justify the original outlay. Investment situations include capital budgetingdecisions relating to expansion plans or new product development, and long-term investmentssuch as government bonds or shares in other companies.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 5One important aspect of managerial control is budgeting. A budget is a plan usuallyexpressed in monetary terms covering a xed period of time, e.g. one year. It shows theactivities that the company intends to undertake in order to achieve its prot goal. While thebudget provides projected data, the cost accounting subsystem produces actual data. Variancesfrom set targets are found by comparing actual costs with budgeted or standard costs. Forexample, a negative or unfavourable variance occurs if actual production uses materials thatare more costly than planned. On the other hand, if actual labour hours used in the manufactureof a product are less than budgeted gures, then a positive or favourable variance results.THE MARKETING FUNCTIONThe marketing function utilises a considerable amount of qualitative information which intro-duces uncertainty into the decision-making process. Qualitative decisions that must be takenby marketing managers include anticipating customer needs, forecasting product sales, andevaluating new sources of competition. Decision-makers often use spreadsheet models as aquantitative tool to obtain answers to various what-if questions. When managers are able tosee the immediate effect of changes to sensitive parameters such as sales volume or productprice, they are better placed to evaluate feasible alternatives. Because models provide valu-able help in performing such sensitive analysis, they are often called decision support systems(DSS).No organisation can function effectively without a forecast for the goods or services whichit provides. A key objective of the marketing division is to produce sales forecasts. Since theprojections of sales volumes for future periods often formthe basis of organisational objectives,their accuracy is of vital concern. Many statistical models have been developed in an effort toimprove forecastingaccuracy, includingregression-basedmodels, the moving-average method,and exponential smoothing techniques.Production may be initiated on the basis of either sales forecasts or rm customer orders.Goods that are produced on the basis of a forecasted demand use a product-orientated strategywhich involves the continuous production of a standardised product such as glass, paper,cement, and steel. Companies that manufacture the same high-volume standardised producteach day are in a position to set standards and maintain a given quality.Some customer orders are for one-off items which involve the production of customisedproducts, in which case a process-orientated strategy is used. An organisation which usesa process-orientated strategy produces low-volume, high-variety products. The productionfacilities must have a high degree of exibility in order to handle the frequent process changesthat are required to produce different items. Maintaining standards in such a rapidly changingprocess environment is much more difcult than in a standardised product-orientated business.THE PRODUCTION FUNCTIONThe main purpose of the production function is to transform raw materials into nished goods.The manufacturing process covers the full life-cycle of a product from its inception to com-pletion. Important decisions must be taken on what items to produce, how to design andJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 06 Excel Models for Business and Operations Managementmanufacture them, which raw materials will be required, and how best to ensure that the prod-ucts meet specied quality and performance. The following sections summarise the essentialproduction subsystems.Production Planning and ControlProduction planning determines how many items to produce, when to produce them, andwhat facilities are required to produce them. The amount of production is determined by(i) sales forecasts and (ii) specic customer orders. The timing of production depends on(i) availability of labour and materials required for the job and (ii) estimated times required toperform production operations. The production facilities required to manufacture the item areprescribed by the design specications.A key output from production planning is the master production schedule (MPS) whichshows how many items must be produced and when they will be produced in order to meetcustomer demands. Because the MPS is based on customer orders and forecasts provided bythe marketing division, it is an important link between marketing and production. It showswhen incoming sales orders can be scheduled for production operations and when nishedgoods can be scheduled for delivery. The MPS also provides important inputs to the materialrequirements planning (MRP) system, which is discussed below.Before an MPS can be developed, estimates of the amounts of each resource needed tomeet MPS requirements must be calculated. A production schedule is therefore dictated by theresource constraints on materials and labour availability, machine capacity, and the productiontime pattern. Effective capacity planning will indicate whether a proposed production scheduleis feasible or not. Inadequate machine capacity could rule out a proposed schedule, or overtimemay be necessary to overcome personnel limitations.Production control involves the co-ordination of three main activities, namely, (i) dispatch-ing, i.e., scheduling which machines and operators will performwhich steps in the manufactur-ing process for each item (ii) monitoring actual operations, and (iii) taking corrective actionswhen and where necessary.Material Requirements Planning (MRP)In manufacturing situations, there is usually an inventory of components (or raw materials)which are used solely in the production of nished products. Since there is no outside demandfor this inventory, there is no sense in stocking it until it is needed in production operations. Thetwomainobjectives of material requirements planning(MRP) are (i) toreduce order-processingdelays by ensuring that materials and parts are available when required for production opera-tions and (ii) reduce inventories and their associated costs by holding only those componentsand items that are really needed. MRP thus combines two of the most important activities inthe manufacturing process, namely, production scheduling and materials control.MRP is a system for managing inventories by anticipating their use. It helps to reduceinventory levels by quickly determining how much materials/components to order and whenthose orders should be placed. MRP is most appropriate where a company manufactures manyJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 7EngineeringdesignBill of materials(BOM)Master productionschedule (MPS)Material requirements planning (MRP)Inventorystatus detailsOutput reportsPlanned orders schedule; Inventory status updatesChanges (of due dates, quantities, etc.) in planned ordersPlanning, performance and exception reportsMarketing InventorycontrolFigure 1.4 Basic input to an MRP system.products and where product demand is variable. For example, a company may manufacture 100different items each requiring many components and materials in its assembly. An MRP systemmust be able to calculate the materials/parts requirements for all 100 items over a xed period.However, much of the materials and components will be common to many of the items. TheMRP system aggregates gross requirements across all the items in order to determine whatexactly to order. An MRP system has three major inputs the master production schedule(MPS), the bill of materials (BOM) and inventory status details as shown in Figure 1.4.The master production schedule (MPS) which is discussed in the production planning sectionabove, provides input details of what nished goods are required and when they are needed.The bill of materials (BOM) is a design document listing all of the materials, components, andassemblies/subassemblies needed to make a nished product.The BOM is developed by the engineering design department and is based on productspecications provided by the marketing division. It not only gives details of a productscomponents but also shows how it is assembled and how many of each component is required.The primary MRP input from the BOM is the products structure tree, which depicts thehierarchical levels of subassemblies and components required to make the product, as shownin the bicycle structure tree (Figure 1.5). The structure tree shows three hierarchical levels withbracketed numbers indicating the number of assemblies and components required to make oneitemon the level above. For example, 50 level-2 spokes are required to make one level-1 wheelassembly, while two wheel assemblies are required in the manufacture of one level-0 bicycle.The third MRP input, namely inventory status details, gives full and up-to-date informationon each inventory item, including quantities on hand, gross requirements, how much hasbeen ordered, safety stock levels, etc. These inventory records also contain details of eachcomponents lead time, i.e., the amount of time taken between placing an order and gettingthe item in stock ready for use. Lead times are a continuous source of uncertainty due to themany activities involved, including timing of order placement, item availability, transportationdifculties, quality control, etc.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 08 Excel Models for Business and Operations ManagementBicycle Level 0Level 1Level 2Frame assembly (1) Wheel assembly (2)Chassis (1) Seat (1) Handlebars (1) Spokes (50) Rim (1) Tyre (1)Figure 1.5 BOM product structure tree.PurchasingA considerable amount of managerial effort goes into ensuring that the right materials aredirected to the right place at the right time and at the right cost. Managing materials insuch an effective way is not easy. In a manufacturing environment, materials managementinvolves co-ordinating the activities of three interrelated areas, namely materials purchasing,inventory planning and control, and the delivery/inspection system. The purpose of materialsmanagement is to increase the efciency of production operations by integrating all materialacquisition, movement, and storage activities within the company.Traditionally, the purchasing function was concerned solely with drawing up a list of reli-able suppliers and then using a lowest cost criterion to choose the best supplier. However,this narrow view of buying materials at lowest cost represents only one aspect of purchasingmanagement. Nowadays, there is more emphasis on developing a satisfactory supplier-buyerrelationship whereby the sharing and exchanging of ideas can benet both sides. The com-petitive advantages to be gained from purchasing depend upon both parties working closelytogether to identify mutual interests. For example, suppliers can use their knowledge of whatis available in the market-place to suggest substitute materials that are cheaper or more reliablethan those currently being purchased by an organisation.The importance of having a mutual understanding between suppliers and purchasers ishighlighted in the popular just-in-time (JIT) approach to materials purchasing. The JIT phi-losophy of purchasing items from suppliers just when they are required, reduces and in somecases eliminates inventories. Most companies appreciate that capital can be wastefully tied upin inventories which sit around gathering dust. The JIT approach is seen as a way to eliminatesuch waste by synchronising manufacturing processes and reducing inventory holding costs.However, for JIT to be successful, purchasing management must have condence that theirsuppliers can deliver materials in the required amount and at the right time. Any delay in thesupply would cause serious production problems hence the need for a mutual value-addedrelationship!The activities of receiving, inspecting, and storing materials are other important aspects ofpurchasing management and also form part of the logistics function which is discussed below.If materials or goods arrive late, production operations may be halted or sales lost. The problemof late deliveries must be properly addressed in order to avoid friction between supplier andpurchaser. Does the problem lie with the supplier or, as can often happen, with the purchasersthemselves? Poor scheduling by the purchaser can allow insufcient time for either punctualJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 9delivery or proper quality inspections. Information concerning the receipt of wrong materialsas well as items that have been damaged in transit, should also be relayed without delay to theproduction control and accounting departments.Inventory ControlBusinesses can only predict what customers will buy and when they will buy it. To overcomesuch uncertainty, stocks are kept to ensure that anticipated demand can be met. Stockholdingcan therefore be viewed as a buffer between supply and demand. For example, raw materialssuch as coal and fuel must be scheduled and stockpiled for the production of electricity, whilebanks have to maintain a certain level of cash inventory to meet customer needs. The maintypes of inventory are (i) raw materials (ii) work-in-progress (also called work-in-process orWIP) which represents partly nished products (iii) nished goods, i.e., completed productsready for shipment.Controlling inventory is an important aspect of good management. In the past, inventorieswere often overstocked just in case something went wrong, i.e., items were over-ordered toprotect against supplier shortages or as a hedge against uctuating price changes. However,excessive inventories tie up valuable capital as well as taking up expensive warehouse space.On the other hand, valuable sales may be lost if customer orders cannot be met because ofinsufcient stock. Likewise, production operations can be brought to a halt because an itemis not available. Using computer models can help reduce inventory levels by providing moreaccurate forecasts on the quantity and timing of inventory transactions.Common objectives of inventory control are (i) to ensure that there is a sufcient supplyof nished goods to meet anticipated customer demand (ii) to ensure that there is a sufcientsupply of materials to enable production operations to operate smoothly and (iii) to reduceinventory costs by taking advantage of quantity discounts or buying when prices are lower.Thus, inventory management strives to have exactly enough inventory on hand to satisfy alldemands without having any excess.There are two main classes of inventories, namely those with dependent-demand items andthose with independent-demand items. Independent-demand items are those whose demandsare unrelated to anything else which the company produces or sells. Independent-demandinventory usually consists of nished goods whose demand is based on uncertain environmentalfactors such as sales forecasts, consumer trends, etc. Independent demands are therefore fullof uncertainties as to how much is needed and when it is required.On the other hand, dependent-demand items are those that can be directly linked to a specicend-product such as the bicycle shown in Figure 1.5. In this situation, the product structuretree shows how many handlebars, spokes, tyres, etc., are required to make one bicycle. Herethe demand for a bicycle automatically triggers demand for known quantities of parts andmaterials, i.e., there is no uncertainty associated with their demand. For example, if a customerorders six bicycles, then six handlebars, twelve tyres, 600 spokes, etc., must be in stock if theorder is to be met.Quality ControlIn a manufacturing environment, the quality control (QC) function ensures that all productsmeet the standards specied by the engineering design department. The two main approachesJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 010 Excel Models for Business and Operations Managementto quality control are acceptance sampling and statistical process control. The termacceptancesampling refers to statistical techniques that are used to accept or reject a batch of items onthe basis of a sample test or inspection. This traditional approach to quality control involvesrandomly selecting a sample from a batch of items and applying various tests to each itemin the sample to see if it works as intended. The QC manager then extends the samples testresults to the whole batch. For example, if 2% of a samples items are found to be defective,the manager concludes that 2% of the whole batch are also faulty.Acceptance sampling involves the risk of nding too many or too few defective items in arandom sample. If the QC manager is unfortunate in picking out too many defectives, a wrongdecision will be made when rejecting the whole batch unnecessarily. The same outcome appliesto accepting a batch because a sample was lucky enough to include very few faulty items.Recent developments in computer-based systems using mechanical, optical and electronicsensors can help in non-destructive quality control, i.e., in the measuring and testing of items.Technology in testing equipment has advanced to the point where many manufacturing com-panies can nowcheck out all items, thus achieving 100%quality control. The same technologycannot of course be applied to destructive quality control whereby the testing of a productinvolves tearing it apart to see how well it is made, e.g., strength tests of materials. In thissituation, acceptance sampling is used.The well-known advocate of quality management, W. Edwards Deming, stated that manage-ment is responsible for 85% of quality problems in a factory environment with workers beingresponsible for only 15%. He pointed out that workers cannot extend quality beyond the limitsof what any process is capable of producing. The quality-control function is now referred toas total quality management (TQM), emphasising the strategic importance of quality to thewhole organisation not just the factory oor. TQM involves an unending process of continu-ous improvement with the objective of achieving perfection. The fact that perfection is neverreached is irrelevant the setting and achieving of ever-higher goals is sufcient justication.Statistical process control (SPC) is the application of statistical techniques to the control ofprocesses. SPC is used to ensure that a process is meeting specied standards by measuring itsperformance. If the process is to produce quality products, its capabilities must be periodicallymeasured to check that it is performing as planned. The quality of the process can be affectedby natural variations that occur in almost every production process. As long as these variationsremain within specied limits, the process is in control and quality will not be affected.However, if the variations go outside the specied limits, the process is out of control andthe causes must be determined. Control charts are used to separate random causes of variationfrom non-random causes such as operator error, faulty setup, poor materials, and so forth.Storage and Distribution (Logistics)The logistics function is a key aspect of production operations. Logistics is concerned mainlywith the handling, storage, and movement of products to markets and materials to manufac-turing facilities. For example, the logistics of a retail company involves purchasing goodsfrom suppliers, receiving and storing such goods, and then distributing them to customers. Ina manufacturing company, the logistics function is more complex because raw materials andspare parts have to be purchased and stored before the production process can commence.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 11When nished goods start rolling off the production line, they also have to be warehoused andeventually distributed to customers. The storage term of storeskeeping usually refers to theactivities associated with the storing of materials and spare parts while warehousing is usedfor the storage of nished goods. In managing logistics, several important issues arise.r What should be the structure of the distribution system centralised or decentralised?r How can warehouse and storage layouts be optimised in order to maximise space whileminimising material handling costs?r Which geographic locations provide the best benets to the organisation?r How much inventory should be held at each location?r Which modes of transportation should be used?Astrategic part of logistics planning is operations layout. Alayout is the physical congurationof processes, equipment, and materials that facilitates the owof materials and personnel withinand between work areas. For example, it makes sense to group similar machines togetherso that jobs are routed to one particular work area rather than being scattered all over theplace. Poor layouts cause bottleneck queues by interrupting the physical ow of materials,and consequently add extra costs to production activities. Layout decisions should thereforebe made with efciency of operations in mind. The various types of layout include process-orientated layout, product-orientated layout, warehouse layout, and ofce layout.Project ManagementProject management is an integral part of operations management. Projects may involve re-curring activities, e.g., plant maintenance, or they may be large one-off projects such as theconstruction of a new manufacturing facility. Large-scale projects consist of numerous tasksthat must be completed, some in parallel and others in sequence, by various individuals orgroups. Because considerable expenditure is involved, projects must be managed carefully toensure that the entire project is completed on time. Project management comprises the im-portant activities of (i) planning (ii) scheduling and (iii) controlling project tasks. The initialphase of project planning involves setting objectives and performance criteria (usually mea-sured in terms of costs and time), identifying resource requirements, and assigning areas ofresponsibility.The practical phase of project control is basically a comparison between what has actuallybeen completed against what was planned at the start. Project control uses milestones peri-odically to review a projects progress. Rather than allow a project to be completed withoutany control checks, management designates certain intermediate points, called milestones, atwhich progress will be evaluated. If a milestone check indicates that a project is running late,then corrective measures must be taken to bring the project back on course.Project scheduling focuses on the activities that make up the project. Aschedule shows wheneach task starts and ends and howlong it will take to complete the task, i.e., the tasks duration.Scheduling also shows how each task is related to others in the project. Because projects oftenhave important deadlines to meet, scheduling is a critical aspect of project management. WhereJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 012 Excel Models for Business and Operations ManagementExcavatesitePourfoundationsPlumbingstage 1BuildwallsFigure 1.6 Partial network of nodes and arcs.there is a large number of interrelated tasks, timing and co-ordination become very complex.Nowadays project managers use computer software to help them identify those tasks that mustbe nished on time in order to avoid delaying the entire project.There are various mathematical techniques for schedulingprojects. The twomainapproachesare the critical path method (CPM) and the project evaluation and review technique (PERT).These two techniques are very similar, the main difference being their assumptions concerningthe accuracy of duration estimates for each task. PERTemphasises the uncertainty in estimatingactivity times while CPM assumes that task times can be accurately predicted. The type ofquestions that can be answered by using PERT/CPM are:r When will the project nish?r What are the critical activities, i.e., tasks which, if delayed, will delay the whole project?r How is the overall project affected if a critical activity is delayed?r What is the interrelationship between activities?The CPM method is the scheduling approach used in virtually all project managementsoftware today, including Microsoft Project. All CPM and PERT models use a network toportray graphically the projects interrelationships. A network consists of nodes and arcs, alsocalled arrows. Figure 1.6 shows the main features of a partial network. A node represents aproject task (i.e., activity) and is depicted as a circle in the network. Arcs are shown as arrowsand dene the interrelationships between nodes, indicating what activity must end beforeanother activity can start.MANAGEMENT DECISION-MAKINGManagement is responsible for setting an organisations objectives. In order to achieve theseobjectives, management must make decisions. Decision-making is an integral part of manage-ment and occurs in every function and at different levels within the organisational structure.While the type of decisions that are made vary considerably, they can be linked to the followingcommon managerial activities:Planning involves (i) establishing organisational goals and objectives and (ii) developingpolicies and procedures for achieving those goals.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 13Organising is the process of (i) determining and co-ordinating activities and (ii) establishingorganisational structures and procedures to ensure that the activities are carried out as planned.Stafng entails the recruitment and training of personnel in order to achieve organisationalgoals and objectives.Controlling involves (i) measuring performance against goals and objectives and (ii) devel-oping procedures for adjusting goals or activities in order to bring them into line.All of the above managerial functions need information to make decisions. Information pro-vides vital input to the decision-making process by increasing knowledge and reducing un-certainty. While they are obviously interdependent, each managerial activity requires differentinformation. For example, a planning decision utilises information concerning future events,e.g., sales forecasts, while control decisions need details of what is happening now or in theimmediate past, e.g., information about machines that have suddenly broken down.In the previous sections, the organisation was viewed from the functional perspective ofmarketing, production, nance/accounting, and human resources activities. Another popularway of looking at an organisations structure is provided by the hierarchical framework ofmanagement. This hierarchical structure focuses on the decision-making process at three dis-tinct levels namely top, middle, and lower management as shown in Figure 1.7 below. Becauseof the different types of decision taken at each level, top management is often referred to asstrategic-level management. Similarly, middle and lower levels are also called tactical-leveland operational-level management respectively.Top ManagementTop management includes the board of directors and the heads of the functional divisionsof marketing, production, nance/accounting, and human resources activities. They exercisecontrol over the long-term strategic direction of policies that determine the companys futuredirection. The information required for strategic decision-making is obtained largely fromsources outside the company and includes such details as market trends, the competition, newproduct developments, etc. This external information contains a high level of uncertainty andin most cases is qualitative in nature. Information about the companys own operations is usedprimarily for forecasting.Types of informationQualitativeQualitative and quantitativeQuantitativeTypes of decisionTopMiddleLowerTacticalStrategicOperationalFigure 1.7 Hierarchical levels of management.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 014 Excel Models for Business and Operations ManagementTable 1.1 Characteristics of decision-making informationManagement Decision Level Characteristics Operational Tactical StrategicDecision-makers Supervisors, foremen DepartmentalmanagersBoard of directors,divisional headsTime horizon Daily Weekly/monthly One year and beyondType of decision Structured Semi-structured UnstructuredDecision level Low Moderate Very highInformation source Mainly internal Internal/external Mainly externalInformation type Quantitative Quantitive andqualitativeQualitativeInformation complexity Simple Moderate ComplexBreadth of control Narrow Intermediate BroadTop-level decisions depend to a large extent on the quality of managerial judgement based onpast experience, intuition, and an element of luck. The term unstructured decision-makingis often used to describe strategic-level situations. Unstructured decisions involve complexprocesses for which no standard procedures exist to help the decision-maker, e.g., launchingnew products. Because strategic decisions occur infrequently, top management receives com-pany information on an irregular basis, usually in the form of summary reports. The chiefcharacteristics of decision-making information are shown in Table 1.1.Middle ManagementMiddle or tactical management is responsible for implementing the decisions taken at the toplevel. It must ensure that organisational goals are achieved by obtaining and using resourcesin an efcient and effective way. Because middle management has the role of controlling andmonitoring the organisations various resources, it is sometimes referred to as managementcontrol. Typical tactical-level decisions include planning working capital, scheduling produc-tion, formulating budgets, and making short-term forecasts. Middle management uses internalinformation to compare actual performance with planned goals. By analysing this information,managers are able to determine whether operations are proceeding as planned or if correctiveaction is needed.Tactical-level decisions utilise both internal and external information. If a competitive ad-vantage is to be gained, then middle management must obtain outside information on industryaverages and other companies productivity levels. By comparing these gures with theirown internal productivity data, middle management is better placed to evaluate the organisa-tions position in the market-place. Because tactical-level decisions are based on a mixtureof internal quantitative details and external qualitative information, they are sometimes calledsemi-structured. A semi-structured problem is one where decision-makers have factual datato analyse but must also use their own judgement to arrive at a satisfactory solution.Sensitivity or what if analysis is a typical semi-structured approach in which the decision-maker asks a series of what if questions in order to determine how key factors respond toJWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 0A Systems View of Business 15assumed changes or conditions. The ability to experiment with quantitative data in order to gaingreater insight into a semi-structured situation lets the decision-maker see the consequencesof certain actions. For example, a manager may wish to see how an organisation might beaffected if there was a 3% increase in labour costs. Some of the models in this book illustratethe benets of using what if analysis.Operational ManagementOperational-level decisions are structured, i.e., theyare basedonfactual data. Structuredprob-lems are routine and repetitive, e.g., a payroll system. They utilise quantitative informationwhich is regularly obtained fromoperational activities and their outcome is totally predictable.Although a structured decision may involve complex calculations, standard techniques alreadyexist for nding a solution. Lower management therefore makes fairly straightforward deci-sions that follow specic rules and patterns requiring very little qualitative input. Activities atthis level include the maintenance of inventory records, the preparation of sales invoices andshipping orders, and decisions on materials requirements.Lower management measures the efciency of factory-level operations and takes remedialaction to improve their efciency wherever possible. Like tactical management, lower-levelmanagement is also involved with control and is often referred to as operational controlmanagement. An example of operational control is the cost control of an organisations product.Cost accounting provides a standard (or expected) cost which may be obtained from theengineering design department or may be based on past records. Actual costs of manufacturingthe product are produced at the end of the accounting period. By comparing expected costswith actual costs, lower management are able to calculate cost variances and hence determineif any corrective action is necessary.In summary, much of an organisations information is utilised by lower-level managers withonly a small percentage being accessed by top management. Exception reports, i.e., reportsthat focus on out-of-control situations such as inventory shortages or machine breakdowns,are important information sources for lower and middle management. Top management mayutilise ad hoc reports to deal with unexpected one-off situations that may arise. They also useorganisational information, supplemented by external market research input, to assess salesforecasts.ENTERPRISE RESOURCE PLANNING (ERP)In todays competitive business environment, many organisations are turning to enterpriseresource planning (ERP) systems to help them manage the increasingly complex processescreated by the globalisation of markets. An ERP system extends the centralised databaseconcept to include customers and suppliers as part of the business value chain. For example, aEuropean manufacturer may use an ERP systemto process an order froma Canadian customerrequiring the purchase of extra components from its Chinese supplier. The ERP system will(i) handle all currency transactions (including exchange rate conversions), (ii) update inventoryandMRPsubsystems, (iii) provide distributors withthe necessarysupplier andcustomer details,and nally (iv) update all accounting processes.JWBK022-01 JWBK022-Barlow March 18, 2005 7:55 Char Count= 016 Excel Models for Business and Operations ManagementThe model of Figure 1.3 shows how information is shared among the various subsystemsof a manufacturing business. In many cases, much of this organisational information is frag-mented, with key departments having their own computer systems. These stand-alone systemscan create inefciencies in the management and control of information by encouraging dataduplication and the proliferation of incompatible software applications. To overcome such apiecemeal approach, database systems have been developed whereby a companys informationis integrated and stored in a single source, called the database (see Chapter 7). The databaseconcept allows users to have immediate access to the entire business information resource.The software company, SAP an acronym for Systems, Applications, and Products is amajor supplier of ERP systems. Numerous large corporations including Coca-Cola, Microsoft,Kodak, IBM, Intel, and Exxon have implemented SAPs R/3 software package. The R/3 prod-uct includes a fully integrated suite of programs for nance and accounting, production andmaterials management, quality management and plant maintenance, sales and distribution, hu-man resources management, and project management. It is also capable of handling differentcountries languages and business regulations.The successful implementation of an ERP system can be expensive, complex, and time-consuming. For example, an organisation that wants to implement the R/3 system must rstproduce a blueprint of its business, involving detailed models of how each process operates.While much of the R/3 system consists of standardised modules, it is sufciently exible toallow customisation to meet specic requirements.REFERENCES AND FURTHER READINGHeizer, J. and Render, B. (2003) Operations Management (7th edn), Prentice Hall, NewJersey.Lucey, T. (2005) Management Information Systems (9th edn), Thomson Learning, London.JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 02Model-building toolsOVERVIEWThe success of an organisation depends to a large extent upon the quality of its managerialdecision-making. Everyone makes a decision when choosing between two or more alternatives.Analternative is a course of actionintendedtosolve a problem. Althoughfewpeople analyse thedecision-making process, all decisions follow a similar series of logical steps as shown in Fig-ure 2.1. The decision-maker must rst recognise that a problemexists. The next step is to estab-lishthe evaluationcriteria, i.e., the necessaryquantitative andqualitative informationwhichwillbe used to analyse various courses of action. The third stage involves identifying various alter-natives for consideration. Having assessed every situation, decision-makers then select the al-ternative that best satises their evaluation criteria. Finally, this optimal choice is implemented.Business managers must choose the appropriate course of action which is most effective inattaining an organisations goals. The types of decisions that have to be taken vary considerablyand depend upon the complexity of the problem to be solved. Usually, the level of complexityincreases proportionally with the level of qualitative information required to solve the problem.Even where a problem is based almost entirely on factual, quantitative information, decision-making can be difcult. For example, the construction of a large building contains so manyinterdependent activities that an overwhelming amount of time could be spent on gatheringfactual information about the situation. Luckily, computer models are now available to assistthe managerial decision-making process!Recognisea problemEstablishevaluationcriteriaIdentifyalternativecourses of actionImplement thischoiceSelect the bestalternativeEvaluatealternativesFigure 2.1 Stages in the decision-making process.17JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 018 Excel Models for Business and Operations ManagementMODELLING CHARACTERISTICSAmodel is a simplied representation of a real-world situation. It can be regarded as a substitutefor the real system, stripping away a large amount of complexity to leave only essential, relevantdetails. A model is used to facilitate understanding of a real object or situation. A decision-maker is better able to evaluate the consequences of alternative actions by analysing the modelsbehaviour. For example, dummies which are used in car-crash simulations, allow engineers totest and analyse the safety of new features. The advantages of a model are (i) it is easy for thedecision-maker to understand (ii) it can be modied quickly and cheaply, and (iii) there is lessrisk when experimenting with a model than with the real system.Typical models showing the layout of a new housing or shopping complex can be found inarchitects ofces. Such models are referred to as physical models because they are three-dimensional representations of real-world objects. They may be scaled-down versions of thereal thing or, as in the case of the crash-testing dummy, they can be exact replicas. Types ofmodels which are more suited to computers include (i) graphical models, which use lines,curves, and other symbols to produce ow charts, pie charts, bar charts, scatter diagrams,etc. and (ii) mathematical models which use formulae and algorithms to represent real-worldsituations. All of the models developed in this book are either graphical or mathematical.Because a model is not an exact representation, it cannot contain all aspects of a problem.Models involve a large number of assumptions about the environment in which the systemoperates, about the operating characteristics of its functions, and the way people behave.These environmental assumptions are called uncontrollable variables because they are outsidethe decision-makers control, e.g., interest rates, consumer trends, currency uctuations, etc.On the other hand, controllable variables are those inputs that inuence a models outcomeand are within the decision-makers control. Examples of control variables are product price,the level of output, or acceptable prot levels.Steps in Model-BuildingThe validity of a models results will depend on how accurately the model represents the realsituation. The ideal model is one which is neither too trivial in its representation of realitynor too complex to implement. There are three main steps in building a spreadsheet model asshown in Figure 2.2. The rst step of problem formulation requires that the decision-makeradopt a systematic and rigorous approach to the selection of variables. One of the main sourcesof errors in model-building is the exclusion of important variables, due either to an oversightor a lack of understanding of the models underlying logic. In either case, the results will bemeaningless or at best, dubious.The second step in model-building is the identication of the relationships among the vari-ables. In many cases well-known formulae may already exist. For example, consider the amountdue to an investor at the end of ve years when 2000 is invested at a compound interest rateof 12% per annum. The general formula for basic compound interest states that if an amountP0 is invested at a xed rate of r%, the principal Pn after n years isPn = P0(1 +r/100)nJWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 0Model-Building Tools 19Formulate the problemIdentify the relevant variablesDistinguish between controllable and uncontrollable variablesBuild the modelEnter data and formulae into the relevant spreadsheet cellsEstablish mathematical formulaeDefine the relationships among the variables, i.e., the models formulaeFigure 2.2 Steps in model-building.The investor will thus get 2000(1.12)5= 3525. In this case, the controllable variables areP0, r, and n. In situations where no known formula exists, the decision-maker must take greatcare in dening the relationships among the variables. A sensible precaution is to check outa newly-derived formula by using historical data and comparing the formulas result with analready-known answer.The nal step is to set up the model as a spreadsheet. The compound-interest example aboveis used as a demonstration model. Aspreadsheet is usually depicted as a matrix or grid of cells,each uniquely numbered by reference to the column and row in which it is found. A cell cancontain either a description, a xed value, or a formula, e.g., cells A1, E3, and E7 in Figure 2.3show each of these three features. The main source of errors in building a spreadsheet modelis due to wrong cell cross-referencing, i.e., the user incorrectly identies source cells whenentering either input or formulae.The advantage of the spreadsheet model is that the user can easily change any of the inputdata. Thus the compoundinterest for anyvalues of P0, r, or n canbe foundbysimplyoverwritingthe current input values in cells E3, E4, and E5. The model also allows users to see instantly theA1 Compound interest example23 Input data Initial principal, P0Principal Pn after n years is 3,524.684 Interest rate, r5 No. of years, n6789B C D E F G H I2,0012%5Cell E7 contains the formulaE3*(1 + E4) ^ E5where the symbols *, ^represent multiplication and' to the power of 'Figure 2.3 A spreadsheet model for compound interest.JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 020 Excel Models for Business and Operations ManagementTable 2.1 Characteristics of the decision-making environment.The decision-making environment Characteristics Certainty Risk UncertaintyControllable variables Known Known KnownUncontrollable variables Known Probabilistic UnknownType of model Deterministic Probabilistic NonprobabilisticType of decision Best Informed UncertainInformation type Quantitative Quantitative and qualitative QualitativeMathematical tools Linear Statistical methods; Decision analysis;programming Simulation Simulationeffects of their deliberations. By experimenting with various combinations of variables, thedecision-maker can obtain a better understanding of the models sensitivity.RISK AND UNCERTAINTY IN DECISION-MAKINGIn business decision-making, there are three classes of decision problems, namelyr decisions under conditions of certaintyr decisions under conditions of riskr decisions under conditions of uncertainty.There are various characteristics associated with each of these three classes of decision-makingas showninTable 2.1. If a decision-maker knows the exact values of all uncontrollable variables,then the model is said to be deterministic, i.e., the outcome is already determined because allvariables both controllable and uncontrollable are known. The best decision is thus madeunder conditions of certainty, i.e., there is no element of doubt associated with the outcome. Apayroll systemis a typical example of a deterministic application because all inputs are alreadyquantied. Linear programming (LP) techniques, including integer and goal programming, arethe most widely used tools for solving deterministic models.Under conditions of certainty, the decision taken is always the best one. In many managerialdecisions, however, there is an element of risk attached to future events because the uncontrol-lable variables are not known with certainty. Decision-makers should therefore be aware thatin a risky environment where they do not have complete control over all inputs, bad outcomeswill sometimes occur. To take an everyday example, there is a level of doubt associated withthe outcome of most sporting events. Such decisions under risk are made every week byracing punters who back horses to win races on the basis of recent performances. While theoutcome is not known with certainty, the decision is an informed one, based on an analysis ofprevious events.While decisions under risk can have more than one outcome, it is assumed that decision-makers have some knowledge of how matters will turn out, i.e., they know the probability ofJWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 0Model-Building Tools 21events occurring. The decision-maker tries to make a good decision which will result in a goodoutcome by taking into account the probabilities associated with the uncontrollable inputs. Inthis situation, a model is said to be probabilistic or stochastic. The decision is an informedone because there is sufcient information available to allow probabilities to be assigned tothe uncontrollable variables.Statistical techniques, involving probabilities and probability distributions, are the maintools used in solving problems which have an element of risk attached to them. Simulationis another model-building tool which is used to analyse problems containing uncontrollablevariables represented by probability distributions. A simulation model is designed to mimicthe behaviour of a real-world situation. A major advantage of simulation models is that theycan handle uncertainties by using random number generation. By running a model many timeswith different sequences of randomnumbers, a reasonable replication of what actually happensin the system is obtained. One common application of simulation is the behaviour patterns ofsupermarket queues.The nal class of decision problem decisions under uncertainty moves into the area ofguesswork where very little, if any, information exists on which to base a decision. A typicalexample would be choosing numbers to win a national lottery. While probabilities can beassigned to uncontrollable variables in decisions under risk, the decision-maker is unable orlacks condence to specify such probabilities in an environment of uncertainty. Even whereall possible outcomes are known, probabilities still cannot be assigned to uncontrollable inputsbecause of the levels of uncertainty. For this reason, models which operate under conditionsof uncertainty are called nonprobabilistic models.Decision analysis also called decision theory is the chief model-building tool used in anenvironment of uncertainty. In decision analysis, uncontrollable variables are called eventsor states of nature. States of nature may result from economic, social, and political factorsbeyond the control of the decision-maker. There are three different criteria which can be appliedto decisions under uncertainty, namely maximin, maximax, and minimax. All of these criteriacan be used without specifying probabilities. They are usually associated with the theory ofgames which, despite the name, has important applications, e.g., militarystrategies. Simulationtechniques, which have been briey discussed above, can also be used where conditions ofuncertainty exist.LINEAR PROGRAMMING (LP)Managers often have to make decisions on how best to allocate scarce resources among com-peting activities. Limited resources such as machinery, time, materials, labour, and moneyare used to manufacture various products, or provide services such as investment plans andadvertising strategies. Typically, there are many different ways to produce these products orservices. Managements problem is to nd the best way, given the limitations of the resources.Linear programming (LP) is a widely used mathematical technique designed to help in theplanning and allocation of key organisational resources. The word linear refers to the fact thatall equations and inequalities associated with an LPproblemmust have linear relationships. Theprogramming part refers to the iterative process that is used to derive an optimum solution.JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 022 Excel Models for Business and Operations ManagementAll LP problems have two main aspects, namelyr to maximise or minimise some quantity such as prot or cost. Having decided upon theobjective to be optimised, it is then necessary to dene it mathematically, i.e., to formulatethe objective function. Typical objectives include:r how to nd the optimal allocation of parts inventory to minimise production costsr how to create the most efcient personnel schedule to minimise costs while meetingbusiness needs and individual scheduling requestsr how to optimise allocation of funds in an investment portfolio to maximise prots.r The use of scarce resources places restrictions on the alternative courses of action availableto a manager. These resource limitations, called constraints, are described in terms of linearinequalities, expressed either as (greater-than-or-equal-to) or (less-than-or-equal-to). Itis the presence of these resource inequalities that creates alternative solutions, with (usually)only one solution being optimal.The best-known linear programming methods are the Simplex and graphical methods. Since thegraphical method can be used only for problems with two or three variables, it is of limited use.The Simplex method, which was developed during the Second World War for use in militarylogistics, is capable of solving very large LP problems involving thousands of variables. Itconsists of a set of step-by-step rules, which are ideally suited for computerisation. Indeed,until the arrival of Karmarkars Algorithm in 1984 (Winston, Chapter 10) most LP softwarepackages were based on the Simplex method. However, Karmarkars new algorithm takessignicantly less computer time than the Simplex method to solve very large-scale problems,and a new generation of LP software, using Karmarkars approach, has now been developed.Applications of Linear ProgrammingLinear programming problems fall into two main categories: (i) blending or product-mix prob-lems and (ii) transportation problems. Blending or product-mix problems focus on achievingan optimumcombination of resources in order to maximise prots or minimise costs. Atypicalfood-blending problem could involve a developing country wishing to produce a high-proteinfood mixture at the lowest possible cost. A popular business mix problem relates to portfoliomanagement whereby a bank wants to allocate funds in order to achieve the highest possiblereturn on its investment.Transportation problems involve a number of supply sources (e.g., warehouses) and a num-ber of destinations (e.g., retail shops). The objective is to minimise the transportation costsinvolved in moving wholesale products from warehouses to shops. Transportation problemsare therefore concerned with selecting minimum-cost routes in a product-distribution networkbetween sources and destinations. The special case of the transportation problem known asthe assignment method, involves assigning employees or machines to different tasks on aone-to-one basis. In this situation, only one source item is assigned to each of the variousdestinations. Both transportation and assignment problems belong to the wider class of LPproblems known as network ow problems.JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 0Model-Building Tools 23In many situations, solutions to LP problems make sense only if they have integer (wholenumber) values. Quantities such as 232/3 employees or 121/2 tables are unrealistic. Simplyrounding off the LP solution to the nearest whole number may not produce a feasible solution.Integer programming (IP) is the termused to describe an LPproblemthat requires the solutionto have integer values. The LP model for the Acme Company developed below is an exampleof integer programming being applied to a product-mix situation.One of the limitations of linear programming is that it allows only one objective functionto be optimised. Thus, the decision-maker must focus on a single objective or goal at a time.However, managers frequently want to optimise several conicting goals at the same time,e.g., how to maximise both production and prots or to maximise prots while minimisingrisk. Goal programming (GP) is a variation of linear programming that provides for multipleobjective optimisation. GP involves making trade-offs among several goals or objectives untila solution is found that satises the decision-maker. GP differs from LP in that it does notproduce an optimum solution. Rather, it provides a method whereby the decision-maker canexplore alternative solutions and then choose that solution which comes closest to meeting thegoals under consideration.While all LPproblems containonlylinear relationships, problems doexist inwhichequationsand/or inequalities are nonlinear. Nonlinear programming (NLP) is a technique which can solveproblems containing nonlinear relationships. A simple denition of a nonlinear relationship isany equation which does not conformto the straight-line relationship, y =mx +c. Formulatingan NLP problem is exactly the same as formulating an LP problem, except that the objectiveand/or constraints may be nonlinear. Nonlinear programming is a collective term for manydifferent categories, which include quadratic programming, geometric programming, convexprogramming, and calculus.Most spreadsheet packages have built-in solvers. A solver is a powerful tool for analysingand solving various types of linear programming problems which contain many variables andconstraints. Excels solver called simply Solver can analyse and solve three types of prob-lems, namely linear programming (LP), integer programming (IP), and nonlinear programming(NLP). Because of its iterative nature, linear programming may not always converge to the bestsolution. It is worth remembering that there may be different optimal solutions to any LP prob-lem and nding them will depend upon the values assigned to options such as the maximumtime allowed, the maximum number of iterations allowed, the size of the residual error, etc.Excels Solver provides default settings for all options, which are appropriate for mostproblems. However, experimentation with the different Solver Option settings, which areshown in Figure 2.6 on page 26, may yield better results. Because of the complexity of nonlinearprogramming (NLP) problems, Solvers nonlinear option will not guarantee that the solutionwhich it found is optimal. This is not a reection on Solvers capabilities but rather on the factthat some NLP problems cannot be solved by any method.EXAMPLE 2.1 LP model for the Acme Companys problemThe Acme Company manufactures two products widgets and gadgets. The company earnsa prot of 2 per box for widgets and 3 a box for gadgets. Each product is assembled andpackaged. It takes 9 minutes to assemble a box of widgets and 15 minutes for a box of gadgets.The packaging department can package a box of widgets in 11 minutes, while a box of gadgetsJWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 024 Excel Models for Business and Operations Managementtakes 5 minutes. Acme wants to nd out what combination of boxes of widgets and gadgetswill maximise total prot, given that there is a maximum of 1800 hours available in both theassembly and packaging departments. The company has decided to see if Excels Solver canprovide a satisfactory solution, so the rst step is to formulate the problem as an LP exercise.Let x1, x2=the optimumnumber of boxes of widgets, gadgets (x1, x2 are positive integers)Acmes objective is to maximise prot, i.e., maximise the objective function, Z (in s), whereZ = 2x1+3x2subject to the following four constraints:r 9x1+ 15x2 108,000 minutes (Time constraint for assembly department)r 11x1+ 5x2 108,000 minutes (Time constraint for packaging department)r x1, x2 0 (x1, x2 must be positive values)r x1, x2= int(eger) (x1, x2 must be integer values)Excels Solver uses the same approach as outlined above except that it uses different terms.Target cell In Solver, the target cell is a single cell containing the objective function which isto be either maximised or minimised.Changing cells These cells contain the unknowns. In the Acme example, there are only twounknowns, x1, x2. Solver uses an iterative approach by changing the values in these cells inorder to progress from one solution to a better solution. This process of changing x1, x2 valuesis repeated until an answer is eventually reached which cannot be improved upon, i.e., theoptimum solution has been found. Note that the changing cells are usually left blank or areassigned zero values.Constraints As described above, constraints are the limits which are imposed upon certaincells, and may include changing cells and/or the target cell.Before using Solver, the rst thing that must be done is to set up the spreadsheet model forthe Acme Company problem, as shown in Figure 2.4. Note that the Solver Parameters detailsand formulae given on lines 14 to 24 are not part of the model and are provided only for theusers benet. For example, it should be clear that the models changing cells are G5, G6 andthe target cell is F11.The model in Figure 2.4 shows the optimal solution calculated by Excels Solver. The answeris 9000 boxes of widgets and 1800 boxes of gadgets, giving a maximumprot of 23,400. Notethat the actual amount of time used in each department is 1800 hours, equalling the maximumtime available. The answers are given in cells G5, G6, F11, C9 and D9 though do not forgetthat these cells will initially display blanks or zeros when the model is rst set up. Havingentered the descriptive data and formatted the cells as shown in lines 1 to 11, complete thefollowing tasks:r Leave changing cells G5, G6 blankr Enter formula C5*$G5 + C6*$G6 into cell C9JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 0Model-Building Tools 251 Example 2.1 - The Acme CompanyA B C D E F G H23456915108,000108,000108,000 User input cells are shaded108,0001152390001800= x1= x27891011 Objective: Maximize profits =Solver ParametersCell Formula Copied toSet Target Cell:Equal to:By Changing Cells:Subject to Constraints:23,40012131415 F11MaxG5:G6C9 100, i.e., N = 7.Class width = largest value smallest valuenumber of classes, N = 61 97 = 7.43Rounding the class width of 7.43 down to 7 and then applying this value to the data range(916), the following frequency distribution table is obtained:Range 916 1724 2532 3340 4148 4956 5764Class frequency 8 7 19 26 28 11 1The quality managers next step is to enter this frequency table into a spreadsheet as shown inFigure 2.7. Excels ChartWizard is used to obtain a histogram. The steps required to producegraphical output are given on lines 3343.Probability Measuring Levels of UncertaintyProbability is the chance that something will happen. Probabilities are expressed as fractionsor decimals. If an event is assigned a probability of 0, this means that the event can neverhappen. If the event is given a probability of 1 then it will always happen. Classical probabilitydenes the probability that an event will occur, given that each of the outcomes are equallylikely, asP (event) = number of ways that the event can occurtotal number of possible outcomesFor example, what is the probability of rolling a 4 on the rst throw of a dice? Since the totalnumber of possible outcomes is 6, and the number of ways that 4 can be achieved with onethrow is 1, the answer is P (event) = 1/6.A probability distribution is similar to a frequency distribution because each uses intervalsto group data items into more meaningful form. Probability distributions can be either discreteor continuous. A discrete probability distribution describes instances where the variable ofinterest can take on only a limited number of values, e.g., the rolling of a dice is limited toone of six numbers. In a continuous probability distribution, the variable can take any valuein a given range, e.g., measuring a childs growth over a specied time period. The mean of adiscrete probability distribution is referred to as its expected value.There are many different probability distributions, both discrete and continuous. The fourmore commonly used are (i) the Binomial distribution, which is a discrete distribution used todescribe many business activities, (ii) the Poisson distribution which is a discrete distributionoften used to count the number of occurrences of some event in a given period of time, (iii) theexponential distribution which is a continuous distribution used to measure the length of timeneeded to perform some activity, and (iv) the important continuous distribution known as thenormal distribution.JWBK022-02 JWBK022-Barlow March 22, 2005 3:30 Char Count= 030 Excel Models for Business and Operations Management12345678910111213141516171819202122232425262728293031323334353637383940414243A B C D E F G H IExample 2.2 - The Wheelie CompanyNo. of Class Relative Classes Interval Frequency Frequency1 916 871926281110.082 1724 0.07 Relative Frequency is the ratio of3 2532 0.19 the class frequency to the total 4 33404148495657640.26 frequency, e.g. relative frequency 5 0.28 for class 1 is 8/100 = 0.08,6 0.11 for class 2 is 7/100 = 0.07etc.7 0.01Totals 100 1Using ChartWizard to obtain graphical output see Appendix for details [1] Highlight the (shaded) range C5:D11 [2] Click the ChartWizard button (with coloured columns) on the standard tool bar [3] From ChartWizard's Step 1 dialog box, choose Chart type 'column' and the Chart sub-type (shown in row 1, column 1). Click twice on the 'next' button to go to Step 3. [4] Step 3 presents Chart Options. Enter titles for X and Y axes and then click on the'Legend' tab. Clear the 'Show Legend' box. Then click on 'next' and 'finish' buttons. [5] Click anywhere on a column with the right-hand button and choose 'Format Data Ser