Modeling Dynamic Systems

Series Editors

Matthias RuthBruce Hannon

SpringerNew YorkBerlinHeidelbergHong KongLondonMilanParisTokyo

Bernard McGarvey Bruce Hannon

Dynamic Modeling for BusinessManagement

An Introduction

With 166 Illustrations and a CD-ROM

Bernard McGarvey Bruce HannonProcess Engineering Center Department of GeographyDrop Code 3127 220 Davenport Hall, MC 150Eli Lilly and Company University of IllinoisLilly Corporate Center Urbana, IL 61801Indianapolis, IN 46285 USAUSA

Series Editors:Matthias Ruth Bruce HannonEnvironmental Program Department of GeographySchool of Public Affairs 220 Davenport Hall, MC 1503139 Van Munching Hall University of IllinoisUniversity of Maryland Urbana, IL 61801College Park, MD 20742–1821 USAUSA

Cover illustration: Top panel––The model with the controls on ORDERING and SELLING. Bot-tom panel––Photo by William F. Curtis.

Library of Congress Cataloging-in-Publication DataHannon, Bruce M.

Dynamic modeling for business management: an introduction / Bruce Hannon, Bernard McGarvey.

p. cm.ISBN 0-387-40461-9 (cloth: alk. paper)1. Management—Mathematical models. 2. Digital computer simulation.

I. McGarvey, Bernard. II. Title.HD30.25.H348 2003519.7�03—dc21 2003054794

ISBN 0-387-40461-9 Printed on acid-free paper.

© 2004 Springer-Verlag New York, Inc. All rights reserved. This work consists of a printed book and a CD-ROM packaged with thebook. The book and the CD-ROM may not be translated or copied in whole or in part withoutthe written permission of the publisher (Springer-Verlag New York, Inc., 175 Fifth Avenue,New York, NY 10010, USA), except for brief excerpts in connection with reviews or scholarlyanalysis. Use in connection with any form of information storage and retrieval, electronicadaptation, computer software, or by similar or dissimilar methodology now known or here-after developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, evenif they are not identified as such, is not to be taken as an expression of opinion as to whetheror not they are subject to proprietary rights.

Printed in the United States of America.

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Disclaimer: This eBook does not include the ancillary media that waspackaged with the original printed version of the book.

The world consists of many complex systems, ranging from our own bodies toecosystems to economic systems. Despite their diversity, complex systems havemany structural and functional features in common that can be effectively simu-lated using powerful, user-friendly software. As a result, virtually anyone can ex-plore the nature of complex systems and their dynamical behavior under a rangeof assumptions and conditions. This ability to model dynamic systems is alreadyhaving a powerful influence on teaching and studying complexity.

The books in this series will promote this revolution in “systems thinking” byintegrating skills of numeracy and techniques of dynamic modeling into a varietyof disciplines. The unifying theme across the series will be the power and sim-plicity of the model-building process, and all books are designed to engage thereader in developing their own models for exploration of the dynamics of systemsthat are of interest to them.

Modeling Dynamic Systems does not endorse any particular modeling para-digm or software. Rather, the volumes in the series will emphasize simplicity oflearning, expressive power, and the speed of execution as priorities that will facil-itate deeper system understanding.

Matthias Ruth and Bruce Hannon

v

Series Preface

The problems of understanding complex system behavior and the challenge ofdeveloping easy-to-use models are apparent in the field of business management.We are faced with the problem of optimizing economic goals while at the sametime managing complicated physical and social systems. In resolving such prob-lems, many parameters must be assessed. This requires tools that enhance the col-lection and organization of data, interdisciplinary model development, trans-parency of models, and visualization of the results. Neither purely mathematicalnor purely experimental approaches will suffice to help us better understand theworld we live in and shape so intensively.

Until recently, we needed significant preparation in mathematics and computerprogramming to develop, run, and interpret such models. Because of this hurdle,many have failed to give serious consideration to preparing and manipulatingcomputer models of dynamic events in the world around them. Such obstaclesproduced models whose internal workings generally were known to only one per-son. Other people were unsure that the experience and insights of the many ex-perts who could contribute to the modeling project were captured accurately. Theoverall trust in such models was limited and, consequently, so was the utility. Theconcept of team modeling was not practical when only a few held the high degreeof technical skill needed for model construction. And yet everyone agreed thatmodeling a complex management process should include all those with relevantexpertise.

This book, and the methods on which it is built, will empower us to model andanalyze the dynamic characteristics of human–production environment interac-tions. Because the modeling is based on the construction of icon-based diagramsusing only four elementary icons, the modeling process can quickly involve allmembers of an expert group. No special mathematical or programming experi-ence is needed for the participants. All members of the modeling team can con-tribute, and each of them can tell immediately if the model is capturing his or herspecial expertise. In this way, the knowledge of all those involved in the questioncan be captured faithfully and in an agreeable manner. The model produced bysuch a team is useful, and those who made it will recommend it throughout theorganization.

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Preface

Such a model includes all the appropriate feedback loops, delays, and uncer-tainties. It provides the organization with a variety of benefits. The modeling ef-fort highlights the gaps in knowledge about the process; it allows the modeling ofa variety of scenarios; it reveals normal variation in a system; and, of course, itgives quantitative results. One of the more subtle values of team modeling is theemergence of a way of analogously conceiving the process. The model structureprovides a common metaphor or analogous frame for the operation of the process.Such a shared mental analogue greatly facilitates effective communication in theorganization.

Our book is aimed at several audiences. The first is the business-school student.Clearly, those being directly prepared for life in the business world need to ac-quire an understanding of how to model as well as the strengths and limitations ofmodels. Students in industrial engineering often perform modeling exercises, butthey often miss the tools and techniques that allow them to do group dynamicmodeling. We also believe that students involved in labor and industrial relationsshould be exposed to this form of business modeling. The importance of the dy-namics of management and labor involvement in any business process is difficultto overstate. Yet these students typically are not exposed to such modeling. Inshort, we want this book to become an important tool in the training of future pro-cess and business managers.

Our second general audience is the young M.B.A., industrial engineer, andhuman-resources manager in their first few years in the workplace. We believethat the skills acquired through dynamic modeling will make them more valuedemployees, giving them a unique edge on their more conventionally trained col-leagues. This book is an introductory text because we want to teach people thebasics before they try to apply the techniques to real-world situations. Manytimes, the first model a person will build is a complex model of an organization.Problems can result if the user is not grounded in the fundamental principles. It islike being asked to do calculus without first doing basic algebra.

Computer modeling has been with us for nearly 40 years. Why then are we soenthusiastic about its use now? The answer comes from innovations in softwareand powerful, affordable hardware available to every individual. Almost anyonecan now begin to simulate real-world phenomena on his or her own, in terms thatare easily explainable to others. Computer models are no longer confined to thecomputer laboratory. They have moved into every classroom, and we believe theycan and should move into the personal repertoire of every educated citizen.

The ecologist Garrett Hardin and the physicist Heinz Pagels have noted that anunderstanding of system function, as a specific skill, must and can become an in-tegral part of general education. It requires recognition that the human mind isnot capable of handling very complex dynamic models by itself. Just as we needhelp in seeing bacteria and distant stars, we need help modeling dynamic sys-tems. For instance, we solve the crucial dynamic modeling problem of duckingstones thrown at us or safely crossing busy streets. We learned to solve theseproblems by being shown the logical outcome of mistakes or through survivableaccidents of judgment. We experiment with the real world as children and get hit

viii Preface

by hurled stones; or we let adults play out their mental model of the conse-quences for us, and we believe them. These actions are the result of experimen-tal and predictive models, and they begin to occur at an early age. These modelsallow us to develop intuition about system behavior. So long as the system re-mains reasonably stable, this intuition can serve us well. In our complex social,economic, and ecological world, however, systems rarely remain stable for long.Consequently, we cannot rely on the completely mental model for individual orespecially for group action, and often, we cannot afford to experiment with thesystem in which we live. We must learn to simulate, to experiment, and to pre-dict with complex models.

Many fine books are available on this subject, but they differ from ours in im-portant ways. The early book edited by Edward Roberts, Managing Applicationsof System Dynamics (Productivity Press, 1978), is comprehensive and yet basedon Dynamo, a language that requires substantial effort to learn. Factory Physics,by Wallace Hopp and Mark Spearman (Irwin/McGraw-Hill, 1996), focuses on thebehavior of manufacturing systems. They review the past production paradigmsand show how dynamic modeling processes can improve the flow of manufactur-ing lines. Business Dynamics, by John Sterman (Irwin/McGraw-Hill, 2000), is aclear and thorough exposition of the modeling process and the inherent behaviorof various if somewhat generic modeling forms.

In a real sense, our book is a blend of all three of these books. We focus on theuse of ithink®, with its facility for group modeling, and show how it can be usedfor very practical problems. We show how these common forms of models applyto a variety of dynamic situations in industry and commerce. The approach weuse is to start from the simplest situation and then build up complexity by ex-panding the scope of the process. After first giving the reader some insight intohow to develop ithink models, we begin by presenting our view of why dynamicmodeling is important and where it fits. Then we stress the need for system per-formance measures that must be part of any useful modeling activity. Next welook at single- and multistep workflow processes, followed by models of riskmanagement, of the producer/customer interface, and then supply chains. Nextwe examine the tradeoffs between quality, production speed, and cost. We closewith chapters on the management of strategy and what we call business learningsystems. By covering a wide variety of topics, we hope to impress on the readerjust how easy it is to apply modeling techniques in one situation to another thatinitially might look different. We want to stress commonality, not difference!

In this book, we have selected the modeling software ithink with its icono-graphic programming style. Programs such as ithink are changing the way inwhich we think. They enable each of us to focus and clarify the mental model wehave of a particular phenomenon, to augment it, to elaborate it, and then to dosomething we cannot otherwise do: find the inevitable dynamic consequenceshidden in our assumptions and the structure of the model. ithink and the Mac-intosh, as well as the new, easy-to-use, Windows®-based personal computers, arenot the ultimate tools in this process of mind extension. However, the relativeease of use of these tools makes the path to freer and more powerful intellectual

Preface ix

inquiry accessible to every student. Whether you are a whiz at math or somewhatof a novice is irrelevant. This is a book on systems thinking and on learning howto translate that thinking into specific, testable models.

Finally, we wish to thank Tina Prow for a thorough edit of this book.

Bernard McGarvey, Indianapolis, Indiana, andBruce Hannon, Urbana, Illinois

Summer 2003

x Preface

Series Preface vPreface vii

Chapter 1. Introduction to Dynamic Modeling 1

1.1 Introduction 11.2 Static, comparative static, and dynamic models 31.3 Model components 51.4 Modeling in ithink 71.5 The detailed modeling process 18

Chapter 2. Modeling of Dynamic Business Systems 21

2.1 Introduction 212.2 Making the organization more manageable:

Systems and processes 232.3 Creating and using a model 262.4 Structural complexity: A market share model 312.5 Complexity due to random variation: An order control process 382.6 Further benefits of dynamic modeling 422.7 Organizing principle of this book 45

Chapter 3. Measuring Process Performance 48

3.1 Introduction 483.2 Financial measures of performance 493.3 The basic profit model 493.4 The role of time, borrowing, and lending 513.5 Choosing among alternatives 553.6 Optimizing at the level of the firm 593.7 Issues with financial measures 623.8 Beyond process output measures 643.9 The process model approach 66

xi

Contents

Chapter 4. Single-Step Processes 76

4.1 Introduction 764.2 The basic process model and Little’s Law 774.3 Queuing systems 864.4 Transient queuing behavior 1014.5 Further modeling with queuing systems 104

Chapter 5. Multistep Serial Workflow Processes 106

5.1 Introduction 1065.2 Modeling multistep processes in ithink 1085.3 Specifying models/modeling objectives 1095.4 An uncoupled process: An order handling process 1105.5 A tightly coupled process: A fast food restaurant process 1215.6 Other configurations 1295.7 Material control systems 131

Chapter 6. Multistep Parallel Workflow Processes 140

6.1 Introduction 1406.2 Parallel queuing models: Designing a checkout system 1416.3 Resource implications: The fast food restaurant revisited 1446.4 Telephone call center model: Balking 1496.5 Machine repair model 1546.6 Batching: A laboratory analysis model 162

Chapter 7. The Supplier Interface: Managing Risk 170

7.1 Introduction 1707.2 First-moment managers 1717.3 Second-moment managers 1717.4 Third-moment managers 1757.5 Fourth-moment managers 176

Chapter 8. Customer Interface 179

8.1 Introduction 1798.2 Controlling the inventory level: Make-to-Stock model 1808.3 The Make-to-Order process: Customer interface 187

Chapter 9. The Tradeoffs Among Quality, Speed, and Cost 192

9.1 Introduction 1929.2 Model development 1939.3 The tradeoffs 1959.4 Coping with uncertainty 198

xii Contents

Chapter 10. Modeling Supply Chains 200

10.1 Introduction 20010.2 Introduction to the Beer Game 20310.3 The Beer Game model 20510.4 Further analysis of the Beer Game model 21810.5 Modifications to the basic model 22210.6 Using the Beer Game model in game mode 223

Chapter 11. The Dynamics of Management Strategy: An Ecological Metaphor 226

11.1 Introduction 22611.2 Hierarchy in nature 22711.3 A model demonstrating the hierarchical nature of an

expanding business 22811.4 Equations for the complete model 236

Chapter 12. Modeling Improvement Processes 240

12.1 Introduction 24012.2 Learning curves 24112.3 Modeling an improvement process 24712.4 Model results 25112.5 Other types of learning curves 257

Appendix A. Modeling Random Variation in Business Systems 259

A.1 Introduction 259A.2 The uniform distribution 262A.3 The triangular distribution 263A.4 The normal distribution: Common cause process variation 265A.5 The exponential distribution: Equipment failure times 267A.6 The Poisson distribution: Modeling defects in products 269A.7 The pass/fail and binomial distribution: Product failures 271A.8 Bimodal distributions: Parallel process flows 273A.9 Custom distributions 274

A.10 The relative comparison of random variation 276

Appendix B. Economic Value Added 279

Appendix C. Derivation of Equations 6.2, 6.3, and 6.4 282

Contents xiii

Appendix D. Optimization Techniques for the Customer Interface Model 284

D.1 Optimizing the model 284D.2 Optimizing the physical model 284D.3 Disappointment with the physical criterion for optimization 286D.4 Finding the optimal financial controls 287

Appendix E. System Requirements for the CD-ROM 293

Bibliography 000

Index 000

xiv Contents