Strategic Modelling and Business Dynamics E-Book

John D. W. Morecroft

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2015

Seitenzahl: 824

Beschreibung

Insightful modelling of dynamic systems for better business strategy The business environment is constantly changing and organisations need the ability to rehearse alternative futures. By mimicking the interlocking operations of firms and industries, modelling serves as a 'dry run' for testing ideas, anticipating consequences, avoiding strategic pitfalls and improving future performance. Strategic Modelling and Business Dynamics is an essential guide to credible models; helping you to understand modelling as a creative process for distilling and communicating those factors that drive business success and sustainability. Written by an internationally regarded authority, the book covers all stages of model building, from conceptual to analytical. The book demonstrates a range of in-depth practical examples that vividly illustrate important or puzzling dynamics in firm operations, strategy, public policy, and everyday life. This updated new edition also offers a rich Learners' website with models, articles and videos, as well as a separate Instructors' website resource, with lecture slides and other course materials (see Related Websites/Extra section below). Together the book and websites deliver a powerful package of blended learning materials that: * Introduce the system dynamics approach of modelling strategic problems in business and society * Include industry examples and public sector applications with interactive simulators and contemporary visual modelling software * Provide the latest state-of-the-art thinking, concepts and techniques for systems modelling The comprehensive Learners' website features models, microworlds, journal articles and videos. Easy-to-use simulators enable readers to experience dynamic complexity in business and society. Like would-be CEOs, readers can re-design operations and then re-simulate in the quest for well-coordinated strategy and better performance. The simulators include a baffling hotel shower, a start-up low-cost airline, an international radio broadcaster, a diversifying tyre maker, commercial fisheries and the global oil industry. "Much more than an introduction, John Morecroft's Strategic Modelling and Business Dynamics uses interactive 'mini-simulators and microworlds' to create an engaging and effective learning environment in which readers, whatever their background, can develop their intuition about complex dynamic systems." John Sterman, Jay W. Forrester Professor of Management, MIT Sloan School of Management "Illustrated by examples from everyday life, business and policy, John Morecroft expertly demonstrates how systems thinking aided by system dynamics can improve our understanding of the world around us." Stewart Robinson, Associate Dean Research, President of the Operational Research Society, Professor of Management Science, School of Business and Economics, Loughborough University

Leseprobe

‘Much more than an introduction, John Morecroft's Strategic Modelling and Business Dynamics uses interactive “mini-simulators and microworlds” to create an engaging and effective learning environment in which readers, whatever their background, can develop their intuition about complex dynamic systems. The examples, from manufacturing operations to competitive strategy, corporate growth and environmental sustainability, provide a rich test-bed for the development of the systems thinking and modelling skills needed to design effective policies for the strategic challenges faced by senior managers throughout the economy today.'

John Sterman, Jay W. Forrester Professor of Management, MIT Sloan School of Management

‘We are living in a world revolutionised by six decades of exponential growth in computing speed, where powerful computers have become consumer electronics and simulation has become virtual reality. Business modelling has come of age. This book, with its vivid examples and simulators, is helping to bring modelling, system dynamics and simulation into the mainstream of management education where they now belong.'

John A. Quelch, Professor of Marketing, Harvard Business School, Former Dean of London Business School

‘John Morecroft's book is an ideal text for students interested in system modelling and its application to a range of real-world problems. The book covers all that is necessary to develop expertise in system dynamics modelling and through the range of applications makes a persuasive case for the power and scope of SD modelling. As such, it will appeal to practitioners as well as students. At Warwick we have a range of undergraduate, masters and MBA courses in simulation, business modelling and strategic development/planning and the text would provide valuable support to those courses.'

Robert Dyson, Emeritus Professor, Operational Research and Management Sciences Group, Warwick Business School

‘Illustrated by examples from everyday life, business and policy, John Morecroft expertly demonstrates how systems thinking aided by system dynamics can improve our understanding of the world around us. Indeed, such thinking provides the basis for improving our world, from making everyday decisions to leading key strategic and policy initiatives. Anyone who is interested in understanding the world and making better choices should read this book.'

Stewart Robinson, Associate Dean Research, President of the Operational Research Society, Professor of Management Science, School of Business and Economics, Loughborough University

‘This text fills the gap between texts focusing on the purely descriptive systems approach and the more technical system dynamics ones. I consider it particularly well suited to the more mature student (MBA, EMBA, Executive courses) due to the focus on conceptualisation and the understanding of complex dynamics.'

Ann van Ackere, Professor of Decision Sciences, HEC Lausanne, Université de Lausanne

‘In today's complex business world company leaders are repeatedly challenged to rethink dominant strategic logics, and if necessary to adapt their firms to cope with turbulence and change. Strategic modelling based on system dynamics is a powerful tool that helps to better understand the feedback structures underlying the dynamics of change. The author demonstrates the appeal and power of business modelling to make sense of strategic initiatives and to anticipate their impacts through simulation. The book offers various simulators that allow readers to conduct their own policy experiments.'

Dr Erich Zahn, Professor for Strategic Management, University of Stuttgart

Strategic Modelling and Business Dynamics

A Feedback Systems Approach

Second Edition

John D.W. Morecroft

This edition first published 2015 © 2015 John Wiley & Sons Ltd First edition published 2007

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Library of Congress Cataloging-in-Publication Data is available.

Morecroft, John D.W. (John Douglas William)    Strategic modelling and business dynamics : a feedback systems approach / John D.W. Morecroft. – Second edition.       pages cm    Includes bibliographical references and index.    ISBN 978-1-118-84468-7 (paper)   1. Decision making–Simulation methods. 2. Business–Simulation methods.   3. Social systems–Simulation methods. 4. System analysis.   5. Computer simulation.   I. Title.

   HD30.23.M663 2015    001.4′34–dc23

201501

A catalogue record for this book is available from the British Library.

ISBN 978-1-118-84468-7 (pbk) ISBN 978-1-118-99481-8 (ebk)   ISBN 978-1-118-84470-0 (ebk)

Cover image: © Getty Images/Stockbyte Cover design: Wiley

To Jay Forrester, academic pioneer

Contents

About the Author

Foreword by Peter Checkland

Preface to the Second Edition

Notes

Preface from the First Edition

Manufacturing Dynamics and Information Networks

Bounded Rationality and Behavioural Decision Making

Modelling for Learning

The Dynamics of Strategy

Soft Systems and Complementary Modelling Methods

Notes

How to Use This Book

MBA and Modular/Executive MBA

Non-Degree Executive Education

Undergraduate and Specialist

Notes

Chapter 1 The Appeal and Power of Strategic Modelling

Introduction

A New Approach to Modelling

The Puzzling Dynamics of International Fisheries

Model of a Natural Fishery

Operating a Simple Harvested Fishery

Preview of the Book and Topics Covered

Appendix – Archive Materials from

World Dynamics

Notes

References

Chapter 2 Introduction to Feedback Systems Thinking

Ways of Interpreting Situations in Business and Society

A Start on Causal Loop Diagrams

Structure and Behaviour Through Time – Feedback Loops and the Dynamics of a Slow-to-Respond Shower

From Events to Dynamics and Feedback – Drug-related Crime

Purpose of Causal Loop Diagrams – A Summary

Feedback Structure and Dynamics of a Technology-based Growth Business

Causal Loop Diagrams – Basic Tips

Causal Loop Diagram of Psychological Pressures and Unintended Haste in a Troubled Internet Start-Up

Notes

References

Chapter 3 Modelling Dynamic Systems

Asset Stock Accumulation

The Coordinating Network

Modelling Symbols in Use: A Closer Look at Drug-related Crime

Equation Formulations

Experiments with the Model of Drug-related Crime

Benefits of Model Building and Simulation

Notes

References

Chapter 4 World of Showers

Getting Started

Redesigning Your World of Showers

Inside World of Showers

Simulations of World of Showers B

Notes

References

Chapter 5 Cyclical Dynamics and the Process of Model Building

An Overview of the Modelling Process

Employment and Production Instability – Puzzling Performance Over Time

Equation Formulations and Computations in Production Control

Modelling Workforce Management and Factory Production Dynamics

Equation Formulations in Workforce Management

Chronic Cyclicality in Employment and Production and How to Cure It

Modelling for Learning and Soft Systems

Appendix 1: Model Communication and Policy Structure Diagrams

Appendix 2: The Dynamics of Information Smoothing

Notes

References

Chapter 6 The Dynamics of Growth from Diffusion

Stocks and Flows in New Product Adoption – A Conceptual Diffusion Model

The Bass Model – An Elegant Special Case of a Diffusion Model

A Variation on the Diffusion Model: The Rise of Low-cost Air Travel in Europe

Strategy and Simulation of Growth Scenarios

Conclusion

Appendix: More About the Fliers Model

Notes

References

Chapter 7 Managing Business Growth

A Conceptual Model of Market Growth and Capital Investment

Formulation Guidelines for Portraying Feedback Structure

An Information Feedback View of Management and Policy

Policy Structure and Formulations for Sales Growth

Policy Structure and Formulations for Limits to Sales Growth

Policy Structure and Formulations for Capital Investment

Simulation Experiments

Redesign of the Investment Policy

Policy Design, Growth and Dynamic Complexity

Conclusion

Appendix – Gain of a Reinforcing Loop

Notes

References

Chapter 8 Industry Dynamics – Oil Price and the Global Oil Producers

Problem Articulation – Puzzling Dynamics of Oil Price

Model Development Process

A Closer Look at the Stakeholders and Their Investment Decision Making

Connecting the Pieces – A Feedback Systems View

A Simple Thought Experiment: Green Mindset and Global Recession

Using the Model to Generate Scenarios

Devising New Scenarios

Endnote: A Brief History of the Oil Producers' Project

Note

References

Chapter 9 Public Sector Applications of Strategic Modelling

Urban Dynamics – Growth and Stagnation in Cities

Medical Workforce Dynamics and Patient Care

Fishery Dynamics and Regulatory Policy

Conclusion

Appendix – Alternative Simulation Approaches

Notes

References

Chapter 10 Model Validity, Mental Models and Learning

Mental Models, Transitional Objects and Formal Models

Models of Business and Social Systems

Tests for Building Confidence in Models

Model Confidence Building Tests in Action: A Case Study in Fast-moving Consumer Goods

Model Structure Tests and the Soap Industry Model

Equation Formulation Tests and the Soap Industry Model

Tests of Learning from Simulation

Summary of Confidence Building Tests

Conclusion – Model Fidelity and Usefulness

Endnote: The Loops of Feedback

Notes

References

About the Website Resources

Index

EULA

List of Illustrations

Chapter 1

Figure 1.1

Stock accumulations for global growth

Figure 1.2

Limits to global growth – rough sketches of alternative futures

Figure 1.3

Pacific sardine catch (top) and North Sea herring catch (bottom) from Fish Banks debriefing materials (Meadows

et al.

, 2001)

Figure 1.4

An imaginary fishery – the game board of the original FishBanks, Ltd

Figure 1.5

Diagram of a natural fishery

Figure 1.6

Net regeneration as a non-linear function of fish density

Figure 1.7

Simulation of a natural fishery with an initial population of 200 fish and maximum fishery size of 4000

Figure 1.8

Diagram of a simple harvested fishery

Figure 1.9

Interface for fisheries gaming simulator

Figure 1.10

Simulation of harvested fishery showing all trajectories

Figure 1.11

Relationship between catch per ship and fish density

Figure 1.12

Simulation of harvested fishery with cunning fish – a thought experiment

Figure 1.13

Diagram of the original

World Dynamics

model

Figure 1.14

Simulations of the original world dynamics model

Chapter 2

Figure 2.1

Event-oriented world view

Figure 2.2

Examples of event-oriented thinking

Figure 2.3

A causal loop diagram about road congestion

Figure 2.4

A feedback perspective

Figure 2.5

The trouble with ‘hidden’ feedback

Figure 2.6

Simple causal loop diagram of food intake

Figure 2.7

Puzzling dynamics of a slow-to-respond shower

Figure 2.8

Causal loop diagram of a slow-to-respond shower

Figure 2.9

Simulated dynamics of a slow-to-respond shower

Figure 2.10

Unintended dynamics of drug-related crime – a rough sketch

Figure 2.11

Causal loop diagram for drug-related crime

Figure 2.12

Feedback loops in the growth of the automated materials handling business

Figure 2.13

Arrows and link polarity

Figure 2.14

Feedback loops in the speed trap

Chapter 3

Figure 3.1

Asset stock accumulation in a stock and flow network

Figure 3.2

A simple stock and flow network for university faculty

Figure 3.3

Faculty size at Greenfield University – a 12-month simulation

Figure 3.4

Faculty size at Greenfield University – a 36-month simulation

Figure 3.5

Asset stocks at BBC World Service

Figure 3.6

Symbols in the coordinating network that connects stocks and flows

Figure 3.7

Drug-related crime – sectors and causal loop

Figure 3.8

Community reaction to crime

Figure 3.9

Inside the Police Department

Figure 3.10

The street market for drugs

Figure 3.11

World of the drug users

Figure 3.12

Equation formulation for drug-related crime

Figure 3.13

Formulation of funds required

Figure 3.14

Formulation of street price and price change

Figure 3.15

Formulation for the allocation of police

Figure 3.16

Overview of the drug-related crime model showing sectors, stocks and links

Figure 3.17

Dynamics as seen in each sector

Figure 3.18

Equations for the Community and Police Department

Figure 3.19

Equations for the street market and world of the drug users

Figure 3.20

Anomalous dynamics in a 60-month simulation

Chapter 4

Figure 4.1

Opening screen of World of Showers A

Figure 4.2

The tap setting control and temperature graph

Figure 4.3

The tap setting graph

Figure 4.4

Your final score

Figure 4.5

Policy levers for responsiveness and patience

Figure 4.6

Balancing loop with delay in World of Showers A

Figure 4.7

Interacting balancing loops in World of Showers B

Figure 4.8

Managing product lines that share capacity

Figure 4.9

Operating structure of shower 2 in World of Showers B

Figure 4.10

Equations for shower 2 in World of Showers B

Figure 4.11

Typical simulation of two interacting showers in World of Showers B

Figure 4.12

Simulation of two interacting showers in a redesigned World of Showers B with pipeline delay reduced from 4 to 2 seconds and time to adjust tap increased from 5 to 10 seconds

Chapter 5

Figure 5.1

Modelling is an iterative learning process

Figure 5.2

Dynamic hypothesis and fundamental modes of dynamic behaviour

Figure 5.3

Team model building – phase 1

Figure 5.4

Team model building – phases 2 and 3

Figure 5.5

Employment and production cyclicality – puzzling performance and structural clue

Figure 5.6

Sector map for dynamics of factory production and employment

Figure 5.7

Asset stocks and list of operating policies in production control and workforce management

Figure 5.8

Stock and flow diagram for production control

Figure 5.9

Simulation of a 10 per cent unexpected demand increase in an ideal factory

Figure 5.10

Equations for inventory accumulation

Figure 5.11

Forecasting shipments through information smoothing

Figure 5.12

Equations for inventory control

Figure 5.13

Desired production

Figure 5.14

The computation process and time slicing

Figure 5.15

The mechanics of simulation and stock accumulation

Figure 5.16

Computations in the information network

Figure 5.17

Stock and flow diagram for workforce management

Figure 5.18

Operating constraint linking workforce to production

Figure 5.19

Simulation of an unexpected 10 per cent increase in demand.

Figure 5.20

The view in workforce management

Figure 5.21

Equations for workforce and departure rate

Figure 5.22

Equations for hiring

Figure 5.23

Equations for workforce planning

Figure 5.24

Simulation of a ten percent increase in demand and five percent random variation.

Figure 5.25

Cyclicality in US manufacturing industry

Figure 5.26

Cycles in service industries

Figure 5.27

Simulation of a 10 per cent increase in demand when the time to correct inventory is doubled from 8 to 16 weeks.

Figure 5.28

Simulation of a 10 per cent increase in demand when the workforce planning delay and time to correct workforce are both halved.

Figure 5.29

Modelling for learning

Figure 5.30

Two approaches to business and social modelling

Figure 5.31

I Spy dynamics in the complexity of everyday life (factory operations in this case) and can discover underlying feedback structure

Figure 5.32

The learning system in soft systems methodology involves a comparison of alternative models of purposeful activity

Figure 5.33

Alternative models of a radio broadcaster

Figure 5.34

Policy structure of the factory model

Figure 5.35

Information smoothing

Figure 5.36

First-order (single stock) smoothing. Top: Smoothing of a step input that increases by 10 per cent. Bottom: Smoothing of a random input with standard deviation of five per cent

Chapter 6

Figure 6.1

Feedback loops for S-shaped growth

Figure 6.2

Stock and flow network and possible feedback loops in the adoption of a new product

Figure 6.3

Stocks, flows and feedback loops in a contagion model of new product adoption

Figure 6.4

Equations for adoption through word-of-mouth – a social contagion formulation

Figure 6.5

Stock accumulation equations for adopters and potential adopters

Figure 6.6

Dynamics of product adoption by word-of-mouth

Figure 6.7

The complete bass diffusion model with advertising

Figure 6.8

Bass equations for adoption with advertising

Figure 6.9

Dynamics of product adoption by word-of-mouth and advertising

Figure 6.10

Muted word-of-mouth

Figure 6.11

Creating awareness of low-cost flights among potential passengers: word-of-mouth and marketing

Figure 6.12

Rivals and relative fare

Figure 6.13

Feedback loops for the launch of a low-cost airline, a variation on the diffusion model

Figure 6.14

Simulations comparing bold marketing (top chart) with cautious marketing (bottom chart) assuming slow retaliation

Figure 6.15

Simulations comparing bold marketing (top chart) with cautious marketing (bottom chart) assuming fast retaliation

Figure 6.16

The opening screen of the fliers simulator

Figure 6.17

Equations for available passenger miles

Figure 6.18

Equations for maximum passenger miles

Chapter 7

Figure 7.1

Growth and underinvestment feedback structure

Figure 7.2

Future time paths for revenue

Figure 7.3

Sector map for dynamic hunch

Figure 7.4

Full causal loop diagram of Forrester's market growth model

Figure 7.5

Formulation guidelines

Figure 7.6

Customer ordering policy

Figure 7.7

Sales force hiring policy

Figure 7.8

The myopic and political world of budgeting

Figure 7.9

The pressures and politics of capital investment

Figure 7.10

Target delivery delay as the output from a goal formation policy

Figure 7.11

Converting information into action – an information feedback view of management

Figure 7.12

The policy function, information filters and bounded rationality – behavioural decision making

Figure 7.13

Behavioural decision making leading to stock accumulation and information feedback

Figure 7.14

Policy structure and formulations for sales growth

Figure 7.15

Policy structure and formulations for limits to sales growth

Figure 7.16

Graph for the effect of delivery delay on orders

Figure 7.17

Graph for capacity utilisation

Figure 7.18

Policy structure and formulations for capital investment

Figure 7.19

Graph for capacity expansion fraction

Figure 7.20

Simulation of sales growth loop in isolation

Figure 7.21

Sales growth with four per cent increase in product price

Figure 7.22

Sales growth with 25 per cent increase in sales force salary

Figure 7.23

Limits to sales growth with fixed capacity

Figure 7.24

Dynamics of sales force with fixed capacity

Figure 7.25

Customer orders in the base case of the full model – simulated and sketched

Figure 7.26

Delivery delay and production capacity in the base case of the full model

Figure 7.27

Sales force, customer orders and order fill rate in the base case of the full model

Figure 7.28

Customer orders and delivery delay with optimistic investment (sketch shows the base case)

Figure 7.29

Customer orders and delivery delay with fixed operating goal (sketch shows the base case)

Figure 7.30

Customer orders and delivery delay with wrong fixed operating goal (sketch shows the base case)

Figure 7.31

Overview of policy structure driving growth

Figure 7.32

Overview of policy structure constraining growth

Figure 7.33

Overview of policy structure for reactive capital investment

Figure 7.34

A framework for modelling the growth of new products and services in a competitive industry

Figure 7.35

The gain of a reinforcing loop

Figure 7.36

Parameters affecting the gain of the sales growth loop

Chapter 8

Figure 8.1

Historical oil price

Figure 8.2

Simple balancing loop with delay in the oil industry

Figure 8.3

Overview of global oil producers

Figure 8.4

Independents' upstream investment

Figure 8.5

Estimated development costs in 1988 (left) and in 1995 (right)

Figure 8.6

Estimated effect of technology on cost in 1988 (left) and in 1995 (right)

Figure 8.7

Policy structure and formulations for independents' upstream investment

Figure 8.8

Demand and price setting

Figure 8.9

Swing producer in swing mode

Figure 8.10

Swing producer in punitive mode

Figure 8.11

Quota setting

Figure 8.12

Quota negotiation and allocation

Figure 8.13

Opportunists' production and capacity

Figure 8.14

Replenishing independents' reserves with Russian reserves

Figure 8.15

Scatter list of phrases describing the global oil industry

Figure 8.16

Feedback loops in the oil producers' model

Figure 8.17

More feedback loops

Figure 8.18

Opening screen of Oil World 1988 showing simulator controls for scenarios

Figure 8.19

Demand and oil production in an archive scenario: OPEC squeeze then glut without Russian oil

Figure 8.20

Oil price and profitability of independents' capacity in an archive scenario: OPEC squeeze then glut without Russian oil

Figure 8.21

Demand and oil production in an archive scenario: Quota busting in a green world without Russian oil

Figure 8.22

Oil price and profitability of independents' capacity in an archive scenario: Quota busting in a green world without Russian oil

Figure 8.23

Demand and oil price in a scenario from the mid-1990s to 2020: Asian boom with quota busting, cautious upstream investment and Russian oil

Figure 8.24

Production profiles and profitability of independents' capacity in a scenario from the mid-1990s to 2020: Asian boom with quota busting, cautious upstream investment and Russian oil

Figure 8.25

Commercial reserves and development cost in a scenario from the mid-1990s to 2020: Asian boom with quota busting, cautious upstream investment and Russian oil

Figure 8.26

Demand and oil price in a high price scenario from the mid-1990s to 2020: Asian boom with OPEC undersupply, very cautious upstream investment and Russian oil

Figure 8.27

Production and OPEC quotas in a 2010–2034 scenario: subdued global oil economy with shale gale and OPEC supply boost

Figure 8.28

Demand and oil price in a 2010–2034 scenario: subdued global oil economy with shale gale and OPEC supply boost

Figure 8.29

Independents' reserves, development cost and profitability in a 2010 scenario: subdued global oil economy with shale gale and OPEC supply boost

Figure 8.30

Production and OPEC quotas in a modified 2010–2034 scenario: subdued global oil economy with shale gale and punitive Saudi supply control

Figure 8.31

Production and OPEC quotas in a 2010 thought experiment: subdued global oil economy with shale gale and mooted US supply control; the ‘Saudi America’ hypothesis

Chapter 9

Figure 9.1

Growth of London

Figure 9.2

Preliminary dynamic hypothesis for urban growth and stagnation

Figure 9.3

Stocks and flows in urban dynamics model

Figure 9.4

Information links to underemployed arrivals

Figure 9.5

Base run of the urban dynamics model

Figure 9.6

The medical workforce planning model

Figure 9.7

Quality of patient care management

Figure 9.8

Base run of the medical workforce planning model

Figure 9.9

Quality of patient care in the base run

Figure 9.10

Factors influencing the change in morale of junior doctors

Figure 9.11

Sector map of the complete medical workforce dynamics and patient care model

Figure 9.12

Work–life balance and rota flexibility

Figure 9.13

Simulated behaviour of junior doctor morale in the complete model

Figure 9.14

Composition of the medical workforce in the complete model

Figure 9.15

Quality of patient care and patient–doctor ratio. A comparison of the complete model (top chart) with the base run (bottom chart)

Figure 9.16

Simulation of a natural fishery with an initial population of 200 fish and maximum fishery size of 4000

Figure 9.17

A simple harvested fishery

Figure 9.18

Simulation of a harvested fishery with stepwise changes in fleet size

Figure 9.19

Fleet adjustment in a harvested fishery

Figure 9.20

Formulation of propensity for growth and catch per ship

Figure 9.21

Overview of a simple fisheries model with endogenous investment

Figure 9.22

Simulation of a fishery that starts in equilibrium, grows with investment and then unexpectedly collapses

Figure 9.23

Policy design in fisheries

Figure 9.24

Overview of fisheries policy model

Figure 9.25

Equations for deployment policy and ships

Figure 9.26

Opening screen of fisheries policy model

Figure 9.27

Dynamics of a regulated fishery – base case

Figure 9.28

The stabilising effect of a higher benchmark for fish density

Figure 9.29

Dynamics of a weakly regulated fishery

Figure 9.30

Lower exit barriers – the effect of quicker scrapping of idle ships

Figure 9.31

Lower exit barriers – a closer look at ship deployment and scrap rate, over 20 years

Figure 9.32

Screen shot from the original 1989 SimCity showing a spatial representation of city infrastructure

Figure 9.33

Comparison of discrete-event and system dynamics models of a natural fishery

Figure 9.34

Dynamics of a natural fishery from a discrete-event simulation model

Figure 9.35

Discrete-event model of a harvested fishery

Figure 9.36

Dynamics of a harvested fishery from a discrete-event simulation model

Figure 9.37

Puzzling dynamics of fisheries

Chapter 10

Figure 10.1

Formal model as transitional object for individual learning – ‘gears of childhood’

Figure 10.2

Formal model as transitional object for team learning

Figure 10.3

Sources of information for modelling

Figure 10.4

Modelling for learning

Figure 10.5

Total market volume and market share by product (1987–2001)

Figure 10.6

Role of the Mental Data Base in Modelling and Confidence Building

Figure 10.7

Management team's first conceptual model of soap market

Figure 10.8

Refined conceptual model of soap market

Figure 10.9

Sector map of the soap industry model

Figure 10.10

Function determining the strength of Old English's competitive response to market performance

Figure 10.11

Equations for bar soap volume and substitution by shower gel

Figure 10.12

Equations for brand switching

Figure 10.13

Consumer response to promotional price in branded bar soaps

Figure 10.14

Consumer response to promotional price in branded and private label soaps

Figure 10.15

Consumer response to promotional price for bar and liquid soaps

Figure 10.16

Comparing actual and simulated data

Figure 10.17

Branded liquid soaps simulated and real volumes

Figure 10.18

Branded liquid soaps simulated and real prices

Figure 10.19

Me Too Liquid Soap: Simulated and real price; simulated market share and income from trade margin

Figure 10.20

Partial model experiments: alternative trajectories for sales volume of liquid soap as imagined first-mover advantage fades and rivals imitate the new product

Figure 10.21

Main feedback loops underlying competitive dynamics in the FMCG industry

Figure 10.22

Opportunities for building confidence in models

Figure 10.23

Modelling and realism – a spectrum of model fidelity

Guide

Cover

Table of Contents

Preface

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About the Author

John Morecroft is Senior Fellow in Management Science and Operations at London Business School where he has taught system dynamics, problem structuring and strategy in MBA, PhD and Executive Education programmes. He served as Associate Dean of the School's Executive MBA and co-designed EMBA-Global, a dual degree programme with New York's Columbia Business School. He is a leading expert in system dynamics and strategic modelling. His publications include numerous journal articles and three co-edited books. He is a recipient of the Jay Wright Forrester Award of the System Dynamics Society for his work on bounded rationality, information feedback and behavioural decision making in models of the firm. He is a Past President of the Society and one of its Founding Members. His research interests include the dynamics of firm performance and the use of models and simulation in strategy development. He has led applied research projects for international organisations including Royal Dutch/Shell, AT&T, BBC World Service, Cummins Engine Company, Ford of Europe, Harley-Davidson, Ericsson, McKinsey & Co and Mars. Before joining London Business School he was on the faculty of MIT's Sloan School of Management where he received his PhD. He also holds degrees in Operational Research from Imperial College, London and in Physics from Bristol University.

Foreword by Peter Checkland

When I was a manager in the synthetic fibre industry in the 1950s and 60s, there was a recognised but problematical pattern of activity in the textile industry. A small increase in demand for textile products would induce big ups and downs back in yarn and fabric production. This arose as a result of the structure of the production-to-retail chain, a sequence from fibre to yarn to fabric to products, these being stages in the hands of different companies between which were time delays. This recurring pattern of response to demand change was one which no one stakeholder could command and control.