Library Catalogue
Business dynamics: Systems thinking and modeling for a complex world /
Sterman, John D
Business dynamics: Systems thinking and modeling for a complex world / John D. Sterman - 13th reprint - Haryana McGraw Hill Education 2023 - xxvi, 982 pages 24 cm
Preface 1. Learning in and about Complex Systems-
1.1 Introduction-
1.1.1 Policy Resistance, the Law of Unintended Consequences, and the Counterintuitive Behavior of Social Systems-
1.1.2 Causes of Policy Resistance-
1.1.3 Feedback-
1.1.4 Process Point: The Meaning of Feedback-
Challenge: Dynamics of Multiple-Loop Systems-
1.2 Learning is a Feedback Process-
1.3 Barriers to Learning-
1.3.1 Dynamic Complexity-
1.3.2 Limited Information-
1.3.3 Confounding Variables and Ambiguity-
1.3.4 Bounded Rationality and the Misperceptions of Feedback-
1.3.5 Flawed Cognitive Maps-
1.3.6 Erroneous Inferences about Dynamics-
1.3.7 Unscientific Reasoning: Judgmental Errors and Biases-
Challenge: Hypothesis Testing-
1.3.8 Defensive Routines and Interpersonal Impediments to Learning-
1.3.9 Implementation Failure-
1.4 Requirements for Successful Learning in Complex Systems-
1.4.1 Improving the Learning Process: Virtues of Virtual Worlds-
1.4.2 Pitfalls of Virtual Worlds-
1.4.3 Why Simulation is Essential-
1.5 Summary-
2. System Dynamics in Action-
2.1 Applications of System Dynamics-
2.2 Automobile Leasing Strategy: Gone Today, Here Tomorrow-
2.2.1 Dynamic Hypothesis-
2.2.2 Elaborating the Model-
2.2.3 Policy Analysis-
2.2.4 Impact and Follow-up-
2.3 On Time and Under Budget: The Dynamics of Project Management-
2.3.1 The Claim-
2.3.2 Initial Model Development-
2.3.3 Dynamic Hypothesis-
2.3.4 The Modeling Process-
2.3.5 Continuing Impact-
2.4 Playing the Maintenance Game-
2.4.1 Dynamic Hypothesis-
2.4.2 The Implementation Challenge-
2.4.3 Results-
2.4.4 Transferring the Learning: The Lima Experience-
2.5 Summary: Principles for Successful Use of System Dynamics-
3. The Modeling Process-
3.1 The Purpose of Modeling: Managers as Organization Designers-
3.2 The Client and the Modeler-
3.3 Steps of the Modeling Process-
3.4 Modeling is Iterative-
3.5 Overview of the Modeling Process-
3.5.1 Problem Articulation: The Importance of Purpose-
3.5.2 Formulating a Dynamic Hypothesis-
3.5.3 Formulating a Simulation Model-
3.5.4 Testing-
3.5.5 Policy Design and Evaluation-
3.6 Summary-
4. Structure and Behavior of Dynamic Systems-
4.1 Fundamental Modes of Dynamic Behavior-
4.1.1 Exponential Growth-
4.1.2 Goal Seeking-
4.1.3 Oscillation-
4.1.4 Process Point-
Challenge: Identifying Feedback Structure from System Behavior-
4.2 Interactions of the Fundamental Modes-
4.2.1 S-shaped Growth-
4.2.2 S-Shaped Growth with Overshoot-
Challenge: Identifying the Limits to Growth-
4.2.3 Overshoot and Collapse-
4.3 Other Modes of Behavior-
4.3.1 Stasis, or Equilibrium-
4.3.2 Randomness-
4.3.3 Chaos-
4.4 Summary-
5. Causal Loop Diagrams-
5.1 Causal Diagram Notation-
5.2 Guidelines for Causal Loop Diagrams-
5.2.1 Causation versus Correlation-
5.2.2 Labeling Link Polarity-
Challenge: Assigning Link Polarities-
5.2.3 Determining Loop Polarity-
Challenge: Employee Motivation-
5.2.4 Name Your Loops-
5.2.5 Indicate Important Delays in Causal Links-
5.2.6 Variable Names-
5.2.7 Tips for Causal Loop Diagram Layout-
5.2.8 Choose the Right Level of Aggregation-
5.2.9 Don’t Put All the Loops into One Large Diagram-
5.2.10 Make the Goals of Negative Loops Explicit-
5.2.11 Distinguish between Actual and Perceived Conditions-
5.3 Process Point: Developing Causal Diagrams from Interview Data-
Challenge: Process Improvement-
5.4 Conceptualization Case Study: Managing Your Workload-
5.4.1 Problem Definition-
5.4.2 Identifying Key Variables-
5.4.3 Developing the Reference Mode-
5.4.4 Developing the Causal Diagrams-
5.4.5 Limitations of the Causal Diagram-
Challenge: Policy Analysis with Causal Diagrams-
5.5 Adam Smith’s Invisible Hand and the Feedback Structure of Markets-
Challenge: The Oil Crises of the 1970s-
Challenge: Speculative Bubbles-
Challenge: The Thoroughbred Horse Market-
5.4.1 Market Failure, Adverse Selection, and the Death Spiral-
5.6 Explaining Policy Resistance: Traffic Congestion-
5.6.1 Mental Models of the Traffic Problem-
5.6.2 Compensating Feedback: The Response to Decreased Congestion-
5.6.3 The Mass Transit Death Spiral-
5.6.4 Policy Analysis: The Impact of Technology-
5.6.5 Compensating Feedback: The Source of Policy Resistance-
Challenge: Identifying the Feedback Structure of Policy Resistance-
5.7 Summary-
6. Stocks and Flows-
6.1 Stocks, Flows, and Accumulation-
6.1.1 Diagramming Notation for Stocks and Flows-
6.1.2 Mathematical Representation of Stocks and Flows-
6.1.3 The Contribution of Stocks to Dynamics-
6.2 Identifying Stocks and Flows-
6.2.1 Units of Measure in Stock and Flow Networks-
6.2.2 The Snapshot Test-
Challenge: Identifying Stocks and Flows-
6.2.3 Conservation of Material in Stock and Flow Networks-
6.2.4 State-Determined Systems-
6.2.5 Auxiliary Variables-
6.2.6 Stocks Change only Through their Rates-
6.2.7 Continuous Time and Instantaneous Flows-
6.2.8 Continuously Divisible versus Quantized Flows-
6.2.9 Which Modeling Approach Should You Use?-
6.2.10 Process Point: Portraying Stocks and Flows in Practice-
6.3 Mapping Stocks and Flows-
6.3.1 When Should Causal Loop Diagrams Show Stock and Flow Structure?-
Challenge: Adding Stock and Flow Structure to Causal Diagrams-
Challenge: Linking Stock and Flow Structure with Feedback-
6.3.2 Aggregation in Stock and Flow Mapping-
Challenge: Modifying Stock and Flow Maps-
Challenge: Disaggregation-
6.3.3 Guidelines for Aggregation-
6.3.4 System Dynamics in Action: Modeling Large-Scale Construction Projects-
6.3.5 Setting the Model Boundary: "Challenging the Clouds"-
6.3.6 System Dynamics in Action: Automobile Recycling-
6.4 Summary-
7. Dynamics of Stocks and Flows-
7.1 Relationship between Stocks and Flows-
7.1.1 Static and Dynamic Equilibrium-
7.1.2 Calculus without Mathematics-
7.1.3 Graphical Integration-
7.1.4 Graphical Differentiation-
Challenge: Graphical Differentiation-
7.2 System Dynamics in Action: Global Warming-
7.3 System Dynamics in Action: The War on Drugs-
7.3.1 The Cocaine Epidemic after 1990-
7.4 Summary-
8. Closing the Loop: Dynamics of Simple Structures-
8.1 First-order Systems-
8.2 Positive Feedback and Exponential Growth-
8.2.1 Analytic Solution for the Linear First-Order System-
8.2.2 Graphical Solution of the Linear First-Order Positive Feedback System-
8.2.3 The Power of Positive Feedback: Doubling Times-
Challenge: Paper Folding-
8.2.4 Misperceptions of Exponential Growth-
8.2.5 Process Point: Overcoming Overconfidence-
8.3 Negative Feedback and Exponential Decay-
8.3.1 Time constants and half lives-
Challenge: Goal-seeking behavior-
8.4 Multiple-Loop Systems-
8.5 Nonlinear First-Order Systems: S-Shaped Growth-
Challenge: Nonlinear Birth and Death Rates-
8.5.1 Formal Definition of Loop Dominance-
8.5.2 First-Order Systems Cannot Oscillate-
8.6 Summary-
9. S-Shaped Growth: Epidemics, Innovation Diffusion, and the Growth of New Products-
9.1 Modeling S-Shaped Growth-
9.1.1 Logistic Growth-
9.1.2 Analytic Solution of the Logistic Equation-
9.1.3 Other Common Growth Models-
9.1.4 Testing the Logistic Model-
9.2 Dynamics of Disease: Modeling Epidemics-
9.2.1 A Simple Model of Infectious Disease-
9.2.2 Modeling Acute Infection: The SIR Model-
9.2.3 Model Behavior: The Tipping Point-
Challenge: Exploring the SIR Model-
9.2.4 Immunization and the Eradication of Smallpox-
Challenge: The Efficacy Of Immunization Programs-
9.2.5 Herd Immunity-
9.2.6 Moving Past The Tipping Point: Mad Cow Disease-
Challenge: Extending the SIR Model-
9.2.7 Modeling the HIV/AIDS Epidemic-
Challenge: Modeling HIV/AIDS-
9.3 Innovation Diffusion as Infection: Modeling New Ideas and New Products-
9.3.1 The Logistic Model of Innovation Diffusion: Examples-
9.3.2 Process Point: Historical Fit and Model Validity-
9.3.3 The Bass Diffusion Model-
Challenge: Phase Space of the Bass Diffusion Model-
9.3.4 Behavior of the Bass Model-
Challenge: Critiquing the Bass Diffusion Model-
Challenge: Extending the Bass Model-
9.3.5 Fad and Fashion: Modeling the Abandonment of an Innovation-
Challenge: Modeling Fads-
9.3.6 Replacement Purchases-
Challenge: Modeling the Life Cycle of Durable Products-
9.4 Summary-
10. Path Dependence and Positive Feedback-
10.1 Path Dependence-
Challenge: Identifying Path Dependence-
10.2 A Simple Model of Path Dependence: The Polya Process-
10.2.1 Generalizing the Model: Nonlinear Polya Processes-
10.3 Path Dependence in the Economy: VHS vs. Betamax-
Challenge: Formulating a Dynamic Hypothesis for the VCR Industry-
10.4 Positive Feedback: The Engine of Corporate Growth-
10.4.1 Product Awareness-
10.4.2 Unit Development Costs-
10.4.3 Price and Production Cost-
10.4.4 Network Effects and Complementary Goods-
10.4.5 Product Differentiation-
10.4.6 New Product Development-
10.4.7 Market Power-
10.4.8 Mergers and Acquisitions-
10.4.9 Workforce Quality and Loyalty-
10.4.10 The Cost of Capital-
10.4.11 The Rules of the Game-
10.4.12 Ambition and Aspirations-
10.4.13 Creating Synergy for Corporate Growth-
10.5 Positive Feedback, Increasing Returns, and Economic Growth-
10.6 Does the Economy Lock in to Inferior Technologies?-
10.7 Limits to Lock In-
10.8 Modeling Path Dependence and Standards Formation-
10.8.1 Model Structure-
10.8.2 Model Behavior-
10.8.3 Policy Implications-
Challenge: Policy Analysis-
Challenge: Extending the Model-
10.9 Summary-
11. Delays-
11.1 Delays: An Introduction
Challenge: Duration and Dynamics of Delays-
11.1.1 Defining Delays-
11.2 Material Delays: Structure and Behavior-
11.2.1 What is the Average Length of the Delay?-
11.2.2 What is the Distribution of the Output around the Average Delay Time?-
11.2.3 Pipeline Delay-
11.2.4 First-Order Material Delay-
11.2.5 Higher-Order Material Delays-
11.2.6 How Much is in the Delay? Little’s Law-
11.3 Information Delays: Structure and Behavior-
11.3.1 Modeling Perceptions: Adaptive Expectations and Exponential Smoothing-
11.3.2 Higher-Order Information Delays-
11.4 Response to Variable Delay Times-
Challenge: Response of Delays to Changing Delay Times-
11.4.1 Nonlinear Adjustment Times: Modeling Ratchet Effects-
11.5 Estimating the Duration and Distribution of Delays-
11.5.1 Estimating Delays when Numerical Data are Available-
11.5.2 Estimating Delays when Numerical Data are not Available-
11.5.3 Process Point: Walk the Line-
11.6 System Dynamics in Action: Forecasting Semiconductor Demand-
11.7 Mathematics of Delays: Koyck Lags and Erlang Distributions-
11.7.1 General Formulation for Delays-
11.7.2 First-Order Delay-
11.7.3 Higher-Order Delays-
11.7.4 Relation of Material and Information Delays-
11.8 Summary-
12. Coflows and Aging Chains-
12.1 Aging Chains-
12.1.1 General Structure of Aging Chains-
12.1.2 Example: Population and Infrastructure in Urban Dynamics-
12.1.3 Example: The Population Pyramid and the Demographic Transition-
12.1.4 Aging Chains and Population Inertia-
12.1.5 System Dynamics in Action: World Population and Economic Development-
12.1.6 Case Study: Growth and the Age Structure of Organizations-
12.1.7 Promotion Chains and the Learning Curve-
12.1.8 Mentoring and On-The-Job Training-
Challenge: The Interactions of Training Delays and Growth-
12.2 Coflows: Modeling the Attributes of a Stock-
Challenge: Coflows-
12.2.1 Coflows with Nonconserved Flows-
Challenge: The Dynamics of Experience and Learning-
12.2.2 Integrating Coflows and Aging Chains-
Challenge: Modeling Design Wins in the Semiconductor Industry-
12.3 Summary-
13. Modeling Decision Making-
13.1 Principles for Modeling Decision Making-
13.1.1 Decisions and Decision Rules-
13.1.2 Five Formulation Fundamentals-
Challenge: Finding Formulation Flaws-
13.2 Formulating Rate Equations-
13.2.1 Fractional Increase Rate-
13.2.2 Fractional Decrease Rate-
13.2.3 Adjustment to a Goal-
13.2.4 The Stock Management Structure: Rate = Normal Rate + Adjustments-
13.2.5 Flow = Resource * Productivity-
13.2.6 Y = Y* * Effect of X1 on Y * Effect of X2 on Y * … * Effect of Xn on Y-
13.2.7 Y = Y* + Effect of X1 on Y + Effect of X2 on Y + … + Effect of Xn on Y-
13.2.8 Fuzzy MIN Function-
13.2.9 Fuzzy MAX Function-
13.2.10 Floating Goals-
Challenge: Floating Goals-
Challenge: Goal Formation with Internal and External Inputs-
13.2.11 Nonlinear Weighted Average-
13.2.12 Modeling Search: Hill-Climbing Optimization-
Challenge: Finding the Optimal Mix of Capital and Labor-
13.2.13 Resource Allocation-
13.3 Common Pitfalls-
13.3.1 All Outflows Require First-Order Control-
Challenge: Preventing Negative Stocks-
13.3.2 Avoid IF…THEN…ELSE Formulations-
13.3.3 Disaggregate Net Flows-
13.4 Summary-
14. Formulating Nonlinear Relationships-
14.1 Table Functions-
14.1.1 Specifying Table Functions-
14.1.2 Example: Building a Nonlinear Function-
14.1.3 Process Point: Table Functions Versus Analytic Functions-
14.2 Case Study: Cutting Corners Versus Overtime-
Challenge: Formulating Nonlinear Functions-
14.2.1 Working Overtime: The Effect of Schedule Pressure on Workweek-
14.2.2 Cutting Corners: The Effect of Schedule Pressure on Time per Task-
14.3 Case Study: Estimating Nonlinear Functions With Qualitative and Numerical Data-
Challenge: Refining Table Functions with Qualitative Data-
14.4 Common Pitfalls-
14.4.1 Using the Wrong Input-
Challenge: Critiquing Nonlinear Functions-
14.4.2 Improper Normalization-
14.4.3 Avoid Hump-shaped Functions-
Challenge: Formulating the Error Rate-
Challenge: Testing the Full Model-
14.5 Eliciting Model Relationships Interactively-
14.5.1 Case Study: Estimating Precedence Relationships in Product Development-
14.6 Summary-
15. Modeling Human Behavior: Bounded Rationality or Rational Expectations?-
15.1 Human Decision Making: Bounded Rationality or Rational Expectations?-
15.2 Cognitive Limitations-
15.3 Individual and Organizational Responses to Bounded Rationality-
15.3.1 Habit, Routines, and Rules of Thumb-
15.3.2 Managing Attention-
15.3.3 Goal Formation and Satisficing-
15.3.4 Problem Decomposition and Decentralized Decision Making-
15.4 Intended Rationality-
15.4.1 Testing for Intended Rationality: Partial Model Tests-
15.5 Case Study: Modeling High-Tech Growth Firms-
15.5.1 Model Structure: Overview-
15.5.2 Order Fulfillment-
15.5.3 Capacity Acquisition-
Challenge: Hill Climbing-
15.5.4 The Sales Force-
15.5.5 The Market-
15.5.6 Behavior of the Full System-
Challenge: Policy Design in the Market Growth Model-
15.6 Summary-
16. Forecasts and Fudge Factors: Modeling Expectation Formation-
16.1 Modeling Expectation Formation-
16.1.1 Modeling Growth Expectations: The TREND Function-
16.1.2 Behavior of the TREND Function-
16.2 Case Study: Energy Consumption-
16.3 Case Study: Commodity Prices-
16.4 Case Study: Inflation-
16.5 Implications for Forecast Consumers-
Challenge: Extrapolation and Stability-
16.6 Initialization and Steady State Response of the TREND Function-
16.7 Summary-
17. Supply Chains and the Origin of Oscillations-
17.1 Supply Chains in Business and Beyond-
17.1.1 Oscillation, Amplification, and Phase Lag-
17.2 The Stock Management Problem-
17.2.1 Managing a Stock: Structure-
17.2.2 Steady State Error-
17.2.3 Managing a Stock: Behavior-
17.3 The Stock Management Structure-
17.3.1 Behavior of the Stock Management Structure-
17.4 The Origin of Oscillations-
17.4.1 Mismanaging the Supply Line: The Beer Distribution Game-
17.4.2 Why Do We Ignore the Supply Line?-
17.4.3 Case Study: Boom and Bust in Real Estate Markets-
17.5 Summary-
18. The Manufacturing Supply Chain-
18.1 The Policy Structure of Inventory and Production-
18.1.1 Order Fulfillment-
18.1.2 Production-
18.1.3 Production Starts-
18.1.4 Demand Forecasting-
18.1.5 Process Point: Initializing a Model in Equilibrium-
Challenge: Simultaneous Initial Conditions-
18.1.6 Behavior of the Production Model-
18.1.7 Enriching the Model: Adding Order Backlogs-
18.1.8 Behavior of the Firm with Order Backlogs-
18.1.9 Adding Raw Materials Inventory-
18.2 Interactions among Supply Chain Partners-
18.2.1 Instability and Trust in Supply Chains-
18.2.2 From Functional Silos to Integrated Supply Chain Management-
Challenge: Reengineering the Supply Chain-
18.3 System Dynamics in Action: Reengineering the Supply Chain in a High-Velocity Industry-
18.3.1 Initial Problem Definition-
18.3.2 Reference Mode and Dynamic Hypothesis-
18.3.3 Model Formulation-
18.3.4 Testing the Model-
18.3.5 Policy Analysis-
18.3.6 Implementation: Sequential Debottlenecking-
18.3.7 Results-
18.4 Summary-
19. The Labor Supply Chain and the Origin of Business Cycles-
19.1 The Labor Supply Chain-
19.1.1 Structure of Labor and Hiring-
19.1.2 Behavior of the Labor Supply Chain-
19.2 Interactions of Labor and Inventory Management-
Challenge: Mental Simulation of Inventory Management with Labor-
19.2.1 Inventory—Workforce Interactions: Behavior-
19.2.2 Process Point: Explaining Model Behavior-
Challenge: Explaining Oscillations-
19.2.3 Understanding the Sources of Oscillation-
Challenge: Policy Design to Enhance Stability-
19.2.4 Adding Overtime-
19.2.5 Response to Flexible Workweeks-
Challenge: Reengineering a Manufacturing Firm for Enhanced Stability-
19.2.6 The Costs of Instability-
Challenge: The Costs of Instability-
Challenge: Adding Training and Experience-
19.3 Inventory—Workforce Interactions and the Business Cycle-
19.3.1 Is the Business Cycle Dead?-
19.4 Summary-
20. The Invisible Hand Sometimes Shakes: Commodity Cycles-
20.1 Commodity Cycles: From Aircraft to Zinc-
20.2 A Generic Commodity Market Model-
20.2.1 Production and Inventory-
20.2.2 Capacity Utilization-
20.2.3 Production Capacity-
20.2.4 Desired Capacity-
Challenge: Intended Rationality of the Investment Process-
20.2.5 Demand-
20.2.6 The Price-Setting Process-
20.3 Application: Cycles in the Pulp and Paper Industry-
Challenge: Sensitivity to Uncertainty in Parameters-
Challenge: Sensitivity to Structural Changes-
Challenge: Implementing Structural Changes–Modeling Livestock Markets-
Challenge: Policy Analysis-
20.4 Summary-
21. Truth and Beauty: Validation and Model Testing-
21.1 Validation and Verification are Impossible-
21.2 Questions Model Users Should Ask–But Usually Don’t-
21.3 Pragmatics and Politics of Model Use-
21.3.1 Types of Data-
21.3.2 Documentation-
21.3.3 Replicability-
21.3.4 Protective versus Reflective Modeling-
21.4 Model Testing in Practice-
21.4.1 Boundary Adequacy Tests-
21.4.2 Structure Assessment Tests-
21.4.3 Dimensional Consistency-
21.4.4 Parameter Assessment-
21.4.5 Extreme Condition Tests-
Challenge: Extreme Condition Tests-
21.4.6 Integration Error Tests-
21.4.7 Behavior Reproduction Tests-
21.4.8 Behavior Anomaly Tests-
21.4.9 Family Member Tests-
21.4.10 Surprise Behavior Tests-
21.4.11 Sensitivity Analysis-
21.4.12 System Improvement Tests-
Challenge: Model Testing-
21.5 Summary-
22. Challenges for the Future-
22.1 Theory-
22.2 Technology-
22.3 Implementation-
22.4 Education-
22.5 Applications-
Challenge: Putting System Dynamics Into Action-
Appendix A Numerical Integration-
Challenge: Choosing a Time Step-
Appendix B Noise-
Challenge: Exploring Noise-
REFERENCES-
INDEX.
9780071068123 (paperback) 007106812
658.4038011
Business dynamics: Systems thinking and modeling for a complex world / John D. Sterman - 13th reprint - Haryana McGraw Hill Education 2023 - xxvi, 982 pages 24 cm
Preface 1. Learning in and about Complex Systems-
1.1 Introduction-
1.1.1 Policy Resistance, the Law of Unintended Consequences, and the Counterintuitive Behavior of Social Systems-
1.1.2 Causes of Policy Resistance-
1.1.3 Feedback-
1.1.4 Process Point: The Meaning of Feedback-
Challenge: Dynamics of Multiple-Loop Systems-
1.2 Learning is a Feedback Process-
1.3 Barriers to Learning-
1.3.1 Dynamic Complexity-
1.3.2 Limited Information-
1.3.3 Confounding Variables and Ambiguity-
1.3.4 Bounded Rationality and the Misperceptions of Feedback-
1.3.5 Flawed Cognitive Maps-
1.3.6 Erroneous Inferences about Dynamics-
1.3.7 Unscientific Reasoning: Judgmental Errors and Biases-
Challenge: Hypothesis Testing-
1.3.8 Defensive Routines and Interpersonal Impediments to Learning-
1.3.9 Implementation Failure-
1.4 Requirements for Successful Learning in Complex Systems-
1.4.1 Improving the Learning Process: Virtues of Virtual Worlds-
1.4.2 Pitfalls of Virtual Worlds-
1.4.3 Why Simulation is Essential-
1.5 Summary-
2. System Dynamics in Action-
2.1 Applications of System Dynamics-
2.2 Automobile Leasing Strategy: Gone Today, Here Tomorrow-
2.2.1 Dynamic Hypothesis-
2.2.2 Elaborating the Model-
2.2.3 Policy Analysis-
2.2.4 Impact and Follow-up-
2.3 On Time and Under Budget: The Dynamics of Project Management-
2.3.1 The Claim-
2.3.2 Initial Model Development-
2.3.3 Dynamic Hypothesis-
2.3.4 The Modeling Process-
2.3.5 Continuing Impact-
2.4 Playing the Maintenance Game-
2.4.1 Dynamic Hypothesis-
2.4.2 The Implementation Challenge-
2.4.3 Results-
2.4.4 Transferring the Learning: The Lima Experience-
2.5 Summary: Principles for Successful Use of System Dynamics-
3. The Modeling Process-
3.1 The Purpose of Modeling: Managers as Organization Designers-
3.2 The Client and the Modeler-
3.3 Steps of the Modeling Process-
3.4 Modeling is Iterative-
3.5 Overview of the Modeling Process-
3.5.1 Problem Articulation: The Importance of Purpose-
3.5.2 Formulating a Dynamic Hypothesis-
3.5.3 Formulating a Simulation Model-
3.5.4 Testing-
3.5.5 Policy Design and Evaluation-
3.6 Summary-
4. Structure and Behavior of Dynamic Systems-
4.1 Fundamental Modes of Dynamic Behavior-
4.1.1 Exponential Growth-
4.1.2 Goal Seeking-
4.1.3 Oscillation-
4.1.4 Process Point-
Challenge: Identifying Feedback Structure from System Behavior-
4.2 Interactions of the Fundamental Modes-
4.2.1 S-shaped Growth-
4.2.2 S-Shaped Growth with Overshoot-
Challenge: Identifying the Limits to Growth-
4.2.3 Overshoot and Collapse-
4.3 Other Modes of Behavior-
4.3.1 Stasis, or Equilibrium-
4.3.2 Randomness-
4.3.3 Chaos-
4.4 Summary-
5. Causal Loop Diagrams-
5.1 Causal Diagram Notation-
5.2 Guidelines for Causal Loop Diagrams-
5.2.1 Causation versus Correlation-
5.2.2 Labeling Link Polarity-
Challenge: Assigning Link Polarities-
5.2.3 Determining Loop Polarity-
Challenge: Employee Motivation-
5.2.4 Name Your Loops-
5.2.5 Indicate Important Delays in Causal Links-
5.2.6 Variable Names-
5.2.7 Tips for Causal Loop Diagram Layout-
5.2.8 Choose the Right Level of Aggregation-
5.2.9 Don’t Put All the Loops into One Large Diagram-
5.2.10 Make the Goals of Negative Loops Explicit-
5.2.11 Distinguish between Actual and Perceived Conditions-
5.3 Process Point: Developing Causal Diagrams from Interview Data-
Challenge: Process Improvement-
5.4 Conceptualization Case Study: Managing Your Workload-
5.4.1 Problem Definition-
5.4.2 Identifying Key Variables-
5.4.3 Developing the Reference Mode-
5.4.4 Developing the Causal Diagrams-
5.4.5 Limitations of the Causal Diagram-
Challenge: Policy Analysis with Causal Diagrams-
5.5 Adam Smith’s Invisible Hand and the Feedback Structure of Markets-
Challenge: The Oil Crises of the 1970s-
Challenge: Speculative Bubbles-
Challenge: The Thoroughbred Horse Market-
5.4.1 Market Failure, Adverse Selection, and the Death Spiral-
5.6 Explaining Policy Resistance: Traffic Congestion-
5.6.1 Mental Models of the Traffic Problem-
5.6.2 Compensating Feedback: The Response to Decreased Congestion-
5.6.3 The Mass Transit Death Spiral-
5.6.4 Policy Analysis: The Impact of Technology-
5.6.5 Compensating Feedback: The Source of Policy Resistance-
Challenge: Identifying the Feedback Structure of Policy Resistance-
5.7 Summary-
6. Stocks and Flows-
6.1 Stocks, Flows, and Accumulation-
6.1.1 Diagramming Notation for Stocks and Flows-
6.1.2 Mathematical Representation of Stocks and Flows-
6.1.3 The Contribution of Stocks to Dynamics-
6.2 Identifying Stocks and Flows-
6.2.1 Units of Measure in Stock and Flow Networks-
6.2.2 The Snapshot Test-
Challenge: Identifying Stocks and Flows-
6.2.3 Conservation of Material in Stock and Flow Networks-
6.2.4 State-Determined Systems-
6.2.5 Auxiliary Variables-
6.2.6 Stocks Change only Through their Rates-
6.2.7 Continuous Time and Instantaneous Flows-
6.2.8 Continuously Divisible versus Quantized Flows-
6.2.9 Which Modeling Approach Should You Use?-
6.2.10 Process Point: Portraying Stocks and Flows in Practice-
6.3 Mapping Stocks and Flows-
6.3.1 When Should Causal Loop Diagrams Show Stock and Flow Structure?-
Challenge: Adding Stock and Flow Structure to Causal Diagrams-
Challenge: Linking Stock and Flow Structure with Feedback-
6.3.2 Aggregation in Stock and Flow Mapping-
Challenge: Modifying Stock and Flow Maps-
Challenge: Disaggregation-
6.3.3 Guidelines for Aggregation-
6.3.4 System Dynamics in Action: Modeling Large-Scale Construction Projects-
6.3.5 Setting the Model Boundary: "Challenging the Clouds"-
6.3.6 System Dynamics in Action: Automobile Recycling-
6.4 Summary-
7. Dynamics of Stocks and Flows-
7.1 Relationship between Stocks and Flows-
7.1.1 Static and Dynamic Equilibrium-
7.1.2 Calculus without Mathematics-
7.1.3 Graphical Integration-
7.1.4 Graphical Differentiation-
Challenge: Graphical Differentiation-
7.2 System Dynamics in Action: Global Warming-
7.3 System Dynamics in Action: The War on Drugs-
7.3.1 The Cocaine Epidemic after 1990-
7.4 Summary-
8. Closing the Loop: Dynamics of Simple Structures-
8.1 First-order Systems-
8.2 Positive Feedback and Exponential Growth-
8.2.1 Analytic Solution for the Linear First-Order System-
8.2.2 Graphical Solution of the Linear First-Order Positive Feedback System-
8.2.3 The Power of Positive Feedback: Doubling Times-
Challenge: Paper Folding-
8.2.4 Misperceptions of Exponential Growth-
8.2.5 Process Point: Overcoming Overconfidence-
8.3 Negative Feedback and Exponential Decay-
8.3.1 Time constants and half lives-
Challenge: Goal-seeking behavior-
8.4 Multiple-Loop Systems-
8.5 Nonlinear First-Order Systems: S-Shaped Growth-
Challenge: Nonlinear Birth and Death Rates-
8.5.1 Formal Definition of Loop Dominance-
8.5.2 First-Order Systems Cannot Oscillate-
8.6 Summary-
9. S-Shaped Growth: Epidemics, Innovation Diffusion, and the Growth of New Products-
9.1 Modeling S-Shaped Growth-
9.1.1 Logistic Growth-
9.1.2 Analytic Solution of the Logistic Equation-
9.1.3 Other Common Growth Models-
9.1.4 Testing the Logistic Model-
9.2 Dynamics of Disease: Modeling Epidemics-
9.2.1 A Simple Model of Infectious Disease-
9.2.2 Modeling Acute Infection: The SIR Model-
9.2.3 Model Behavior: The Tipping Point-
Challenge: Exploring the SIR Model-
9.2.4 Immunization and the Eradication of Smallpox-
Challenge: The Efficacy Of Immunization Programs-
9.2.5 Herd Immunity-
9.2.6 Moving Past The Tipping Point: Mad Cow Disease-
Challenge: Extending the SIR Model-
9.2.7 Modeling the HIV/AIDS Epidemic-
Challenge: Modeling HIV/AIDS-
9.3 Innovation Diffusion as Infection: Modeling New Ideas and New Products-
9.3.1 The Logistic Model of Innovation Diffusion: Examples-
9.3.2 Process Point: Historical Fit and Model Validity-
9.3.3 The Bass Diffusion Model-
Challenge: Phase Space of the Bass Diffusion Model-
9.3.4 Behavior of the Bass Model-
Challenge: Critiquing the Bass Diffusion Model-
Challenge: Extending the Bass Model-
9.3.5 Fad and Fashion: Modeling the Abandonment of an Innovation-
Challenge: Modeling Fads-
9.3.6 Replacement Purchases-
Challenge: Modeling the Life Cycle of Durable Products-
9.4 Summary-
10. Path Dependence and Positive Feedback-
10.1 Path Dependence-
Challenge: Identifying Path Dependence-
10.2 A Simple Model of Path Dependence: The Polya Process-
10.2.1 Generalizing the Model: Nonlinear Polya Processes-
10.3 Path Dependence in the Economy: VHS vs. Betamax-
Challenge: Formulating a Dynamic Hypothesis for the VCR Industry-
10.4 Positive Feedback: The Engine of Corporate Growth-
10.4.1 Product Awareness-
10.4.2 Unit Development Costs-
10.4.3 Price and Production Cost-
10.4.4 Network Effects and Complementary Goods-
10.4.5 Product Differentiation-
10.4.6 New Product Development-
10.4.7 Market Power-
10.4.8 Mergers and Acquisitions-
10.4.9 Workforce Quality and Loyalty-
10.4.10 The Cost of Capital-
10.4.11 The Rules of the Game-
10.4.12 Ambition and Aspirations-
10.4.13 Creating Synergy for Corporate Growth-
10.5 Positive Feedback, Increasing Returns, and Economic Growth-
10.6 Does the Economy Lock in to Inferior Technologies?-
10.7 Limits to Lock In-
10.8 Modeling Path Dependence and Standards Formation-
10.8.1 Model Structure-
10.8.2 Model Behavior-
10.8.3 Policy Implications-
Challenge: Policy Analysis-
Challenge: Extending the Model-
10.9 Summary-
11. Delays-
11.1 Delays: An Introduction
Challenge: Duration and Dynamics of Delays-
11.1.1 Defining Delays-
11.2 Material Delays: Structure and Behavior-
11.2.1 What is the Average Length of the Delay?-
11.2.2 What is the Distribution of the Output around the Average Delay Time?-
11.2.3 Pipeline Delay-
11.2.4 First-Order Material Delay-
11.2.5 Higher-Order Material Delays-
11.2.6 How Much is in the Delay? Little’s Law-
11.3 Information Delays: Structure and Behavior-
11.3.1 Modeling Perceptions: Adaptive Expectations and Exponential Smoothing-
11.3.2 Higher-Order Information Delays-
11.4 Response to Variable Delay Times-
Challenge: Response of Delays to Changing Delay Times-
11.4.1 Nonlinear Adjustment Times: Modeling Ratchet Effects-
11.5 Estimating the Duration and Distribution of Delays-
11.5.1 Estimating Delays when Numerical Data are Available-
11.5.2 Estimating Delays when Numerical Data are not Available-
11.5.3 Process Point: Walk the Line-
11.6 System Dynamics in Action: Forecasting Semiconductor Demand-
11.7 Mathematics of Delays: Koyck Lags and Erlang Distributions-
11.7.1 General Formulation for Delays-
11.7.2 First-Order Delay-
11.7.3 Higher-Order Delays-
11.7.4 Relation of Material and Information Delays-
11.8 Summary-
12. Coflows and Aging Chains-
12.1 Aging Chains-
12.1.1 General Structure of Aging Chains-
12.1.2 Example: Population and Infrastructure in Urban Dynamics-
12.1.3 Example: The Population Pyramid and the Demographic Transition-
12.1.4 Aging Chains and Population Inertia-
12.1.5 System Dynamics in Action: World Population and Economic Development-
12.1.6 Case Study: Growth and the Age Structure of Organizations-
12.1.7 Promotion Chains and the Learning Curve-
12.1.8 Mentoring and On-The-Job Training-
Challenge: The Interactions of Training Delays and Growth-
12.2 Coflows: Modeling the Attributes of a Stock-
Challenge: Coflows-
12.2.1 Coflows with Nonconserved Flows-
Challenge: The Dynamics of Experience and Learning-
12.2.2 Integrating Coflows and Aging Chains-
Challenge: Modeling Design Wins in the Semiconductor Industry-
12.3 Summary-
13. Modeling Decision Making-
13.1 Principles for Modeling Decision Making-
13.1.1 Decisions and Decision Rules-
13.1.2 Five Formulation Fundamentals-
Challenge: Finding Formulation Flaws-
13.2 Formulating Rate Equations-
13.2.1 Fractional Increase Rate-
13.2.2 Fractional Decrease Rate-
13.2.3 Adjustment to a Goal-
13.2.4 The Stock Management Structure: Rate = Normal Rate + Adjustments-
13.2.5 Flow = Resource * Productivity-
13.2.6 Y = Y* * Effect of X1 on Y * Effect of X2 on Y * … * Effect of Xn on Y-
13.2.7 Y = Y* + Effect of X1 on Y + Effect of X2 on Y + … + Effect of Xn on Y-
13.2.8 Fuzzy MIN Function-
13.2.9 Fuzzy MAX Function-
13.2.10 Floating Goals-
Challenge: Floating Goals-
Challenge: Goal Formation with Internal and External Inputs-
13.2.11 Nonlinear Weighted Average-
13.2.12 Modeling Search: Hill-Climbing Optimization-
Challenge: Finding the Optimal Mix of Capital and Labor-
13.2.13 Resource Allocation-
13.3 Common Pitfalls-
13.3.1 All Outflows Require First-Order Control-
Challenge: Preventing Negative Stocks-
13.3.2 Avoid IF…THEN…ELSE Formulations-
13.3.3 Disaggregate Net Flows-
13.4 Summary-
14. Formulating Nonlinear Relationships-
14.1 Table Functions-
14.1.1 Specifying Table Functions-
14.1.2 Example: Building a Nonlinear Function-
14.1.3 Process Point: Table Functions Versus Analytic Functions-
14.2 Case Study: Cutting Corners Versus Overtime-
Challenge: Formulating Nonlinear Functions-
14.2.1 Working Overtime: The Effect of Schedule Pressure on Workweek-
14.2.2 Cutting Corners: The Effect of Schedule Pressure on Time per Task-
14.3 Case Study: Estimating Nonlinear Functions With Qualitative and Numerical Data-
Challenge: Refining Table Functions with Qualitative Data-
14.4 Common Pitfalls-
14.4.1 Using the Wrong Input-
Challenge: Critiquing Nonlinear Functions-
14.4.2 Improper Normalization-
14.4.3 Avoid Hump-shaped Functions-
Challenge: Formulating the Error Rate-
Challenge: Testing the Full Model-
14.5 Eliciting Model Relationships Interactively-
14.5.1 Case Study: Estimating Precedence Relationships in Product Development-
14.6 Summary-
15. Modeling Human Behavior: Bounded Rationality or Rational Expectations?-
15.1 Human Decision Making: Bounded Rationality or Rational Expectations?-
15.2 Cognitive Limitations-
15.3 Individual and Organizational Responses to Bounded Rationality-
15.3.1 Habit, Routines, and Rules of Thumb-
15.3.2 Managing Attention-
15.3.3 Goal Formation and Satisficing-
15.3.4 Problem Decomposition and Decentralized Decision Making-
15.4 Intended Rationality-
15.4.1 Testing for Intended Rationality: Partial Model Tests-
15.5 Case Study: Modeling High-Tech Growth Firms-
15.5.1 Model Structure: Overview-
15.5.2 Order Fulfillment-
15.5.3 Capacity Acquisition-
Challenge: Hill Climbing-
15.5.4 The Sales Force-
15.5.5 The Market-
15.5.6 Behavior of the Full System-
Challenge: Policy Design in the Market Growth Model-
15.6 Summary-
16. Forecasts and Fudge Factors: Modeling Expectation Formation-
16.1 Modeling Expectation Formation-
16.1.1 Modeling Growth Expectations: The TREND Function-
16.1.2 Behavior of the TREND Function-
16.2 Case Study: Energy Consumption-
16.3 Case Study: Commodity Prices-
16.4 Case Study: Inflation-
16.5 Implications for Forecast Consumers-
Challenge: Extrapolation and Stability-
16.6 Initialization and Steady State Response of the TREND Function-
16.7 Summary-
17. Supply Chains and the Origin of Oscillations-
17.1 Supply Chains in Business and Beyond-
17.1.1 Oscillation, Amplification, and Phase Lag-
17.2 The Stock Management Problem-
17.2.1 Managing a Stock: Structure-
17.2.2 Steady State Error-
17.2.3 Managing a Stock: Behavior-
17.3 The Stock Management Structure-
17.3.1 Behavior of the Stock Management Structure-
17.4 The Origin of Oscillations-
17.4.1 Mismanaging the Supply Line: The Beer Distribution Game-
17.4.2 Why Do We Ignore the Supply Line?-
17.4.3 Case Study: Boom and Bust in Real Estate Markets-
17.5 Summary-
18. The Manufacturing Supply Chain-
18.1 The Policy Structure of Inventory and Production-
18.1.1 Order Fulfillment-
18.1.2 Production-
18.1.3 Production Starts-
18.1.4 Demand Forecasting-
18.1.5 Process Point: Initializing a Model in Equilibrium-
Challenge: Simultaneous Initial Conditions-
18.1.6 Behavior of the Production Model-
18.1.7 Enriching the Model: Adding Order Backlogs-
18.1.8 Behavior of the Firm with Order Backlogs-
18.1.9 Adding Raw Materials Inventory-
18.2 Interactions among Supply Chain Partners-
18.2.1 Instability and Trust in Supply Chains-
18.2.2 From Functional Silos to Integrated Supply Chain Management-
Challenge: Reengineering the Supply Chain-
18.3 System Dynamics in Action: Reengineering the Supply Chain in a High-Velocity Industry-
18.3.1 Initial Problem Definition-
18.3.2 Reference Mode and Dynamic Hypothesis-
18.3.3 Model Formulation-
18.3.4 Testing the Model-
18.3.5 Policy Analysis-
18.3.6 Implementation: Sequential Debottlenecking-
18.3.7 Results-
18.4 Summary-
19. The Labor Supply Chain and the Origin of Business Cycles-
19.1 The Labor Supply Chain-
19.1.1 Structure of Labor and Hiring-
19.1.2 Behavior of the Labor Supply Chain-
19.2 Interactions of Labor and Inventory Management-
Challenge: Mental Simulation of Inventory Management with Labor-
19.2.1 Inventory—Workforce Interactions: Behavior-
19.2.2 Process Point: Explaining Model Behavior-
Challenge: Explaining Oscillations-
19.2.3 Understanding the Sources of Oscillation-
Challenge: Policy Design to Enhance Stability-
19.2.4 Adding Overtime-
19.2.5 Response to Flexible Workweeks-
Challenge: Reengineering a Manufacturing Firm for Enhanced Stability-
19.2.6 The Costs of Instability-
Challenge: The Costs of Instability-
Challenge: Adding Training and Experience-
19.3 Inventory—Workforce Interactions and the Business Cycle-
19.3.1 Is the Business Cycle Dead?-
19.4 Summary-
20. The Invisible Hand Sometimes Shakes: Commodity Cycles-
20.1 Commodity Cycles: From Aircraft to Zinc-
20.2 A Generic Commodity Market Model-
20.2.1 Production and Inventory-
20.2.2 Capacity Utilization-
20.2.3 Production Capacity-
20.2.4 Desired Capacity-
Challenge: Intended Rationality of the Investment Process-
20.2.5 Demand-
20.2.6 The Price-Setting Process-
20.3 Application: Cycles in the Pulp and Paper Industry-
Challenge: Sensitivity to Uncertainty in Parameters-
Challenge: Sensitivity to Structural Changes-
Challenge: Implementing Structural Changes–Modeling Livestock Markets-
Challenge: Policy Analysis-
20.4 Summary-
21. Truth and Beauty: Validation and Model Testing-
21.1 Validation and Verification are Impossible-
21.2 Questions Model Users Should Ask–But Usually Don’t-
21.3 Pragmatics and Politics of Model Use-
21.3.1 Types of Data-
21.3.2 Documentation-
21.3.3 Replicability-
21.3.4 Protective versus Reflective Modeling-
21.4 Model Testing in Practice-
21.4.1 Boundary Adequacy Tests-
21.4.2 Structure Assessment Tests-
21.4.3 Dimensional Consistency-
21.4.4 Parameter Assessment-
21.4.5 Extreme Condition Tests-
Challenge: Extreme Condition Tests-
21.4.6 Integration Error Tests-
21.4.7 Behavior Reproduction Tests-
21.4.8 Behavior Anomaly Tests-
21.4.9 Family Member Tests-
21.4.10 Surprise Behavior Tests-
21.4.11 Sensitivity Analysis-
21.4.12 System Improvement Tests-
Challenge: Model Testing-
21.5 Summary-
22. Challenges for the Future-
22.1 Theory-
22.2 Technology-
22.3 Implementation-
22.4 Education-
22.5 Applications-
Challenge: Putting System Dynamics Into Action-
Appendix A Numerical Integration-
Challenge: Choosing a Time Step-
Appendix B Noise-
Challenge: Exploring Noise-
REFERENCES-
INDEX.
9780071068123 (paperback) 007106812
658.4038011