Discrete-Event Simulation and System Dynamics for Management Decision Making

9Explaining Puzzling Dynamics: A Comparison of System Dynamics and Discrete-Event Simulation

John Morecroft¹ and Stewart Robinson²

¹London Business School, London, UK

²School of Business and Economics, Loughborough University, UK

9.1 Introduction

Everyday situations present many examples of puzzling dynamics – performance over time that defies intuition and common sense. You drive for miles at a fast and steady speed on a busy motorway yet sometimes encounter unexpected tailbacks with no apparent cause. You occasionally visit your hairdresser. These visits are on the same day of the week at the same time but you never know in advance whether you will wait one minute or half an hour for a haircut.

One way to investigate such puzzling dynamics is to build a computer simulation model that represents the various interrelated factors and pressures at work in the situation, and then run the model to see whether or not it is capable of generating similar puzzling performance. If a model can, in some meaningful way, mimic observed performance then modellers claim they have an explanation for the phenomenon. In developing a simulation model to investigate puzzling dynamics, the analyst can select from a number of approaches, among which two of the most common are discrete-event simulation (DES) (Banks et al., 2001; Pidd, 2004; Robinson, 2004; Law, 2007) and system dynamics (SD) (Forrester, 1961; Richardson and Pugh, 1981; Coyle, 1996; Sterman, 2000; Morecroft, 2007). Both are widely ...