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Six ways to improve a simulation analysis

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  • S E Chick

Abstract

Discrete event simulation is a powerful tool that can address many problems that arise in manufacturing, business process design, health care decision-making, and a host of other areas. However, it is not the only tool. Depending on the situation, there may be alternative modelling approaches with which decision makers feel comfortable, and there may be multiple types of simulation models that are appropriate for a given problem. At the same time, the choice of model itself may restrict the applicability of the analysis, or may bias the conclusions that are obtained from using the model. Particularly in interdisciplinary applications, it is important that a simulation modeller find a common language with collaborators, and that all parties be flexible to account for a variety of modelling constructs, perspectives, and ideas. This requires patience, diligence, and a willingness to probe more deeply. This paper provides a personal perspective, based upon over two decades of modelling experience and interactions with simulation professionals, on techniques and mindsets for approaching simulation in order to improve the understanding of simulated systems as well as decisions that are based upon that understanding.

Suggested Citation

  • S E Chick, 2006. "Six ways to improve a simulation analysis," Journal of Simulation, Taylor & Francis Journals, vol. 1(1), pages 21-28, December.
    • Handle: RePEc:taf:tjsmxx:v:1:y:2006:i:1:p:21-28
    DOI: 10.1057/palgrave.jos.4250006
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    Cited by:

    1. Marta O. Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    2. Morgan, Jennifer Sian & Howick, Susan & Belton, Valerie, 2017. "A toolkit of designs for mixing Discrete Event Simulation and System Dynamics," European Journal of Operational Research, Elsevier, vol. 257(3), pages 907-918.
    3. Marta Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    4. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.

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