Publication | Closed Access
Improving the rigor of discrete‐event simulation in logistics and supply chain research
115
Citations
29
References
2009
Year
Logistics ProcessesSupply Chain OptimizationEngineeringLogistics OptimizationSupply Chain RiskSupply Chain StudiesDiscrete-event SimulationOperations ResearchSimulation MethodologyDiscrete‐event SimulationManagementSystems EngineeringSupply ChainLogisticsModeling And SimulationLogistics ModelSupply Chain ViabilitySystem SimulationDiscrete Event SimulationPhysical DistributionSupply Chain ResearchSupply Chain DesignSupply Chain ManagementOperations ManagementSimulation Model DevelopmentSupply ManagementBusinessSupply Chain AnalysisSupply Chain Configuration
This paper introduces an eight‑step simulation model development process (SMDP) for logistics and supply‑chain simulation, defines rigor criteria for each step, and offers a framework with examples to guide researchers and reviewers toward higher‑quality simulation studies. The authors conducted a comprehensive literature review of discrete‑event simulation studies in logistics and supply‑chain contexts, selecting those that detail multi‑echelon model development steps to illustrate rigor, and used these to construct the eight‑step SMDP framework. The review shows that current logistics and supply‑chain simulation studies lack standardized rigor criteria, prompting the authors to propose the SMDP checklist to improve model validity and help practitioners assess the trustworthiness of published results.
Purpose The purpose of this paper is to present an eight‐step simulation model development process (SMDP) for the design, implementation, and evaluation of logistics and supply chain simulation models, and to identify rigor criteria for each step. Design/methodology/approach An extensive review of literature is undertaken to identify logistics and supply chain studies that employ discrete‐event simulation modeling. From this pool, studies that report in detail on the steps taken during the simulation model development and model more than one echelon in logistics, supply chain, or distribution systems are included to illustrate rigor in developing such simulation models. Findings Literature review reveals that there are no preset rigor criteria for publication of logistics and supply chain simulation research, which is reflected in the fact that studies published in leading journals do not satisfactorily address and/or report the efforts taken to maintain the rigor of simulation studies. Although there has been a gradual improvement in rigor, more emphasis on the methodology required to ensure quality simulation research is warranted. Research limitations/implications The SMDP may be used by researchers to design and execute rigorous simulation research, and by reviewers for academic journals to establish the level of rigor when reviewing simulation research. It is expected that such prescriptive guidance will stimulate high quality simulation modeling research and ensure that only the highest quality studies are published. Practical implications The SMDP provides a checklist for assessment of the validity of simulation models prior to their use in practical decision making. It assists in making practitioners better informed about rigorous simulation design so that, when answering logistics and supply chain system questions, the practitioner can decide to what extent they should trust the results of published research. Originality/value This paper develops a framework based on some of the most rigorous studies published in leading journals, provides rigor evaluation criteria for each step, provides examples for each step from published studies, and illustrates the SMDP using a supply‐chain risk management study.
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