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.
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.
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.
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.
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.
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.
