Minimum Viable Model (MVM) Methodology for Integration of Agile Methods into Operational Simulation of Logistics

Abstract

Background: Logistics problems involve a large number of complexities, which makes the development of models challenging. While computer simulation models are developed for addressing complexities, it is essential to ensure that the necessary operational behaviours are captured, and that the architecture of the model is suitable to represent them. The early stage of simulation modelling, known as conceptual modelling (CM), is thus dependent on successfully extracting tacit operational knowledge and avoiding misunderstanding between the client (customer of the model) and simulation analyst. Objective: This paper developed a methodology for managing the knowledge-acquisition process needed to create a sufficient simulation model at the early or the CM stage to ensure the correctness of operation representation. Methods: A minimum viable model (MVM) methodology was proposed with five principles relevant to CM: iterative development, embedded communication, soliciting tacit knowledge, interactive face validity, and a sufficient model. The method was validated by a case study of freight operations, and the results were encouraging. Conclusions: The MVM method improved the architecture of the simulation model through eliciting tacit knowledge and clearing up communication misunderstandings. It also helped shape the architecture of the model towards the features most appreciated by the client, and features not needed in the model. Originality: The novel contribution of this work is the presentation of a method for eliciting tacit information from industrial clients, and building a minimally sufficient simulation model at the early modelling stage. The framework is demonstrated for logistics operations, though the principles may benefit simulation practitioners more generally.

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