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

Zichong Lyu, Dirk Pons, Yilei Zhang and Zuzhen Ji
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Zichong Lyu: Department of Mechanical Engineering, University of Canterbury, Kirkwood Ave, Christchurch 8140, New Zealand
Dirk Pons: Department of Mechanical Engineering, University of Canterbury, Kirkwood Ave, Christchurch 8140, New Zealand
Yilei Zhang: Department of Mechanical Engineering, University of Canterbury, Kirkwood Ave, Christchurch 8140, New Zealand
Zuzhen Ji: Department of Chemical and Biological Engineering, Zhejiang University, Zheda Ave, Hangzhou 310027, China

Logistics, 2022, vol. 6, issue 2, 1-28

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.

Keywords: simulation conceptual modelling; discrete-event simulation; communication and collaboration; agile method; tacit knowledge; freight logistics (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:6:y:2022:i:2:p:37-:d:835998

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