Data-driven imitation learning-based approach for order size determination in supply chains

Dony S. Kurian, V. Madhusudanan Pillai, J. Gautham and Akash Raut

European Journal of Industrial Engineering, 2023, vol. 17, issue 3, 379-407

Abstract: Past studies have attempted to formulate the order decision-making behaviour of humans for inventory replenishment in dynamic stock management environments. This paper investigates whether a data-driven approach like machine learning can imitate the order size decisions of humans and consequently enhance supply chain performances. Accordingly, this paper proposes a supervised machine learning-based order size determination approach. The proposed approach is initially executed using the order decision data collected from a simulated stock management environment similar to the 'beer game'. Subsequent comparative analysis shows that the proposed approach successfully enhances all supply chain performance measures compared to other well-known ordering methods. Additionally, the proposed approach is validated on a retail case study to investigate its efficacy. This paper thus focuses on extending the past works reported in the literature by modelling human order decision-making as data-driven imitation learning and contributing to machine learning applications for order management. [Submitted: 19 August 2021; Accepted: 16 February 2022]

Keywords: supply chain; order size determination; machine learning; behavioural experiments; LightGBM; imitation learning; beer game. (search for similar items in EconPapers)
Date: 2023
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