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

Authors: Dony S. Kurian; V. Madhusudanan Pillai; J. Gautham; Akash Raut

Addresses: Department of Mechanical Engineering, National Institute of Technology Calicut, NIT Campus, Calicut – 673601, Kerala, India ' Department of Mechanical Engineering, National Institute of Technology Calicut, NIT Campus, Calicut – 673601, Kerala, India ' Department of Mechanical Engineering, National Institute of Technology Calicut, NIT Campus, Calicut – 673601, Kerala, India ' Department of Electrical and Electronics Engineering, National Institute of Technology Calicut, NIT Campus, Calicut – 673601, Kerala, India

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.

DOI: 10.1504/EJIE.2023.130601

European Journal of Industrial Engineering, 2023 Vol.17 No.3, pp.379 - 407

Received: 19 Aug 2021
Accepted: 16 Feb 2022
Published online: 01 May 2023 *