Supply chain risk management with machine learning technology: a literature review and future research directions

Yang, M., Lim, M. K. , Qu, Y., Ni, D. and Xia0, Z. (2023) Supply chain risk management with machine learning technology: a literature review and future research directions. Computers and Industrial Engineering, 175, 108859. (doi: 10.1016/j.cie.2022.108859) (PMCID:PMC9715461)

Abstract

Coronavirus disease 2019 (COVID-19) has placed tremendous pressure on supply chain risk management (SCRM) worldwide. Recent technological advances, especially machine learning (ML) technology, have shown the possibility to prevent supply chain risk (SCR) by decreasing the need for human labor, increasing response speed, and predicting risk. However, the literature lacks a comprehensive analysis of the relationship between ML and SCRM. This work conducts a comprehensive review of the relatively limited literature in this field. An analysis of 67 shortlisted articles from 9 databases shows that this area is still in the rapid development stage and that researchers have shown extraordinary interest in it. The main purpose of this study is to review the current research status so that researchers have a clear understanding of the research gaps in this area. Moreover, this study provides an opportunity for researchers and practitioners to pay attention to ML algorithms for SCRM during the COVID-19 pandemic.

• Item Type: Articles
• Additional Information: This work was supported by the National Natural Science Foundation of China [grant No. 72071021, 71671019], the Graduate Research and Innovation Foundation of Chongqing, China [grant No. CYS21047], and the Chongqing Social Science Planning Program [grant No.2022NDQN44].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Yang, M., Lim, M. K., Qu, Y., Ni, D., and Xia0, Z.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Computers and Industrial Engineering
• Publisher: Elsevier
• ISSN: 0360-8352
• ISSN (Online): 1879-0550
• Published Online: 02 December 2022
• Copyright Holders: Copyright © 2022 The Author(s)
• First Published: First published in Computers and Industrial Engineering 175: 108859
• Publisher Policy: Reproduced under a Creative Commons License