TY - JOUR
AU - Yang, Mei
AU - Lim, Ming K.
AU - Qu, Yingchi
AU - Ni, Du
AU - Xiao, Zhi
PY - 2022
DA - December
TI - Supply chain risk management with machine learning technology: a literature review and future research directions
JO - Computers & Industrial Engineering
VL - 175
DO - http://dx.doi.org/10.1016/j.cie.2022.108859
AB - 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.
PB - Elsevier
UR - https://eprints.gla.ac.uk/286119/
KW - Coronavirus (COVID-19)
ER