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A systematic review of the research trends of machine learning in supply chain management

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Abstract

Research interests in machine learning (ML) and supply chain management (SCM) have yielded an enormous amount of publications during the last two decades. However, in the literature, there was no systematic examination on the research development in the discipline of ML application, in particular in SCM. Therefore, this study was carried out to present the latest research trends in the discipline by analyzing the publications between 1998/01/01 and 2018/12/31 in five major databases. The quantitative analysis of 123 shortlisted articles showed that ML applications in SCM were still in a developmental stage since there were not enough high-yielding authors to form a strong group force in the research of ML applications in SCM and their publications were still at a low level; even though 10 ML algorithms were found to be frequently used in SCM, the use of these algorithms were unevenly distributed across the SCM activities most frequently reported in the articles of the literature. The aim of this study is to provide a comprehensive view of ML applications in SCM, working as a reference for future research directions for SCM researchers and application insight for SCM practitioners.

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  1. School of Economics and Business Administration, Chongqing University, Chongqing, 400044, China

    Du Ni & Zhi Xiao

  2. College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Ming K. Lim

  3. Centre for Business in Society, Coventry University, Coventry, CV1 5FB, UK

    Ming K. Lim

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  1. Du Ni
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Ni, D., Xiao, Z. & Lim, M.K. A systematic review of the research trends of machine learning in supply chain management. Int. J. Mach. Learn. & Cyber. 11, 1463–1482 (2020). https://doi.org/10.1007/s13042-019-01050-0

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