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Application of machine learning in supply chain management: a comprehensive overview of the main areas

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posted on 2023-05-21, 01:21 authored by Tirkolaee, EB, Sadeghi, S, Mooseloo, FM, Hadi Rezaei VandchaliHadi Rezaei Vandchali, Aeini, S
In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). (e volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. (erefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

History

Publication title

Mathematical Problems in Engineering

Volume

2021

Article number

1476043

Number

1476043

Pagination

1-14

ISSN

1024-123X

Department/School

Australian Maritime College

Publisher

Hindawi Limited

Place of publication

United Kingdom

Rights statement

Copyright © 2021 Erfan Babaee Tirkolaee et al. This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Repository Status

  • Open

Socio-economic Objectives

Logistics; Technological and organisational innovation

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