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

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Tirkolaee, EB, Sadeghi, S, Mooseloo, FM, Vandchali, HR ORCID: 0000-0003-4109-3314 and Aeini, S 2021 , 'Application of machine learning in supply chain management: a comprehensive overview of the main areas' , Mathematical Problems in Engineering, vol. 2021 , pp. 1-14 , doi: 10.1155/2021/1476043.

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Abstract

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about toomuch data for supply chain management (SCM). (e volume of data generated from all parts of the supply chain has changed thenature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods havedecreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate somemethods that have high capability to analyze and interpret big data. (erefore, the main purpose of this paper is to identify theapplications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developinga conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predictingsupply 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 andchallenges are discussed, and then managerial insights and future research directions are given.

Item Type: Article
Authors/Creators:Tirkolaee, EB and Sadeghi, S and Mooseloo, FM and Vandchali, HR and Aeini, S
Keywords: machine learning, supply chain management
Journal or Publication Title: Mathematical Problems in Engineering
Publisher: Hindawi Limited
ISSN: 1024-123X
DOI / ID Number: 10.1155/2021/1476043
Copyright Information:

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.

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