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

Citation

Tirkolaee, EB and Sadeghi, S and Mooseloo, FM and Vandchali, HR and Aeini, S, Application of machine learning in supply chain management: a comprehensive overview of the main areas, Mathematical Problems in Engineering, 2021 Article 1476043. ISSN 1024-123X (2021) [Refereed Article]


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Copyright 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.

DOI: doi:10.1155/2021/1476043

Abstract

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM).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.

Item Details

Item Type:Refereed Article
Keywords:machine learning, supply chain management
Research Division:Commerce, Management, Tourism and Services
Research Group:Transportation, logistics and supply chains
Research Field:Supply chains
Objective Division:Defence
Objective Group:Defence
Objective Field:Logistics
UTAS Author:Vandchali, HR (Dr Hadi Rezaei Vandchali)
ID Code:145739
Year Published:2021
Web of Science® Times Cited:6
Deposited By:Maritime and Logistics Management
Deposited On:2021-08-04
Last Modified:2021-09-24
Downloads:6 View Download Statistics

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