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

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Date

2021

Author

Tirkolaee, Erfan Babaee
Sadeghi, Saeid
Mooseloo, Farzaneh Mansoori
Vandchali, Hadi Rezaei
Aeini, Samira

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Citation

Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas. Mathematical Problems in Engineering, 2021.

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In today's complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM).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

Source

MATHEMATICAL PROBLEMS IN ENGINEERING

Volume

2021

URI

https://doi.org/10.1155/2021/1476043
https://hdl.handle.net/20.500.12713/1958

Collections

  • Makale Koleksiyonu [46]
  • Scopus İndeksli Yayınlar Koleksiyonu [1427]
  • WoS İndeksli Yayınlar Koleksiyonu [1485]



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