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Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Author

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

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In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM).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

Suggested Citation

  • Erfan Babaee Tirkolaee & Saeid Sadeghi & Farzaneh Mansoori Mooseloo & Hadi Rezaei Vandchali & Samira Aeini, 2021. "Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, June.
    • Handle: RePEc:hin:jnlmpe:1476043
    DOI: 10.1155/2021/1476043
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    Cited by:

    1. Morteza Noruzi & Ali Naderan & Jabbar Ali Zakeri & Kamran Rahimov, 2023. "A Robust Optimization Model for Multi-Period Railway Network Design Problem Considering Economic Aspects and Environmental Impact," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    2. Garg, Vipul & Gabaldon, Janeth & Niranjan, Suman & Hawkins, Timothy G., 2025. "Impact of strategic performance measures on performance: The role of artificial intelligence and machine learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).
    3. Osheyor Joachim Gidiagba & Lagouge Tartibu & Modestus Okwu, 2025. "Integrating Machine Learning with Multi-Criteria Decision-Making Models for Sustainable Supplier Selection in Dynamic Supply Chains," Logistics, MDPI, vol. 9(4), pages 1-22, October.
    4. Juan Camilo Gutierrez & Sonia Isabel Polo Triana & Juan Sebastian León Becerra, 2025. "Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review," OPSEARCH, Springer;Operational Research Society of India, vol. 62(3), pages 1140-1172, September.

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