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Machine Learning and Supply Chain Management

In: The Palgrave Handbook of Supply Chain Management

Author

Listed:
  • Matthew Quayson

    (University of Electronic Science and Technology of China
    Ho Technical University)

  • Chunguang Bai

    (University of Electronic Science and Technology of China)

  • Derrick Effah

    (University of Electronic Science and Technology of China)

  • Kwame Simpe Ofori

    (International University of Grand-Bassam)

Abstract

Scholars have turned to highly capable machine learning (ML) approaches for analyzing and interpreting huge amounts of data due to the limitations of older methodologies. There has been a recent uptick in using machine learning algorithms in supply chain management (SCM). This chapter uses some literature and a bibliometric analysis to provide an overview of the field. Overall, ML is applied for supplier management, risk management, transport and distribution, and the circular economy. Some of the areas of study we review, based on a bibliometric analysis, include frameworks, performance management, and artificial intelligence (AI) challenges for supply chain management. Conversely, issues rarely discussed include the selection of ML techniques for supply chain management (SCM), sustainability issues, the future of ML in supply chain management, and system requirements for ML in supply chain management. Based on these issues, we provide insights for managers, interesting research areas for future research directions for SCM researchers, and application insight for SCM practitioners.

Suggested Citation

  • Matthew Quayson & Chunguang Bai & Derrick Effah & Kwame Simpe Ofori, 2024. "Machine Learning and Supply Chain Management," Springer Books, in: Joseph Sarkis (ed.), The Palgrave Handbook of Supply Chain Management, pages 1327-1355, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-19884-7_92
    DOI: 10.1007/978-3-031-19884-7_92
    as

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