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The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries

Author

Listed:
  • Samia Zaoui

    (Université de Toulouse)

  • Clovis Foguem

    (Auban Moët Hospital (Groupement Universitaire de Campagne)
    Université de Lille)

  • Dieudonné Tchuente

    (TBS Business School)

  • Samuel Fosso-Wamba

    (TBS Business School)

  • Bernard Kamsu-Foguem

    (Université de Toulouse)

Abstract

The main objective of this research was to examine the instrumental role played by interpretable learning systems, specifically artificial intelligence (AI) technologies, in enhancing supply chain viability and resilience. It seeks to contribute to our understanding of the critical role played by interpretable learning systems in supporting decision-making during emergencies and crises. The research employs an empirical approach to address the research gaps in the application and impact of interpretable learning systems in supply chain management by utilizing the case of COVID-19 vaccine deliveries in France as a descriptive study. The findings highlight the ability to develop a learning system that adeptly predicts vaccine deliveries and vaccination rates. It emphasizes the importance of interpretable learning systems in optimizing supply chain management, navigating the complex landscape of vaccine distribution, establishing effective prioritization strategies, and maximizing the efficient utilization of resources.

Suggested Citation

  • Samia Zaoui & Clovis Foguem & Dieudonné Tchuente & Samuel Fosso-Wamba & Bernard Kamsu-Foguem, 2023. "The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 633-657, December.
    • Handle: RePEc:spr:gjofsm:v:24:y:2023:i:4:d:10.1007_s40171-023-00357-w
    DOI: 10.1007/s40171-023-00357-w
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