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

• Published in: Global journal of flexible systems management Vol. 24; no. 4; pp. 633 - 657
• Main Authors: Zaoui, Samia, Foguem, Clovis, Tchuente, Dieudonné, Fosso-Wamba, Samuel, Kamsu-Foguem, Bernard
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01-12-2023
• Subjects: Business and Management; Original Research; COVID-19; Interpretability; Machine learning; Risk prediction; Flexible supply chain; Artificial intelligence; Viability; Resilience
• ISSN: 0972-2696; 0974-0198
• DOI: 10.1007/s40171-023-00357-w

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.