Enhancing Humanitarian Supply Chain Resilience: Evaluating Artificial Intelligence and Big Data Analytics in Two Nations

Emmanuel Ahatsi () and Oludolapo Akanni Olanrewaju
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Emmanuel Ahatsi: Department of Industrial Engineering, Durban University of Technology, Durban 4000, South Africa
Oludolapo Akanni Olanrewaju: Department of Industrial Engineering, Durban University of Technology, Durban 4000, South Africa

Logistics, 2025, vol. 9, issue 2, 1-28

Abstract: Background: This study examines the application of Artificial Intelligence (AI) and Big Data Analytics (BDA) in enhancing humanitarian supply chain resilience, focusing on Ghana and South Africa. Despite their potential, AI-BDA applications are underexplored in disaster response, particularly in developing economies. Methods: An explanatory research design using a quantitative approach was employed, analyzing data from 200 supply chain professionals in both nations. Structured questionnaires assessed the implementation of four key AI-BDA techniques: Time-Series Forecasting (TSF), Early Warning Systems (EWS), Logistics Optimization (LO), and Real-time Monitoring (RTM). Exploratory factor analysis and regression analysis were conducted to evaluate the relationship between these techniques and supply chain resilience, controlling for organizational size and technological readiness. Results: The findings indicate that AI-BDA techniques significantly improve humanitarian supply chain resilience, with TSF and LO demonstrating the highest predictive power. Additionally, technological readiness facilitates the adoption of these techniques. Conclusions: While AI-BDA offers substantial benefits, opportunities for greater adoption remain, particularly in real-time monitoring and predictive analytics. Humanitarian organizations should invest in capacity-building initiatives, enhance data quality, and foster multi-stakeholder partnerships to maximize the impact of AI-BDA.

Keywords: artificial intelligence (AI); big data analytics (BDA); humanitarian supply chain resilience (HSCR); Ghana; South Africa (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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