Olanrewaju, Oludolapo AkanniAhatsi, Emmanuel2026-09-152026-09-152024https://hdl.handle.net/10321/6479Submitted in fulfilment of the requirements for the degree of Doctor of Engineering: Industrial Engineering, Durban University of Technology, Durban, South Africa, 2024.While there is great promise in AI-BDA applications revolutionising humanitarian operations through predictive analytics and resource optimisation, they are underexplored in disaster response contexts, especially in developing economies. The aim of this research was to evaluate current AI-BDA techniques and their effect on supply chain resilience in humanitarian settings focusing on Ghana and South Africa. The study employed an explanatory research design with a quantitative approach, analysing data from purposively sampled 200 supply chain professionals in Ghana and South Africa. Structured questionnaires measuring the implementation of four key AI-BDA techniques: Time-Series Forecasting (TSF), Early Warning Systems (EWS), Logistics Optimization (LO) and Real-time Monitoring (RTM) were used for data collection. Exploratory factor analysis and regression analysis were performed to analyse the relationship between AI-BDA techniques and supply chain resilience, controlling for organisational size and technological readiness. The results of the findings show that the AI-BDA techniques have significant effects on humanitarian supply chain’s resilience with TSF and LO having the highest predictive power with technology readiness and organisational size facilitating the adoption of AI-BDA. Moreover, the findings revealed that resource-related barriers, particularly skill gaps among staff and lack of technical expertise, represent the most significant challenges to AI-BDA adoption. The study recommends implementing a holistic AI-BDA approach that aligns with humanitarian principles. This involves a multi-faceted strategy that not only emphasizes the ethical use of AI-BDA but also prioritizes personnel capacity building through tailored training programs. These programs should focus on enhancing technical skills, such as data analysis, machine learning algorithms, and ethical considerations in data usage. By integrating these elements, organizations can ensure that AIBDA tools are utilized responsibly and effectively, ultimately leading to improved outcomes in humanitarian efforts while maintaining public trust and safeguarding individual rights.176 penHumanitarian logisticsSupply chain managementHumanitarian assistanceBusiness logisticsArtificial intelligenceBig dataThesishttps://doi.org/10.51415/10321/6479