Unfolding AI’s strategic role in humanitarian supply chains: A fuzzy cognitive model aligned with SCOR-oriented performance and policy roadmapping

Humanitarian Supply Chains (HSCs) face numerous challenges, including uncertainty, high dynamics, and resource constraints. The ongoing development of Artificial Intelligence (AI) offers a promising avenue to transform SC processes and enhance HSC performance. This study examines the impact of Artificial Intelligence Facilitators (AIFs) on key performance metrics (PMs) within the Supply Chain Operations Reference (SCOR) model in HSCs. Using Fuzzy Cognitive Maps (FCMs), we model the causal relationships between 13 AIFs and five SCOR indicators, incorporating expert knowledge under conditions of vagueness and hesitation. The Net Influence analysis identified predictive analytics (AIF1) as having the highest causal impact on aid management (SCOR5), with a score of 0.739. Other significant influences included population monitoring (AIF9) on reliability (SCOR1) at 0.724, and drones (AIF10) on aid management (SCOR5) at 0.706. Moderate impacts were observed from logistics optimization (AIF5) on agility (SCOR3) at 0.702, and resource allocation (AIF6) on accountability (SCOR2) and costs (SCOR4) at 0.682 and 0.663, respectively. To assess model robustness, five sensitivity scenarios were simulated using the Active Hebbian Learning (AHL) algorithm. A 10 % increase in causal strength (Scenario 1) resulted in notable improvements in agility and aid efficiency, while a 30 % increase in hesitation (Scenario 4) revealed vulnerabilities in population monitoring and needs assessment due to rising uncertainty. A key contribution of this research is developing a strategic roadmap that visually integrates high-impact AI enablers with SCOR PMs across three hierarchical levels, providing policymakers a data-driven framework for prioritizing AI implementation based on influence assessments and scenario insights.