Conceptual Advances in Predictive Intelligence Models for Humanitarian and Disaster Response Supply Chain Resilience

Abstract

Humanitarian and disaster response supply chains operate under extreme uncertainty, time pressure, and resource constraints, where delays or misallocations directly translate into human suffering and loss of life. In recent years, predictive intelligence models have emerged as critical enablers for enhancing supply chain resilience by improving anticipatory decision-making, situational awareness, and adaptive coordination across complex humanitarian networks. This review examines conceptual advances in predictive intelligence models applied to humanitarian and disaster response supply chains, with emphasis on their theoretical foundations, methodological evolution, and resilience-oriented capabilities. The paper synthesizes developments across data-driven forecasting, probabilistic risk modeling, machine learning, and hybrid human–AI decision frameworks, highlighting how these approaches support demand anticipation, disruption prediction, inventory pre-positioning, and logistics network reconfiguration. Particular attention is given to the integration of real-time data streams from remote sensing, social media, Internet of Things devices, and institutional reporting systems, as well as the role of explainability and trust in high-stakes humanitarian contexts. The review also discusses persistent challenges, including data sparsity, ethical constraints, model transferability across disaster types and regions, and governance issues related to inter-agency coordination. By organizing the literature around resilience dimensions—robustness, adaptability, and recoverability—the paper offers a unifying conceptual lens for evaluating predictive intelligence models beyond pure accuracy metrics. The study concludes by identifying research gaps and proposing future directions, including human-centered predictive systems, federated and privacy-preserving learning, and policy-aligned intelligence architectures. Overall, the review provides a structured foundation for researchers, practitioners, and policymakers seeking to leverage predictive intelligence to strengthen humanitarian supply chain resilience in increasingly volatile disaster environments.