AI-Powered Predictive Analytics for Supply Chain Resilience: Mitigating Disruptions and Enhancing Delivery Outcomes

The main objective of this study was to investigate how integrating AI-powered predictive analytics into supply chain management could help improve resilience and delivery performance. We developed and evaluated Random Forest and XGBoost models using a comprehensive logistics dataset (N = 15,549) to predict delivery disruptions. Shipping mode was the most important in explaining the delivery outcomes (MI = 0.179), followed by delivery duration (MI = 0.015). Analysis by region showed consistent delay frequencies (57-59%) by markets, but delivery durations differed significantly between developed and emerging areas. On the Random Forest model, we obtained 61.2% (±1.3%) for delivery outcomes prediction, slightly better than XGBoost (59.8% ±0.9%). We present empirical evidence to support the efficacy of AI in supply chain optimization and provide actionable frameworks for using predictive analytics in supply chain operations. This work helps to strengthen the theory and practice of AI-guided solutions in modern supply chain management.