AI-powered predictive analytics for enhancing supply chain resilience and delivery outcomes

This study investigates whether AI-powered predictive analytics can support exploratory delivery disruption detection, operationalizing supply chain resilience as an observable delivery performance outcome rather than a latent organizational capability. Random Forest and XGBoost classification models were developed and evaluated using a publicly available secondary logistics dataset hosted on Kaggle (N = 12,144), whose original organizational provenance and collection procedures could not be independently verified. Accordingly, findings are interpreted as exploratory and dataset-specific. Random Forest achieved 63.5% accuracy and XGBoost 62.0% on the held-out test set, compared with a 57.9% majority-class baseline, indicating modest predictive improvement over trivial classification. Both models performed substantially better on delayed deliveries than on on-time deliveries, suggesting limited three-class discriminative capability. Because delivery duration was derived from the same temporal fields underlying the outcome label, its predictive role is interpreted cautiously due to potential target-adjacent information. Regional comparisons are therefore presented descriptively through separate delivery indicators rather than a weighted composite resilience score. The study contributes a cautious predictive benchmark for exploratory delay-risk screening and identifies methodological priorities for future work using validated logistics datasets and leakage-resistant feature designs.