Integrating Artificial Intelligence and Advanced Computing Models to Reduce Logistics Delays in Pharmaceutical Distribution

Abstract

Logistics delays in pharmaceutical distribution pose significant operational and public health risks because time-sensitive products are subject to strict service-level, quality, and regulatory constraints. This study quantitatively examined the determinants of logistics delay using shipment-level operational data from a regulated pharmaceutical distribution network. The analytical dataset comprised 12,480 shipments observed over a 12-month period, covering 14 carriers, 8 warehouses, and 5 hub facilities across domestic and cross-border lanes. Delay was operationalized both as a continuous outcome, measured in hours of deviation from planned delivery milestones, and as a late-delivery event based on service-level agreement thresholds. Descriptive analysis showed that while 80.3% of shipments were delivered on time, 19.7% exceeded lateness thresholds, with 18.1% experiencing delays greater than eight hours. Cold-chain shipments represented 26.0% of the sample and accounted for 29.3% of severe delay cases, indicating disproportionate risk concentration. Inferential results from baseline regression models demonstrated that facility congestion was the strongest predictor of delay duration (β = 1.42, p < 0.001), followed by route complexity (β = 0.96, p < 0.001) and cold-chain risk (β = 1.08, p < 0.001). Carrier reliability exhibited a statistically significant protective effect, reducing delay magnitude (β = −1.15, p < 0.001). Classification-oriented models for late-delivery outcomes showed that nonlinear machine learning approaches improved prioritization performance relative to logistic regression. Gradient boosting achieved an area under the curve of 0.82 and captured 56.9% of late deliveries within the top 10% highest-risk predictions, compared with 38.6% for the baseline model. Under facility- and lane-based holdout validation, ranking performance declined moderately but remained operationally meaningful, with top-decile capture rates above 46%. Overall, the findings indicated that logistics delay in pharmaceutical distribution was driven primarily by internal facility dwell dynamics, interaction-heavy risk configurations, and compliance-related handling constraints. The study demonstrated that decision-utility–oriented evaluation and leakage-aware validation were essential for credible quantitative evidence in regulated logistics networks.