AI-Driven Supply Chain Resilience: Predicting and Mitigating Disruptions Through Machine Learning

Abstract

Supply chain disruption management requires both anticipation and operational responses under
uncertainty. This paper develops an integrated machine learning and prescriptive optimization
framework that combines multimodal risk signals, calibrated disruption probabilities, severity
forecasting, scenario simulation and cost-aware mitigation. The proposed architecture ingests
supplier reliability, lead-time variability, demand volatility, inventory coverage, transportation
congestion and external hazard indicators, then translates predictions into actionable decisions
about safety stock, supplier diversification, expediting and capacity reservation. To illustrate
evaluation procedures without misrepresenting experimental evidence, a fully disclosed synthetic
scenario is constructed and numerical results are explicitly labeled illustrative rather than
empirical. In this scenario, an integrated policy lowers simulated expected disruption cost
relative to a reactive baseline while requiring additional preventive expenditure. The manuscript
discusses calibration, temporal validation, explainability, fairness, privacy, data drift and
implementation constraints. It offers reproducible evaluation logic and a research agenda for
testing the framework on real multi-echelon supply chain data.