Resilient and Intelligent Supply Chains: Advances and Challenges in AI-Driven Optimization and Forecasting

Supply chains are increasingly exposed to compounding disruptions, volatile demand, and sustainability constraints, which challenge optimization approaches designed for stable operating conditions. This review synthesizes recent advances in supply chain optimization with a focus on the integration of artificial intelligence and operations research in decision-making. The paper examines three major capability layers: prescriptive optimization for planning and resource allocation, predictive modeling for demand and risk anticipation, and digitalized execution through simulation and digital twin environments. Across these layers, the analysis shows that hybrid AI-OR architectures tend to outperform isolated methods in settings characterized by high demand volatility, multi-echelon complexity, and disruption exposure, by combining predictive adaptability with constraint-aware decision quality. The review also highlights a strategic shift from single-objective efficiency toward multi-objective performance that jointly manages cost, service, resilience, and environmental impact. From an implementation perspective, the evidence indicates that measurable industrial gains depend less on algorithm novelty alone and more on system-level integration, data governance, and cross-functional deployment. Key research gaps remain in benchmark standardization, explainability, uncertainty-aware optimization, and long-horizon validation under disruption. The paper concludes that the next generation of supply chain optimization will be defined by continuously learning, human-supervised decision ecosystems that remain robust under uncertainty while delivering operational and sustainability outcomes.