Learning-Driven Predictive Orchestration for Cost-Efficient Global Supply Networks

Abstract

This paper synthesizes recent advances in artificial intelligence and machine learning for predictive analytics that target efficiency and economic performance in global supply net-works. Using a systematic evidence review across peer-reviewed and industry sources (2014–2024), the analysis maps algorith-mic levers—neural sequence models for demand, reinforcement policies for inventory, metaheuristics for routing, and anomaly-informed risk scoring—to operational outcomes including lower holding and transport costs, reduced stockouts, and shorter cycle times. The review identifies integration frictions (data gover-nance, legacy interoperability, bias) and governance enablers (model monitoring, cyber controls, explainability) that condi-tion realized benefits, and consolidates implementation patterns such as edge-enabled visibility, AI-blockchain traceability, and predictive maintenance tie-ins to logistics assets. The resulting framework links model class and data regime to cost/efficiency objectives and disruption exposure, offering decision guidance for staging AI/ML deployment to maximize economic value while preserving resilience at network scale.