Artificial Intelligence-Driven Predictive Analytics and Optimization Algorithms for Enhancing End-to-End Visibility and Resilience in Global Supply Chain Networks

Abstract

Global supply chain networks have become increasingly complex due to globalization, fluctuating demand, and unpredictable disruptions. Artificial Intelligence (AI)-driven predictive analytics and optimization algorithms offer real-time insights, risk mitigation, and enhanced resilience in supply chain operations. This paper explores AI methodologies for optimizing supply chain networks, improving visibility, and ensuring robustness against uncertainties. It includes a review of relevant literature, examines AI-based predictive models, optimization techniques, and discusses practical applications through case studies. The study highlights AI's role in enhancing supply chain decision-making, reducing costs, and fostering sustainability.