INTELLIGENT SUPPLY CHAIN RISK MITIGATION: A MACHINE LEARNING APPROACH TO NEWS-BASED DISRUPTION FORECASTING

Abstract

Modern supply chains are increasingly complex and vulnerable to a wide array of disruptions, from natural disasters to geopolitical events. This article presents an innovative approach to supply chain risk management through the development of an AI-powered early warning system. By leveraging natural language processing, machine learning, and predictive analytics, our system continuously monitors global news sources, social media, and other relevant data streams to identify potential supply chain disruptions before they occur. The proposed framework integrates real-time data ingestion, sophisticated risk assessment algorithms, and decision support tools to provide supply chain managers with actionable insights for proactive risk mitigation. We demonstrate the system's efficacy through case studies in the automotive and agricultural sectors, showing significant improvements in supply chain resilience and cost savings compared to traditional risk management approaches. Our findings suggest that AI-driven predictive analytics can revolutionize supply chain risk management, enabling organizations to anticipate and mitigate disruptions more effectively in an increasingly uncertain global business environment.