Machine Learning-Driven Predictive Models for Proactive Supply Chain Risk Management in the Face of Global Disruptions

Global supply chains have grown increasingly complex and interconnected, thus becoming more vulnerable to risks-natural disasters, geopolitical instability, and market cycles. Active risk management is the key to getting outages before they happen. The paper studies the use of machine learning (ML) to predict and control supply chain risks-specifically the use of predictive models for early disruption detection. Machine learning models can forecast and measure risk based on different types of data, both internal (i.e., inventory, shipping dates) and external (i.e., international trade, political climate, and other data) to enable businesses to take preventative measures. This paper also discusses the limitations and benefits of applying ML-based risk management systems in supply chains including data quality, model performance and real-time decisioning. The paper also identifies key performance metrics for comparison like prediction accuracy, detection speed and ROI, while emphasizing the cross-functional cooperation between data scientists and supply chain managers. According to the results, applying predictive models to supply chain processes significantly increases resilience allowing businesses to respond quickly and effectively to outages. This research adds a great deal to the debate regarding how machine learning can change the ways in which we manage risk and boost supply chain effectiveness.