Supply chain disruptions pose escalating threats to global business operations, necessitating advanced predictive capabilities beyond traditional reactive risk management approaches. This research develops and empirically validates an artificial intelligence-driven early warning system that leverages ensemble machine learning algorithms for real-time supply chain risk detection. The proposed framework integrates multi-source data streams encompassing internal operations, financial metrics, and external environmental factors through a hierarchical risk indicator system weighted at 50%, 30%, and 20% respectively. The methodology employs five machine learning algorithms—Random Forest, XGBoost, Long Short-Term Memory networks, Support Vector Machines, and Neural Networks—within an ensemble architecture to process heterogeneous data inputs. Empirical validation utilized a comprehensive dataset of 850,000 records spanning 36 months across manufacturing, retail, and technology sectors, capturing 450 documented risk events from multiple supply chain networks. XGBoost demonstrated superior individual performance achieving 92% accuracy, 94% area under the receiver operating characteristic curve, and 89% F1-score, while the ensemble approach enhanced predictive accuracy by 15% compared to single-algorithm implementations. Real-world deployment across three manufacturing facilities and two distribution centers validated the system's operational effectiveness, demonstrating 89% accuracy in predicting high-impact disruptions with 2-4 week advance warning periods. The framework achieved substantial business impact including 35% reduction in risk-related losses, 28% decrease in supply chain disruption frequency, and 40% improvement in response times, while maintaining an acceptable 8% false positive rate and 99.7% system availability. Sensitivity analysis confirmed robust performance under crisis conditions with 80-84% accuracy retention during simulated financial crises, natural disasters, and geopolitical conflicts. This research contributes a scalable, interpretable framework that bridges theoretical risk management concepts with practical AI implementation, providing organizations with actionable intelligence for transitioning from reactive to predictive supply chain risk management paradigms.

Sichong Huang. AI-Driven Early Warning Systems for Supply Chain Risk Detection: A Machine Learning Approach. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 9: 92-107. https://doi.org/10.25236/AJCIS.2025.080913.

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