Anticipating supply chain disruptions with graph AI models

Supply chain networks are increasingly complex and interconnected, making them vulnerable to disruptions caused by natural disasters, geopolitical tensions, cyberattacks, and market volatility. Traditional forecasting and risk management techniques often fall short in dynamically capturing the multi-relational and non-linear dependencies within these networks. This paper explores the role of Graph AI models—particularly Graph Neural Networks (GNNs)—in modeling, predicting, and mitigating supply chain disruptions. We propose a framework for integrating Graph AI into supply chain operations, emphasizing the significance of topological insights, data heterogeneity, real-time analytics, and adaptive learning. By referencing recent advances and empirical findings, we outline a path for deploying Graph AI as a strategic asset in resilient and intelligent supply chain management.