Explainable Artificial Intelligence in Supply Chain Risk Management: A Causal Inference Model for Supplier Dependency Relationships - Yang, Ruoqian; Wang, Longmei; Peng, Wei; Huang, Xinmou; Wang, Tingting (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
Traditional supply chains face significant challenges due to suppliers with concealed and uncontrolled relationships. This causes unexpected disruptions and delays, leading to financial losses. Current risk management approaches cannot see evolving relationships, track causes, or provide real-time insights. To address this, an explainable and adaptable risk prediction framework has been proposed, combining Temporal Graph Neural Networks (TGNN), Neural Causal Discovery using the Peter-Clark (PC) algorithm, and an Adaptive Hawk-Moth Optimization (AHMO) method. The framework constructs supply chain graphs that evolve while utilizing Multi-Layer Perceptron (MLP) technology with skip connections to identify supplier relationships and assess risk. Causal SHAP enhances the model with additional explanation capabilities by offering understandable risk score explanations. Results demonstrate that the model achieves strong real-world performance through its power and flexibility, evidenced by experimental results with an MAE of 0.0112, MSE of 0.0019, RMSE of 0.0439, MSLE of 0.0046, and RMSLE of 0.0141.