Predicting supply chain risks using machine learning: The trade-off between performance and interpretability

This work proposes a supply chain risk prediction framework using data-driven AI techniques and relying on the synergy between AI and supply chain experts and explores the trade-off between prediction performance and interpretability by implementing and applying the framework on the case of predicting delivery delays in a real-world multi-tier manufacturing supply chain.