Enabling a data-driven assessment of operational supply risks in automotive supply chains

The automotive industry is facing increasing challenges due to shifting market demands and technological changes. These developments put pressure on suppliers to ensure consistent supply security and make the supply chain more vulnerable to operational risks. This paper proposes a data-driven framework to assess these risks by leveraging data exchange between an OEM and its suppliers to improve the early detection of emerging risks. Using key performance indicators derived from operational data, the proposed framework identifies, analyzes, and predicts the impact of risks on supply capabilities, supporting proactive risk management. Pattern recognition and simulation enhance early warning capabilities, while expert-based evaluation supports decision-making for supply chain resilience. This framework fills a gap in predictive supply chain risk assessment and emphasizes industry-specific data insights.