A data-driven Component Risk Matrix to assess supply chain disruption risk

Abstract

We present a data-driven approach to assess supply chain disruption risk at the component level. Our ‘Component Risk Matrix’ categorizes components based on their predicted stock break frequency and severity. Our predictive models employ an XGBoost model with historical disruption data and each component's unique characteristics. Our approach enables prioritizing components for resilience measures by quantifying their criticality and identifying the key drivers behind this criticality. We validate our methodology on 1867 components from an original equipment manufacturer, demonstrating its practical applicability and providing insights toward risk mitigation. This data-driven approach empowers companies to strategically build supply chain resilience in designing their products and supply chains