Supply Chain Turbulence Index using Foot Traffic Data and Factor Decomposition Method

Since the emergence of the COVID-19 pandemic, disruptions in supply chains have significantly impacted both the global economy and asset markets. Despite a rising interest in supply-related data among policymakers, researchers, and financial market participants, existing indicators often wrestle with pervasive issues of low frequency and coarse granularity. In this carefully crafted paper, we ambitiously propose new, robust indices for the highfrequency nowcasting of disturbances specifically within the automotive supply chain. Firstly, by judiciously utilizing inter-factory transition data alongside time series anomaly detection methodologies, we have successfully created the Supply Chain Turbulence Index (SCTI). To further augment the SCTI, we introduce a novel, sophisticated technique, grounded on the principles of enhanced Variational Autoencoders, to diligently isolate supply factors contributing to bottlenecks in the supply chain, subsequently creating a nuanced subindex, dubbed SCTI-supply. The SCTI exhibits a strong correlation with existing statistics concerning supply chain delays and demonstrates the remarkable capability to detect micro-level production interruptions across various car manufacturers’ plants. On the other hand, SCTIsupply correlates effectively with low-frequency supply chain indicators we developed from established statistics and proves exceedingly effective in identifying supply shocks under rigorous event study analysis.