An Empirical Investigation of the Impact of Data-Driven Decision -Making on Supply Chain Resilience in the Automotive Industry

Abstract

The empirical investigation of supply chain risk management techniques is the aim of this paper. The analysis is predicated on a survey that was undertaken in the German automobile sector involving 67 manufacturing facilities. The study identifies supply chain risks by evaluating their probability of occurrence and possible effects on the supply chain after looking into the susceptibility of supply chains generally and major drivers of supply chain risks. The outcomes are displayed in a probability-impact matrix that differentiates supply chain hazards from external sources. Additionally, tools for managing supply chain risks are looked into. As a result, the effect of performance-enhancing supply chain risk management is evaluated. The plants are categorized using a cluster analysis based on parameters indicating the instruments of supply chain risk management in order to differentiate between businesses with a high degree of implementation and those with none or very limited use.