Creating Transparency in the Finished Vehicles Transportation Process Through the Implementation of a Real-Time Decision Support System

Abstract

The complexity of global distribution networks in the automotive industry and likewise the number of disruptions significantly increased throughout the last years. In order to monitor relevant processes and to optimize decision-making in case of disruptions, a concept for a decision support system (DSS) was introduced. For this purpose, the distribution process weaknesses of the German premium automotive company BMW were identified. The method used was a Failure Mode and Effect Analysis with operational managers and relevant process partners interviews. Based on the findings, performance indicators, thresholds, early warnings and options for action were specified. A big data platform supports the processing of the growing number of relevant data in real-time. In the long-term decision-making can be automated using machine learning algorithms. This paper proves that negative impacts of disruptions can be minimized, and the robustness of the process improved by anticipating and identifying deviations beforehand and in real-time. Hence, companies save money while strengthening customer satisfaction. The DSS can be seen as a necessary precursor of a digital twin.