Ansätze zur Entwicklung einer Zuteilungssystematik von Bauteilen mit komplexen Abhängigkeiten in großen Produktionsnetzwerken

Abstract

The increasing volatility of supply networks, driven by geopolitical tensions, material shortages and transportation bottlenecks, poses significant challenges to the reliable supply of critical components. This is particularly evident in the automotive industry, where short planning cycles, high product variety and fluctuating demand create substantial complexity in the short-term allocation of scarce resources. This dissertation explores how a multi-criteria allocation framework can be designed to optimize eco-nomic performance while enhancing operational stability within highly interconnected production net-works. Grounded in the Design Science Research methodology, 23 solution objectives are derived to develop a four-stage process model that systematically guides allocation decisions from the onset of a supply bottleneck to final resolution. Additionally, five empirically validated design principles provide a conceptual foundation for robust allocation systems. A technical evaluation was conducted through a case study with a leading German automotive manufacturer, involving a simulation based on over 500,000 vehicle orders. The results reveal significant potential for cost savings related to allocation decisions, alongside enhanced utilization of critical com-ponents. Practical relevance and user acceptance were assessed through 29 expert interviews with representatives from six industry and research organizations. The feedback highlights the value of simulation-based decision support and the importance of clear governance structures in dynamic production environments. The scientific contribution of this dissertation lies in the integration and empirical validation of a multi-criteria, simulation-supported process model. It provides practically applicable design principles for allocating scare resources under uncertainty, thereby contributing to the advancement of data-driven planning in complex production networks.