Optimization of Intralogistics Flows through Real-Time Locating Systems: The Case of Tesla Gigafactory Berlin

The increasing complexity of automotive manufacturing plants requires innovative digital solutions capable of providing accurate data and enabling data-driven optimization of intralogistics processes, ensuring both efficiency and flexibility. This thesis explores the potential of Real-Time Locating Systems (RTLS) as a digital enabler within Tesla’s Gigafactory Berlin-Brandenburg. The focus lies on the continuous supply of materials to General Assembly (GA) lines, where Just-In-Time (JIT) deliveries must be guaranteed to sustain production flow. The research addresses two main objectives: first, to evaluate the capability of RTLS to generate accurate and standardized localization data for vehicles and materials circulating inside the plant; second, to integrate this information into the design and management of tugger train fleets, with particular emphasis on fleet sizing and delivery scheduling. To this end, the thesis combines static sizing methods with simulation-based approaches, enabling both the validation of current operations and the assessment of optimized scenarios. The findings demonstrate that RTLS provides reliable insights into intralogistics flows, supporting data-driven decisions for route design, delivery planning, and fleet utilization. Furthermore, the integration of real-time data into intralogistics optimization enables significant improvements in resource allocation and overall cost-effectiveness. The study confirms that RTLS technologies can play a key role in shaping the future of smart manufacturing, offering both practical benefits for current operations and a scalable foundation for further digital transformation.