Development of a warehouse utilization data analysis and forecast tool for Volkswagen AG

Against the background of the fourth industrial revolution, Industry 4.0, production systems and warehouses increasingly get networked. These cyber-physical systems generate an exponentially rising amount of data. Consequently, the possibilities for deriving information for decision-making from the so-called big data increase as well. The objective of this bachelor's thesis was to exploit one of these potentials for the automobile manufacturer Volkswagen AG. Logistics data from the Volkswagen Industrial Cloud was analyzed in order to obtain information about the utilization and stock development of multiple Volkswagen warehouses. The required data was accessed and identified within several corresponding databases, following which datasets were created. A cloud computing-based calculation algorithm was developed afterward for evaluating the created datasets. Due to the complexity of the information, a visualization concept according to lean user experience design principles was created. This concept aimed to improve the efficiency of information comprehension for a human user. For processing the above-mentioned project stages, the agile project management method Scrum was applied. The information needed was derived according to the intelligence cycle within the framework of structured data analysis. The project work led to 100% data accuracy with regard to the stored stock of a sample warehouse in Hanover while other warehouses could be analyzed successfully afterward. However, the utilization accuracy still was to be monitored and tested at the time of project completion. Furthermore, premises for future forecasting through AI-based software as a service tools were created. The data basis for this implementation needs to be collected over time as a subsequent step. This thesis contains confidential information from Volkswagen AG. The affected sections were therefore blackened in the public report