This thesis examines the application of process mining to improve logistical processes of manufacturing companies. Process mining combines elements of data science with process management to identify and improve real processes by analysing event logs. The thesis includes a comprehensive theoretical introduction to the topics of logistics, business process management and data science, followed by a more detailed insight into the fundamentals of process mining. An essential part of the thesis is the development of a practical guideline for the use of process mining, based on a systematic literature review. This guideline is applied in a case study in which the methodology is implemented in a production company in order to improve logistical processes. The preparation, implementation and follow-up of the case study are discussed in detail, including how data is collected, processed and analysed to identify potential for process optimisation. The results of the case study show that by applying the developed guidelines, an improvement in the transparency of logistical processes can be achieved. Challenges and limitations of the technology are also discussed, in particular the quality and availability of data. The discussion of the results leads to concrete recommendations on how process mining can be used effectively to increase the performance of logistics processes. This includes careful data maintenance, the selection of suitable process mining tools and the ongoing training of employees in order to fully utilise the potential of the technology.