The automotive supplier industry is characterised by high quality requirements and high competitive business environments. Traceability systems offer these companies the opportunity to minimise damage in the event of a complaint and allows them to record production-related data in real time, thus utilising the potential of Industry 4.0 technologies by linking machine data with the information of parts and components. Therefore, the aim of this master thesis is to identify the challenges associated with the implementation of an internal traceability system for parts and components in the automotive industry, as well as to develop a requirements catalogue. For this purpose, a systematic literature research was carried out and a catalogue including technical, process-related and economic requirements, as well as requirements for the organisation, employees, data and software was created. Furthermore, the logistical and bill of material levels were identified as a tool to help organisations define the object to be traced and a list was created containing the different identification and process features that could be recorded in the context of traceability. The studies examined during the literature review show that the benefits of internal traceability go beyond ensuring quality and traceability in the event of a complaint, as they additionally help companies increase the transparency, efficiency and flexibility of the material flow, as well as to improve the management and control of complex production processes. The developed requirements catalogue was assessed based on the example of the automotive supplier Magna Powertrain GmbH & Co KG. The goal is to record the actual process of internal traceability and to derive recommendations for action to achieve complete traceability at the level of individual parts. These include ensuring the internal traceability process from the first process step and adapting the opportunity for identification as close as possible to the requirements of the process. Furthermore, it is important to collect all available data and store it in a central database so that it can be actively used for analyses while maintaining good data quality.