When applying tools for mass production of components with high quality demands, for example in the medical or the automotive sector, ensuring the traceability of the produced parts as well as the correlation of the individual part quality is a major challenge. Quality control in mass production is most frequently referred to a batch wise inspection of single parts. In most cases it is impossible for the employer to trace the quality of an individual part and the respective conditions applied for processing. This is indispensable information to connect parts outside of the tolerances and the corresponding defects to the conditions of the tool and the manufacturing process. An increase of quality in production and consequently a reduction of production waste is achievable by putting the actual processing parameters into relation with the respective produced parts. Therefore, the aim of the presented work was to implement an autonomous sensor system for monitoring stamping and injection moulding processes. The system comprises specific sensors for data acquisition concerning the most critical processing parameters such as cavity-specific pressure, mould temperature and closing force as well as ambient conditions and time and location of the tool utilisation. Furthermore, a digital twin is implemented in order to validate the occurrence of defects in the produced part simulating the part production by applying the measured processing parameters. Crucial information about the tool usage is displayed in a clearly arranged dashboard developed to inform about operational outcomes as well as productivity data. In conjunction with a connected marking system, the unique identification of the produced parts is realised. Additionally, all recorded data regarding the production conditions is connected to the respective part in a databank. By this, failure and defect analysis can be executed with high efficiency and future defects can easier be prevented.