Strategies for Improving Data Management in Automotive Testing and Commissioning Processes

The decoupling of software from hardware in automotive systems, driven by the rising share of software in modern vehicles, has introduced a paradigm shift, enabling various software configurations on identical hardware platforms. Consequently, ensuring the correct functionality and reliability of the electric and electronic hardware components, testing and commissioning processes in the vehicle production have grown in importance and complexity. However, the efficiency of these processes relies on diverse datasets, for example parameterization data that allows tailored testing based on the vehicle’s equipment configuration. Therefore, the availability and accuracy of these data need to be guaranteed. Data for testing and commissioning, influenced by the digitization of production processes and their planning, is not only facing the challenges of greater software volumes and faster update cycles, but also those arising from legacy processes or the integration of various IT systems into production environments. In this context, this research explores methods for evaluating the quality of data that is required for planning as well as executing testing and commissioning processes. Special attention is paid to diagnostic data descriptions, requirement documents for testing and commissioning as well as documentation data of the error elimination process. First, a representative sample of such data is selected and then analyzed in terms of requirements from data consumers. Thereby, criteria for the assessment of data quality are developed aiming to improve process efficiency. The presented set of criteria aim to provide a foundation for original equipment manufacturers (OEMs) to optimize their data management methods. This work offers valuable insights and practical steps towards improving data accessibility and fostering better documentation practices in automotive production.