Identifikation von Anwendungsfällen und Integration datengetriebener Methoden im Produktentwicklungsprozess

Description

The rise of digitalisation and the growing use of artificial intelligence (AI) are gaining increasing attention in Germany, particularly in product development. Despite the vast potential of available data, much of it remains untapped. Digital engineering offers a paradigm shift, yet companies, especially small and medium-sized enterprises (SMEs), struggle to integrate these methods into their processes. While previous research has focused on individual aspects, a comprehensive approach to integrating data-driven methods is still lacking. This thesis presents a method to support SMEs in identifying suitable use cases and integrating data-driven methods into the product development process. An industry study highlights knowledge gaps and a shortage of qualified personnel as key challenges, forming the foundation for the method’s development. The approach consists of five phases, including an assessment of digital maturity, process analysis, and the systematic selection and adaptation of data-driven methods. Its applicability in an industrial context has been successfully validated. This work not only addresses current challenges but also highlights the promising future of AI in product development.