Autor: Angelika C. Bullinger-Hoffmann

Traditional quality control often falls short in high-mix, low-volume production environments due to variability and complexity. This project introduces an advanced workstation to boost industrial productivity and quality, developed with Axiomatic Design to ensure a clear link between customer needs, functional requirements, and design solutions. Combining polarization cameras, high-resolution imaging, adaptive lighting, and deep learning-based computer vision, the system performs high-accuracy inspection on quantity, quality, and compliance. A digital assistance system offers real-time feedback via an intuitive interface. Validation in a controlled environment confirmed both the system’s practical benefits and its scalability.

Personnel deployment planning in industrial maintenance is a complex challenge, as dispatchers often have to match incomplete customer requests with the appropriate employee skills. An AI-based assistance system can help by automatically analyzing relevant data and providing well-founded suggestions for employee selection. This article describes the user-centered development and introduction of such a system as part of a pilot project at a medium-sized service provider. The user-centered design ensures that dispatchers retain their autonomy. Involving employees from the outset creates acceptance and promotes a deeper understanding of the system’s advantages.