The project designs an AI-based assistance system for data-driven decision support in operational shop floor management. The system uses near-real-time sensor data and ML models to predict process deviations in a continuous production environment. In the event of a predicted deviation, the system generates additional assistance by deriving deviation indicators and recommendations for action using explainable AI methods and experience-based knowledge. The aim is to enable operative decision-makers to take preventive actions.
Researcher
Publications
- Schamberger, M., Breu, M., & Bodendorf, F. (2024). Data-Driven Decision-Making in Shop Floor Quality Management – A Systematic Literature Review. In Yi-Chi Wang, Siu Hang Chan, Zih-Huei Wang (Eds.), Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order. Proceedings of FAIM 2024, June 23–26, 2024, Taichung, Taiwan, Volume 2 (pp. 424-431). Taichung, TW: Cham: Springer. https://doi.org/10.1007/978-3-031-74485-3_47
- Hörner, L., Schamberger, M., & Bodendorf, F. (2022). Using Tacit Expert Knowledge to Support Shop-floor Operators Through a Knowledge-based Assistance System. Computer Supported Cooperative Work-The Journal of Collaborative Computing. https://doi.org/10.1007/s10606-022-09445-4
- Hörner, L., Schamberger, M., & Bodendorf, F. (2020). Externalisierung von prozess-spezifischem Mitarbeiterwissen im Produktionsumfeld. Zeitschrift für wirtschaftlichen Fabrikbetrieb, 115, 413-417. https://doi.org/10.3139/104.112357
Partner
