Digital-supported problem solving for shopfloor steering using case-based reasoning and Bayesian networks

Abstract

Uncertainty and incompleteness of data challenge the design of knowledge systems for problem solving in shopfloor management. The paper proposes a data-driven design that incorporates traditional means of quality management and goals of production planning and control. It integrates data of a failure mode effects analysis (FMEA) and an 8D problem-solving process into a Bayesian network (BN) embedded case-based reasoning (CBR) cycle. Reducing inconsistencies within the BN, an optimization method uses scoring schemes and structural equation modeling for learning its structure. The results suggest that the optimized BN-CBR system outperforms the single use of CBR in terms of accuracy.