LeaF: Learning Failure Management

The research project focuses on the error management of individual and small batch production companies in the context of database-driven analysis methods. The increasing availability of data as well as a database based documentation and analysis of defect data based on it offers the possibility to recognize focal points and patterns from the totality of processed defects and to use these for an efficient defect handling. Prioritization and classification algorithms are used for the multi-criteria identification of error centers and form the basis of a systematic analysis methodology.

On the one hand, the error event can be linked with direct and indirect error-promoting process influences. At the same time, the systematic evaluation of historical defect data and corresponding solution concepts serves to accelerate and sustain the process of finding measures and eliminating defects for future defect occurrence. The already acquired knowledge on measures for fault elimination is not lost and is no longer exclusively linked to the wealth of experience of the individual employee.