Analysis reuse exploiting taxonomical information and belief assignment in industrial problem solving

• A new enriched experience representation resulting from a particular problem solving.
• Two mechanisms to exploit the information content of context and semantic analysis.
• The proposed approach emulates the people's cognitive structure and reasoning model.
• Understandable outcomes to support the capture and reuse of analysis and knowledge.
• Integration of the models and inference mechanisms in a commercial software tool.

To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of “Rex Manager” with principles of sustainable management for continuous improvement of industrial processes in companies.