Abstract
Systematic and efficient use of expert’s knowledge in industrial product and process design is gaining in importance in many knowledge-intensive business processes along the product life cycle (PLC). In the majority of cases, this knowledge is hidden in product and process data and not made explicit by the experts. In this paper an approach is presented to support knowledge intensive business processes by analyzing product and process related data on the basis of a general process model for identifying knowledge in data, aiming at search patterns to find similar historical cases for reusing their solutions. The process model is a top-down-approach from analyzing business processes to applying algorithms to specific data. Considering “offer engineering” in a scenario from automotive supplier industry as a knowledge intentsive task, and since in the product’s development phase 70-80% of its cost is determined, this phase in the PLC is used as a guideline to demonstrate the usefulness of the process model. A tool is presented which allows an adaptive, fuzzy search process in numerical, alphanumerical, and geometrical data based on the evaluated support strategies. First results validate the potential benefit of this approach for conceptual planners in automotive supplier industry.
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Entrup, C.L., Barth, T., Schäfer, W. (2006). Towards a Process Model for Identifying Knowledge-Related Structures in Product Data. In: Reimer, U., Karagiannis, D. (eds) Practical Aspects of Knowledge Management. PAKM 2006. Lecture Notes in Computer Science(), vol 4333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11944935_17
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DOI: https://doi.org/10.1007/11944935_17
Publisher Name: Springer, Berlin, Heidelberg
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