Knowledge-Based Problem Solving in Physical Product Development – A Methodological Review

Peter Burggräf, Johannes Wagner, Tim Weißer
<span title="">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d5wanubrsjhehcfo2eijwbv55m" style="color: black;">Expert Systems with Applications: X</a> </i> &nbsp;
Highlights:  Physical product development constitutes an issue in practice and theory  NLP and case enrichment are key components to access problem relevant knowledge  Multi-agent case-based reasoning systems represent the current state of the art  Prevention-and anticipation mechanisms should be considered for future research  Clustering algorithms contribute to objectify literature filtering Abstract: The manufacturing of products at low maturity levels (referred to as physical product
more &raquo; ... velopment) requires knowledge intensive nonconformance problem solving, yet constituting a major difficulty in industry. Due to the exponential increase of failure cost during the product development process however, problems have to be effectively remedied as early as possible. Facing shortened innovation cycles, problem solving efficiency simultaneously constitutes a competitive factor. The purpose of this theoretical review is therefore the analysis of relevant approaches contributing to knowledge-based problem solving in physical product development, to synthesize a comprehensive construct as well as to derive novel conceptualizations. The latter demonstrably emerges from natural language processing, case ontologies and machine-/deep learning support, embedded in a distributed case-based reasoning architecture. Building on this, we likewise encourage researchers and professionals to propose new studies dedicated to the field of problem solving in physical product development. ___________________________________________________________________________
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