Quantifying the relevance of product feature classification in product family design

Conrad S. Tucker

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

The methodology proposed in this chapter aims to address the link between the evolution of product feature relevance and the implications to product platform and product family design. By quantifying relevant/irrelevant product features to be included in next-generation product platform design, designers can identify the stand-alone or platform sharing components required to achieve desired product functionality. A data mining algorithm is introduced that uses time series data (consisting of product features) to determine the standard, nonstandard, and obsolete product features in the design of next-generation products. Product features are then mapped to engineering components/modules by employing data mining Natural Language Processing techniques that quantify the functionality requirements that are needed for a given set of product features. The goal of this work is to demonstrate the value of incorporating evolving product feature trends in the market space directly into product platform and product family sharing decisions.

Original languageEnglish (US)
Title of host publicationAdvances in Product Family and Product Platform Design
Subtitle of host publicationMethods and Applications
PublisherSpringer New York
Pages147-177
Number of pages31
ISBN (Electronic)9781461479376
ISBN (Print)9781461479369
DOIs
StatePublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Tucker, C. S. (2014). Quantifying the relevance of product feature classification in product family design. In Advances in Product Family and Product Platform Design: Methods and Applications (pp. 147-177). Springer New York. https://doi.org/10.1007/978-1-4614-7937-6_6