Examination of platform and differentiating elements in product family design

Michael Van Wie, Robert B. Stone, Henri Thevenot, Timothy Simpson

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

The problems of mass customization, portfolio design, and platform design all pose a common challenge to the designer: knowing how to partition a set of product variants to maximize commonality and simultaneously achieve sufficient differentiation for purposes of customization. This research focuses on the particular issue of how differences between platform elements and differentiating elements are evidenced in the product layout or configuration. The premise of this research is that certain architectural properties, such as modularity, vary between platform and differentiating elements. In particular, certain measures of commonality offer an appropriate set of indices for evaluating these differences in a systematic and repeatable manner. Both function and physical solution commonality provide a descriptor with which to distinguish and rank platform and differentiating elements. By evaluating components of a product in terms of function commonality, physical solution commonality, and modularity, a comparison can be made between platforms and differentiating elements with respect to these indices. The hypothesis of this work is that platforms are integrated and the non-common differentiating elements are, relative to the platforms, more modular. While anecdotal evidence exists to support this idea, the purpose of this work is to evaluate two existing product families as a means for analyzing this hypothesized relation. The result of this research is a descriptive set of knowledge that illustrates distinguishing factors between platform and differentiating elements. The data specifically demonstrates the differences in modularity between platforms and differentiating elements, thus suggesting how this design aspect can and should be addressed during design. While not the focus of this study, future research involving a more prescriptive approach to design can directly benefit from the results. The knowledge gained in this work serves as a foundation for addressing portfolio design where both customization and commonality are key issues.

Original languageEnglish (US)
Pages (from-to)77-96
Number of pages20
JournalJournal of Intelligent Manufacturing
Volume18
Issue number1
DOIs
StatePublished - Feb 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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