Optimal product portfolio formulation by merging predictive data mining with multilevel optimization

Conrad S. Tucker, Harrison M. Kim

Research output: Contribution to journalArticle

38 Scopus citations

Abstract

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer preference data are translated into performance design targets. Second is product architecture reconfiguration that we model as a dynamic design entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a product architecture is not fixed at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through the cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the engineering level that maximize company profit.

Original languageEnglish (US)
Article number041103
JournalJournal of Mechanical Design, Transactions of the ASME
Volume130
Issue number4
DOIs
StatePublished - Apr 1 2008

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All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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