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Research Papers: Design Theory and Methodology

Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization

[+] Author and Article Information
Conrad S. Tucker

Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, 104 South Mathews Avenue, Urbana, IL 61801ctucker4@uiuc.edu

Harrison M. Kim1

Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, 104 South Mathews Avenue, Urbana, IL 61801hmkim@uiuc.edu

www.webtools.uiuc.edu

1

Corresponding author.

J. Mech. Des 130(4), 041103 (Mar 20, 2008) (15 pages) doi:10.1115/1.2838336 History: Received October 31, 2006; Revised October 22, 2007; Published March 20, 2008

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.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

Grahic Jump Location
Figure 4

Optimal product portfolio example. Illustrates how just two product architectures can generate product variants that make up a family of products (product portfolio of K=5 products).

Grahic Jump Location
Figure 3

Data flow of product portfolio formulation

Grahic Jump Location
Figure 2

D2K Naïve Bayes prediction of maximum customer purchasing price (MaxPrice) and associated market share αi

Grahic Jump Location
Figure 1

Overall predictive product portfolio formulation (adapted from D2K manual (14))

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