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Research Papers

Data-Driven Decision Tree Classification for Product Portfolio Design Optimization

[+] Author and Article Information
Conrad S. Tucker

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

Harrison M. Kim1

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

To enhance the overall flow of the paper, the elaborate constraints governing the engineering design of cell product variants are condensed and represented by only geng(xeng) and heng(xeng) above. Refer to the Appendix including Table 3 for detailed cell phone design model.

1

Corresponding author.

J. Comput. Inf. Sci. Eng 9(4), 041004 (Nov 02, 2009) (14 pages) doi:10.1115/1.3243634 History: Received December 20, 2007; Revised February 16, 2009; Published November 02, 2009; Online November 02, 2009

The formulation of a product portfolio requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. A design methodology is presented that significantly enhances the product portfolio design process by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a decision tree data mining technique that generates a set of product concepts that are subsequently validated in the engineering design using multilevel optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: (1) it must satisfy customer price and performance expectations (based on the predictive model) defined here as the feasibility criterion; (2) the feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion; (3) the optimal set of products/variants should be a manageable size as defined by the enterprise decision makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when decision tree data mining techniques are incorporated into the product portfolio design and selection process. Using data mining tree generation techniques, a customer data set of 40,000 responses with 576 unique attribute combinations (entire set of possible product concepts) is narrowed down to 46 product concepts and then validated through the multilevel engineering design response of feasible products. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, without violating customer product performance expectations.

FIGURES IN THIS ARTICLE
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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

Grahic Jump Location
Figure 1

Overall flow of product portfolio optimization process.

Grahic Jump Location
Figure 2

C4.5 decision tree solution for 40,000 customer data set

Grahic Jump Location
Figure 3

Set of linear design equations (in matrix form) guiding the product architecture formulation

Grahic Jump Location
Figure 4

A matrix forming the linear equation set. The matrix is sparse, with active elements signified by a value of 1.

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