Full Content is available to subscribers

Subscribe/Learn More  >

Product Family Concept Generation and Validation Through Predictive Decision Tree Data Mining and Multi-Level Optimization

[+] Author Affiliations
Conrad S. Tucker, Harrison M. Kim

University of Illinois at Urbana-Champaign, Urbana, IL

Paper No. DETC2007-34892, pp. 971-987; 17 pages
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 6: 33rd Design Automation Conference, Parts A and B
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4807-8 | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME


The formulation of a product family requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. We present a methodology to significantly reduce the computational time required to achieve an optimal product portfolio by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a data mining decision tree technique that generates a set of product concepts that are subsequently validated in the engineering design level using multi-level optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: 1) The ability to satisfy customer’s price and performance expectations (based on 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 decisions 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 data mining predictive techniques are applied to the formulation of an optimal product portfolio. Using data mining tree generation techniques, a customer response data set of 40,000 individual product preferences is narrowed down to 46 product family concepts and then validated through the multilevel engineering design response of feasible architectures. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, while concurrently satisfying customer product performance expectations.

Copyright © 2007 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In