Data-driven decision tree classification for product portfolio design optimization

Conrad S. Tucker, Harrison M. Kim

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalJournal of Computing and Information Science in Engineering
Volume9
Issue number4
DOIs
StatePublished - Dec 1 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Data-driven decision tree classification for product portfolio design optimization'. Together they form a unique fingerprint.

Cite this