A fuzzy CBR technique for generating product ideas

Muh-Cherng Wu*, Ying Fu Lo, Shang Hwa Hsu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

This paper presents a fuzzy CBR (case-based reasoning) technique for generating new product ideas from a product database for enhancing the functions of a given product (called the baseline product). In the database, a product is modeled by a 100-attribute vector, 87 of which are used to model the use-scenario and 13 are used to describe the manufacturing/recycling features. Based on the use-scenario attributes and their relative weights - determined by a fuzzy AHP technique, a fuzzy CBR retrieving mechanism is developed to retrieve product-ideas that tend to enhance the functions of the baseline product. Based on the manufacturing/recycling features, a fuzzy CBR mechanism is developed to screen the retrieved product ideas in order to obtain a higher ratio of valuable product ideas. Experiments indicate that the retrieving-and-filtering mechanism outperforms the prior retrieving-only mechanism in terms of generating a higher ratio of valuable product ideas.

Original languageEnglish
Pages (from-to)530-540
Number of pages11
JournalExpert Systems with Applications
Volume34
Issue number1
DOIs
StatePublished - 1 Jan 2008

Keywords

  • Case-based reasoning
  • Fuzzy AHP
  • Fuzzy CBR
  • New product development

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