Product concept evaluation and selection using data mining and domain ontology in a crowdsourcing environment. Issue 4 (October 2015)

• Record Type: Journal Article
• Authors: Chang, Danni; Chen, Chun-Hsien
• Is Part Of: Advanced engineering informatics. Volume 29:Issue 4(2015:Oct.)
• Page Start: 759
• Page End: 774
• Publication Date: 2015-10
• Subjects: Product concept evaluation and selection -- Data mining -- Domain ontology -- Crowdsourcing; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2015.06.003
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: For product design and development, crowdsourcing shows huge potential for fostering creativity and has been regarded as one important approach to acquiring innovative concepts. Nevertheless, prior to the approach could be effectively implemented, the following challenges concerning crowdsourcing should be properly addressed: (1) burdensome concept review process to deal with a large amount of crowd-sourced design concepts; (2) insufficient consideration in integrating design knowledge and principles into existing data processing methods/algorithms for crowdsourcing; and (3) lack of a quantitative decision support process to identify better concepts. To tackle these problems, a product concept evaluation and selection approach, which comprises three modules, is proposed. These modules are respectively: (1) a data mining module to extract meaningful information from online crowd-sourced concepts; (2) a concept re-construction module to organize word tokens into a unified frame using domain ontology and extended design knowledge; and (3) a decision support module to select better concepts in a simplified manner. A pilot study on future PC (personal computer) design was conducted to demonstrate the proposed approach. The results show that the proposed approach is promising and may help to improve the concept review and evaluation efficiency; facilitate data processing using design knowledge; and enhance the reliability of concept selection decisions.