Mapping customer needs to design parameters in the front end of product design by applying deep learning. Issue 1 (2018)

• Record Type: Journal Article
• Authors: Wang, Yue; Mo, Daniel Y.; Tseng, Mitchell M.
• Is Part Of: CIRP annals. Volume 67:Issue 1(2018)
• Page Start: 145
• Page End: 148
• Publication Date: 2018
• Subjects: Design -- Customization -- Deep learning; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2018.04.018
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: The key to successful product design is better understanding of customer needs (CNs), and efficiently translating CNs into design parameters (DPs). With the recent trend toward the diversification of CNs, the rapid introduction of new products, and shortened lead times, there is a growing need to speed up the mapping from CNs to DPs. By leveraging on product review data extracted e-commerce websites, this paper proposes a deep learning-based approach to improve the effectiveness and efficiency of mapping CNs to DPs. The results show that the proposed approach can meet customer needs with high efficiency.