Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry. (January 2022)

• Record Type: Journal Article
• Authors: Tsang, Y.P.; Wu, C.H.; Lin, Kuo-Yi; Tse, Y.K.; Ho, G.T.S.; Lee, C.K.M.
• Is Part Of: Journal of manufacturing systems. Volume 62(2022)
• Page Start: 777
• Page End: 791
• Publication Date: 2022-01
• Subjects: Product design -- Fuzzy front end -- Fuzzy inference system -- Big data analytics -- Industrial intelligence; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.02.003
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Industrial big data analytics facilitates the deployment of machine learning on product design. A closed-loop learning scheme is built to support product design in the stage of fuzzy front end. Recursive association-rule-based fuzzy inference system analyses fuzziness of fuzzy front end. Abstract: New product development to enhance companies' competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers, manufacturing capability, and market trend, are considered in the FFE phase to avoid common flaws in product design. Because of the high degree of uncertainties in the FFE, multidimensional and high-volume data are added from time to time at the beginning of the formal product development process. To address the above concerns, deploying big data analytics to establish industrial intelligence is an active but still under-researched area. In this paper, an intelligent product design framework is proposed to incorporate fuzzy association rule mining (FARM) and a genetic algorithm (GA) into a recursive association-rule-based fuzzy inference system to bridge the gap between customer attributes and design parameters. Considering the current incidence of epidemics, such as the COVID-19 pandemic, communication of information in the FFE stage may be hindered. Through this study, a recursive learning scheme is established, therefore, to strengthen market performance, design performance, and sustainability on product design. It is found that the industrial big data analytics in the FFE process achieve greater flexibility and self-improvement mechanism on the evolution of product design.