Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/94615
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorTsang, YPen_US
dc.creatorWu, CHen_US
dc.creatorLin, KYen_US
dc.creatorTse, YKen_US
dc.creatorHo, GTSen_US
dc.creatorLee, CKMen_US
dc.date.accessioned2022-08-25T01:54:11Z-
dc.date.available2022-08-25T01:54:11Z-
dc.identifier.issn0278-6125en_US
dc.identifier.urihttp://hdl.handle.net/10397/94615-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2021 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.en_US
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Tsang, Y. P., Wu, C. H., Lin, K.-Y., Tse, Y. K., Ho, G. T. S., & Lee, C. K. M. (2022). Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry. Journal of Manufacturing Systems, 62, 777-791 is available at https://dx.doi.org/10.1016/j.jmsy.2021.02.003.en_US
dc.subjectBig data analyticsen_US
dc.subjectFuzzy front enden_US
dc.subjectFuzzy inference systemen_US
dc.subjectIndustrial intelligenceen_US
dc.subjectProduct designen_US
dc.titleUnlocking the power of big data analytics in new product development : an intelligent product design framework in the furniture industryen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1016/j.jmsy.2021.02.003en_US
dcterms.abstractNew 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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of manufacturing systems, Jan. 2022, v. 62, p. 777-791en_US
dcterms.isPartOfJournal of manufacturing systemsen_US
dcterms.issued2022-01-
dc.identifier.scopus2-s2.0-85101100273-
dc.description.validate202208 bcwwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0206-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextLaboratory for Artificial Intelligence in Design Limited (AiDLab)en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS53187812-
dc.description.oaCategoryGreen (AAM)en_US
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