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dc.contributor.authorLee, BY
dc.contributor.authorAhmed-Kristensen, S
dc.date.accessioned2024-03-15T10:19:25Z
dc.date.issued2023-06-19
dc.date.updated2024-03-14T17:12:05Z
dc.description.abstractIn the midst of Industry 4.0 where digitalisation is stimulated through the Internet of Things (IoT), Big Data, and machine learning technologies, an increasing volume of valuable data has been acquired from sensors and interconnected devices. This data-driven paradigm can enable organisations to create new or improved products and services, build long-term customer relationships in a value co-creation manner, adapt to continuous business reconfiguration or address societal challenges such as sustainability. Scientific research addressing Data-driven design has increased steadily in the last few years. However, despite this, there is still a need for a comprehensive understanding of data-driven design processes. Thus, through a systematic literature review, we review the data-driven design activities observed in the new product and service development and types of data utilised in New Product Development (NPD) process. This paper contributes to design research and through reviewing the current landscape of Data-driven design identifies ten data-driven design activities and four-dimensional aspects in NPD processen_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationVol. 3: ICED23, pp. 1925 - 1934en_GB
dc.identifier.doihttps://doi.org/10.1017/pds.2023.193
dc.identifier.grantnumberEP/T022566/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/135560
dc.language.isoenen_GB
dc.publisherCambridge University Pressen_GB
dc.rights© The Author(s), 2023. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.en_GB
dc.subjectBig dataen_GB
dc.subjectNew product developmenten_GB
dc.subjectDesign practiceen_GB
dc.subjectData-driven designen_GB
dc.titleFour Patterns of Data-Driven Design Activities in New Product Developmenten_GB
dc.typeArticleen_GB
dc.date.available2024-03-15T10:19:25Z
exeter.locationBordeaux
exeter.place-of-publicationCambridge University Press
dc.descriptionThis is the final version. Available on open access from Cambridge University Press via the DOI in this recorden_GB
dc.descriptionPaper presented at the International Conference on Engineering Design, ICED23, Bordeaux, France, 24 - 28 July 2023en_GB
dc.identifier.eissn2732-527X
dc.identifier.journalProceedings of the Design Societyen_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2023-06-19
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2024-03-14T17:12:08Z
refterms.versionFCDVoR
refterms.dateFOA2024-03-15T10:19:31Z
refterms.panelCen_GB
pubs.name-of-conferenceInternational Conference of Engineering Design


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© The Author(s), 2023. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Except where otherwise noted, this item's licence is described as © The Author(s), 2023. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.