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dc.contributor.authorSoroka, A
dc.contributor.authorLiu, Y
dc.contributor.authorHan, L
dc.contributor.authorHaleem, MS
dc.date.accessioned2023-11-03T11:26:50Z
dc.date.available2023-11-03T11:26:50Z
dc.date.issued2017
dc.identifier.issn2212-8271
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/91657
dc.description.abstractRedistributed manufacturing (RdM) refers to manufacturing business models, strategies, systems and technologies that change the economics and organization of manufacturing, particularly related to location and scale. Small-scale manufacturing has the potential to help tailor products to satisfy the specific needs of consumers different in terms of geographical location, cultural roots, improve sustainability and drive the society towards circular economy. While RdM has the great potential to deliver this, currently, very little has been understood on how RdM could help SMEs for gaining economic benefits due to the constraint of their business model, lack of understanding on customers, limited resource commitment on R&D, marketing and sales, supply chain integration, etc. Similarly user-driven design and customer-insights delivered through ‘big data’ analytics has the potentially to be highly beneficial for manufacturing SME and little is known of how they can be combined with RdM to benefit SMEs. Hence, they may impose risks on the business and operation of SMEs should poor choices be made or systems be implemented badly. The economic importance of SMEs within the UK and Europe is long established with manufacturing SMEs accounting for 60% of all private sector jobs in the UK. Within the European Union the overwhelming majority of companies (trading in the non-financial sectors) were SMEs (99.8%), employing 89.7 million people (67.1% of the workforce). This paper reports some of the results of an initial exploratory survey of manufacturing SMEs within the United Kingdom. Focusing on their background and status, and their current understanding and interests in RdM, big customer analytics and related topics.en_US
dc.format.extent692 - 697
dc.languageen
dc.publisherElsevier BVen_US
dc.relation.ispartofProcedia CIRP
dc.rightsThis item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.titleBig Data Driven Customer Insights for SMEs in Redistributed Manufacturingen_US
dc.typeArticleen_US
dc.rights.holder© 2017 The Author(s). Published by Elsevier B.V.
dc.identifier.doi10.1016/j.procir.2017.03.319
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.publisher-urlhttp://dx.doi.org/10.1016/j.procir.2017.03.319en_US
pubs.volume63en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Except where otherwise noted, this item's license is described as This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.