Big data driven customer insights for SMEs in redistributed manufacturing

Soroka, Anthony ORCID: https://orcid.org/0000-0002-9738-9352, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Han, Liangxiu and Haleem, Muhammad Salman 2017. Big data driven customer insights for SMEs in redistributed manufacturing. Procedia CIRP 63 , pp. 692-697. 10.1016/j.procir.2017.03.319

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Abstract

Redistributed 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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Business (Including Economics) Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC) Engineering
Uncontrolled Keywords:	Big data; Redistributed manufacturing; Customer insights
Publisher:	Elsevier
ISSN:	2212-8271
Funders:	EPSRC
Date of First Compliant Deposit:	17 July 2017
Date of Acceptance:	18 March 2017
Last Modified:	05 Nov 2024 22:16
URI:	https://orca.cardiff.ac.uk/id/eprint/102506

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