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dc.contributor.authorKaiser, J
dc.contributor.authorTerrazas, G
dc.contributor.authorMcFarlane, D
dc.contributor.authorde Silva, L
dc.date.accessioned2021-12-07T00:30:53Z
dc.date.available2021-12-07T00:30:53Z
dc.date.issued2021
dc.identifier.issn0951-5666
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331247
dc.description.abstractMachine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a ‘simple solution’, which we will characterise in this paper, should be sufficient.
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC: EP/R032777/1).
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International (CC BY)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleTowards low-cost machine learning solutions for manufacturing SMEs
dc.typeArticle
dc.publisher.departmentDepartment of Engineering Student
dc.publisher.departmentDepartment of Engineering
dc.date.updated2021-12-03T15:51:56Z
prism.publicationNameAI and Society
dc.identifier.doi10.17863/CAM.78692
dcterms.dateAccepted2021-12-01
rioxxterms.versionofrecord10.1007/s00146-021-01332-8
rioxxterms.versionVoR
dc.contributor.orcidKaiser, J [0000-0002-7264-5065]
dc.identifier.eissn1435-5655
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R032777/1)
cam.issuedOnline2021-12-22
cam.orpheus.successTue Feb 01 19:02:23 GMT 2022 - Embargo updated
cam.depositDate2021-12-03
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International (CC BY)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY)