Towards low-cost machine learning solutions for manufacturing SMEs
| cam.depositDate | 2021-12-03 | |
| cam.issuedOnline | 2021-12-22 | |
| cam.orpheus.success | Tue Feb 01 19:02:23 GMT 2022 - Embargo updated | |
| dc.contributor.author | Kaiser, J | |
| dc.contributor.author | Terrazas, G | |
| dc.contributor.author | McFarlane, D | |
| dc.contributor.author | de Silva, L | |
| dc.contributor.orcid | Kaiser, J [0000-0002-7264-5065] | |
| dc.date.accessioned | 2021-12-07T00:30:53Z | |
| dc.date.available | 2021-12-07T00:30:53Z | |
| dc.date.issued | 2021 | |
| dc.date.updated | 2021-12-03T15:51:56Z | |
| dc.description.abstract | <jats:title>Abstract</jats:title><jats:p>Machine 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, <jats:italic>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</jats:italic>. If an analytics solution does require learning capabilities, a ‘simple solution’, which we will characterise in this paper, should be sufficient.</jats:p> | |
| dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC: EP/R032777/1). | |
| dc.identifier.doi | 10.17863/CAM.78692 | |
| dc.identifier.eissn | 1435-5655 | |
| dc.identifier.issn | 0951-5666 | |
| dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/331247 | |
| dc.language.iso | eng | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.publisher.department | Department of Engineering Student | |
| dc.publisher.department | Department of Engineering | |
| dc.publisher.url | http://dx.doi.org/10.1007/s00146-021-01332-8 | |
| dc.rights | Attribution 4.0 International (CC BY) | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Machine learning | |
| dc.subject | Low cost | |
| dc.subject | Small- and medium-sized enterprises | |
| dc.subject | Digital manufacturing on a shoestring | |
| dc.title | Towards low-cost machine learning solutions for manufacturing SMEs | |
| dc.type | Article | |
| dcterms.dateAccepted | 2021-12-01 | |
| prism.publicationName | AI and Society | |
| pubs.funder-project-id | Engineering and Physical Sciences Research Council (EP/R032777/1) | |
| pubs.licence-display-name | Apollo Repository Deposit Licence Agreement | |
| pubs.licence-identifier | apollo-deposit-licence-2-1 | |
| rioxxterms.type | Journal Article/Review | |
| rioxxterms.version | VoR | |
| rioxxterms.versionofrecord | 10.1007/s00146-021-01332-8 |
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