{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T07:31:05Z","timestamp":1771054265251,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Small and medium-sized enterprises (SMEs) in manufacturing are increasingly facing challenges of digital transformation and a shift towards cloud-based solutions to leveraging artificial intelligence (AI) or, more specifically, machine learning (ML) services. Although literature covers a variety of frameworks related to the adaptation of cloud solutions, cloud-based ML solutions in SMEs are not yet widespread, and an end-to-end process for ML cloud service selection is lacking. The purpose of this paper is to present a systematic selection process of ML cloud services for manufacturing SMEs. Following a design science research approach, including a literature review and qualitative expert interviews, as well as a case study of a German manufacturing SME, this paper presents a four-step process to select ML cloud services for SMEs based on an analytic hierarchy process. We identified 24 evaluation criteria for ML cloud services relevant for SMEs by merging knowledge from manufacturing, cloud computing, and ML with practical aspects. The paper provides an interdisciplinary, hands-on, and easy-to-understand decision support system that lowers the barriers to the adoption of ML cloud services and supports digital transformation in manufacturing SMEs. The application in other practical use cases to support SMEs and simultaneously further development is advocated.<\/jats:p>","DOI":"10.3390\/computers11010014","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T08:20:42Z","timestamp":1642407642000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Systematic Selection Process of Machine Learning Cloud Services for Manufacturing SMEs"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2730-5915","authenticated-orcid":false,"given":"Can","family":"Kaymakci","sequence":"first","affiliation":[{"name":"Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany"},{"name":"Institute for Energy Efficiency in Production EEP, University of Stuttgart, 70569 Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7565-3562","authenticated-orcid":false,"given":"Simon","family":"Wenninger","sequence":"additional","affiliation":[{"name":"FIM Research Center, University of Applied Sciences Augsburg, 86159 Augsburg, Germany"},{"name":"Project Group Business and Information Systems Engineering of the Fraunhofer FIT, 86159 Augsburg, Germany"}]},{"given":"Philipp","family":"Pelger","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany"},{"name":"Institute for Energy Efficiency in Production EEP, University of Stuttgart, 70569 Stuttgart, Germany"}]},{"given":"Alexander","family":"Sauer","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany"},{"name":"Institute for Energy Efficiency in Production EEP, University of Stuttgart, 70569 Stuttgart, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Serrano-Ruiz, J.C., Mula, J., and Poler, R. 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