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dc.contributor.author | Mateo Casalí, Miguel Angel | es_ES |
dc.contributor.author | Fraile Gil, Francisco | es_ES |
dc.contributor.author | Boza, Andrés | es_ES |
dc.contributor.author | Nazarenko, Artem | es_ES |
dc.date.accessioned | 2023-11-07T10:31:10Z | |
dc.date.available | 2023-11-07T10:31:10Z | |
dc.date.issued | 2023-07-31 | |
dc.identifier.uri | http://hdl.handle.net/10251/199416 | |
dc.description.abstract | [EN] The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through the extensive introduction of automatons, computers and Internet of Things (IoT) components. All this seeks to streamline, improve, and increase production at the lowest possible cost and avoid any failure in the creation of the product, following a strategy called Zero Defect Manufacturing . Machine Learning Operations (MLOps) provide a ML-based solution to this challenge, promoting the automation of all product-relevant steps, from development to deployment. When integrating different machine learning models within manufacturing operations, it is necessary to understand what functionality is needed and what is expected. This article presents a maturity model that can help companies identify and map their current level of implementation of machine learning models. | es_ES |
dc.description.sponsorship | The research leading to these results received funding from the European Union H2020 programs with grant agreements No. 825631, “Zero-Defect Manufacturing Platform (ZDMP)” and No. 958205, “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)”. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | International Journal of Production Management and Engineering | es_ES |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Manufacturing Execution System | es_ES |
dc.subject | Zero-defect Manufacturing | es_ES |
dc.subject | Manufacturing Operations | es_ES |
dc.subject | CMM | es_ES |
dc.subject | ISA-95 | es_ES |
dc.subject | MLOps | es_ES |
dc.subject | Machine Learning | es_ES |
dc.title | An industry maturity model for implementing Machine Learning operations in manufacturing | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.4995/ijpme.2023.19138 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/825631/EU/Zero-Defect Manufacturing Platform/ZDMP | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/958205/EU/Industrial Data Services for Quality Control in Smart Manufacturing/i4Q | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Centro de Investigación en Gestión e Ingeniería de Producción - Centre d'Investigació en Gestió i Enginyeria de Producció | es_ES |
dc.description.bibliographicCitation | Mateo Casalí, MA.; Fraile Gil, F.; Boza, A.; Nazarenko, A. (2023). An industry maturity model for implementing Machine Learning operations in manufacturing. International Journal of Production Management and Engineering. 11(2):179-186. https://doi.org/10.4995/ijpme.2023.19138 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.4995/ijpme.2023.19138 | es_ES |
dc.description.upvformatpinicio | 179 | es_ES |
dc.description.upvformatpfin | 186 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 11 | es_ES |
dc.description.issue | 2 | es_ES |
dc.identifier.eissn | 2340-4876 | |
dc.relation.pasarela | OJS\19138 | es_ES |
dc.contributor.funder | European Commission | es_ES |
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