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An industry maturity model for implementing Machine Learning operations in manufacturing

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An industry maturity model for implementing Machine Learning operations in manufacturing

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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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