An industry maturity model for implementing Machine Learning operations in manufacturing
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/164358 |
Resumo: | Funding Information: 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)”. Publisher Copyright: © 2023 The Author(s). |
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An industry maturity model for implementing Machine Learning operations in manufacturingCMMISA-95Machine LearningManufacturing Execution SystemManufacturing OperationsMLOpsZero-defect ManufacturingBusiness and International ManagementStrategy and ManagementManagement Science and Operations ResearchIndustrial and Manufacturing EngineeringSDG 9 - Industry, Innovation, and InfrastructureFunding Information: 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)”. Publisher Copyright: © 2023 The Author(s).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.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNMateo-Casalí, Miguel ÁngelGil, Francisco FraileBoza, AndrésNazarenko, Artem2024-03-02T00:26:26Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article8application/pdfhttp://hdl.handle.net/10362/164358eng2340-5317PURE: 84393923https://doi.org/10.4995/ijpme.2023.19138info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:52:04Zoai:run.unl.pt:10362/164358Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:00:09.418847Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An industry maturity model for implementing Machine Learning operations in manufacturing |
title |
An industry maturity model for implementing Machine Learning operations in manufacturing |
spellingShingle |
An industry maturity model for implementing Machine Learning operations in manufacturing Mateo-Casalí, Miguel Ángel CMM ISA-95 Machine Learning Manufacturing Execution System Manufacturing Operations MLOps Zero-defect Manufacturing Business and International Management Strategy and Management Management Science and Operations Research Industrial and Manufacturing Engineering SDG 9 - Industry, Innovation, and Infrastructure |
title_short |
An industry maturity model for implementing Machine Learning operations in manufacturing |
title_full |
An industry maturity model for implementing Machine Learning operations in manufacturing |
title_fullStr |
An industry maturity model for implementing Machine Learning operations in manufacturing |
title_full_unstemmed |
An industry maturity model for implementing Machine Learning operations in manufacturing |
title_sort |
An industry maturity model for implementing Machine Learning operations in manufacturing |
author |
Mateo-Casalí, Miguel Ángel |
author_facet |
Mateo-Casalí, Miguel Ángel Gil, Francisco Fraile Boza, Andrés Nazarenko, Artem |
author_role |
author |
author2 |
Gil, Francisco Fraile Boza, Andrés Nazarenko, Artem |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
DEE - Departamento de Engenharia Electrotécnica e de Computadores RUN |
dc.contributor.author.fl_str_mv |
Mateo-Casalí, Miguel Ángel Gil, Francisco Fraile Boza, Andrés Nazarenko, Artem |
dc.subject.por.fl_str_mv |
CMM ISA-95 Machine Learning Manufacturing Execution System Manufacturing Operations MLOps Zero-defect Manufacturing Business and International Management Strategy and Management Management Science and Operations Research Industrial and Manufacturing Engineering SDG 9 - Industry, Innovation, and Infrastructure |
topic |
CMM ISA-95 Machine Learning Manufacturing Execution System Manufacturing Operations MLOps Zero-defect Manufacturing Business and International Management Strategy and Management Management Science and Operations Research Industrial and Manufacturing Engineering SDG 9 - Industry, Innovation, and Infrastructure |
description |
Funding Information: 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)”. Publisher Copyright: © 2023 The Author(s). |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z 2024-03-02T00:26:26Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/164358 |
url |
http://hdl.handle.net/10362/164358 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2340-5317 PURE: 84393923 https://doi.org/10.4995/ijpme.2023.19138 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
8 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138177702690816 |