An industry maturity model for implementing Machine Learning operations in manufacturing

Detalhes bibliográficos
Autor(a) principal: Mateo-Casalí, Miguel Ángel
Data de Publicação: 2023
Outros Autores: Gil, Francisco Fraile, Boza, Andrés, Nazarenko, Artem
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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spelling 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
repository.mail.fl_str_mv
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