An industry maturity model for implementing Machine Learning operations in manufacturing
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...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2023-07-01
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| Series: | International Journal of Production Management and Engineering |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/IJPME/article/view/19138 |
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| _version_ | 1846092433418354688 |
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| author | Miguel Angel Mateo Casalí Francisco Fraile Gil Andrés Boza Artem Nazarenko |
| author_facet | Miguel Angel Mateo Casalí Francisco Fraile Gil Andrés Boza Artem Nazarenko |
| author_sort | Miguel Angel Mateo Casalí |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6fd1b4588e38402e9b2cc9b1cf2bc69d |
| institution | Kabale University |
| issn | 2340-4876 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | International Journal of Production Management and Engineering |
| spelling | doaj-art-6fd1b4588e38402e9b2cc9b1cf2bc69d2025-01-02T21:01:02ZengUniversitat Politècnica de ValènciaInternational Journal of Production Management and Engineering2340-48762023-07-0111217918610.4995/ijpme.2023.1913818330An industry maturity model for implementing Machine Learning operations in manufacturingMiguel Angel Mateo Casalí0https://orcid.org/0000-0001-5086-9378Francisco Fraile Gil1https://orcid.org/0000-0003-0852-8953Andrés Boza2https://orcid.org/0000-0002-5429-0416Artem Nazarenko3https://orcid.org/0000-0003-2435-3970Universitat Politècnica de ValènciaUniversitat Politècnica de València Universitat Politècnica de València Nova University of LisbonThe 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.https://polipapers.upv.es/index.php/IJPME/article/view/19138manufacturing execution systemzero-defect manufacturingmanufacturing operationscmmisa-95mlopsmachine learning |
| spellingShingle | Miguel Angel Mateo Casalí Francisco Fraile Gil Andrés Boza Artem Nazarenko An industry maturity model for implementing Machine Learning operations in manufacturing International Journal of Production Management and Engineering manufacturing execution system zero-defect manufacturing manufacturing operations cmm isa-95 mlops machine learning |
| title | 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_short | An industry maturity model for implementing Machine Learning operations in manufacturing |
| title_sort | industry maturity model for implementing machine learning operations in manufacturing |
| topic | manufacturing execution system zero-defect manufacturing manufacturing operations cmm isa-95 mlops machine learning |
| url | https://polipapers.upv.es/index.php/IJPME/article/view/19138 |
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