An industry maturity model for implementing Machine Learning operations in manufacturing

Authors

DOI:

https://doi.org/10.4995/ijpme.2023.19138

Keywords:

Manufacturing Execution System, Zero-defect Manufacturing, Manufacturing Operations, CMM, ISA-95, MLOps, Machine Learning

Abstract

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.

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Author Biographies

Miguel Angel Mateo Casalí, Universitat Politècnica de València

Centro de Investigación en Gestión e Ingeniería de Producción (CIGIP)

Francisco Fraile Gil, Universitat Politècnica de València

Centro de Investigación en Gestión e Ingeniería de Producción (CIGIP)

Andrés Boza, Universitat Politècnica de València

Centro de Investigación en Gestión e Ingeniería de Producción (CIGIP)

Artem Nazarenko, Nova University of Lisbon

Faculty of Sciences and Technology

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Published

2023-07-31

How to Cite

Mateo Casalí, M. A., 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

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