An industry maturity model for implementing Machine Learning operations in manufacturing

7Citations
Citations of this article
30Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Mateo-Casalí, M. Á., Gil, F. 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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free