An industry maturity model for implementing Machine Learning operations in manufacturing
Submitted: 2023-01-19
|Accepted: 2023-07-20
|Published: 2023-07-31
Copyright (c) 2023 Miguel Angel Mateo Casalí, Francisco Fraile Gil, Andrés Boza, Artem Nazarenko
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Downloads
Keywords:
Manufacturing Execution System, Zero-defect Manufacturing, Manufacturing Operations, CMM, ISA-95, MLOps, Machine Learning
Supporting agencies:
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.
References:
Escobar, C., Arinez, J., & Morales-Menéndez, R. (2020). Process-Monitoring-for-Quality-A Step Forward in the Zero. https://doi.org/10.4271/2020-01-1302
Naqa, I., Li, R., & Murphy, M. J. (Eds.). (2015). Machine Learning in Radiation Oncology. Springer International Publishing. https://doi.org/10.1007/978-3-319-18305-3
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210(September 2018), 15-26. https://doi.org/10.1016/j.ijpe.2019.01.004
Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. Fifteenth International Conference on ICT and Knowledge Engineering, 1-6. https://doi.org/10.1109/ICTKE.2017.8259629
Psarommatis, F., Prouvost, S., May, G., & Kiritsis, D. (2020). Product Quality Improvement Policies in Industry 4.0: Characteristics, Enabling Factors, Barriers, and Evolution Toward Zero Defect Manufacturing. Frontiers in Computer Science, 2(August), 1-15. https://doi.org/10.3389/fcomp.2020.00026
Serrano-Ruiz, J. C., Mula, J., & Poler, R. (2021). Smart manufacturing scheduling: A literature review. Journal of Manufacturing Systems, 61, 265-287. https://doi.org/10.1016/j.jmsy.2021.09.011
Lindström, J., Kyösti, P., Birk, W., & Lejon, E. (2020). An initial model for zero defect manufacturing. Applied Sciences (Switzerland), 10(13). https://doi.org/10.3390/app10134570
Calvin, T. W. (1983). Quality Control Techniques for 'Zero Defects'. Technical Paper - Society of Manufacturing Engineers, C(3), 323-328. https://doi.org/10.1016/0026-2714(84)90075-1
Jürgen, K. (2007). Manufacturing Execution System - MES.
Fox, M. S., Chionglo, J. F., & Barbuceanu, M. (1993). The Integrated Supply Chain Management Project. Retrieved from https://www.researchgate.net/publication/239033756_The_Integrated_Supply_Chain_Management_Project
Unver, H. O. (2013). An ISA-95-based manufacturing intelligence system in support of lean initiatives. International Journal of Advanced Manufacturing Technology, 65(5-8), 853-866. https://doi.org/10.1007/s00170-012-4223-z
Junín Durán de Leon, A., Cruz Rentería, J. R., Muñoz Zamora, G., García-Alva, S., Gutiérrez-torres, L., & Sánchez Hernández, Z. (2016). Desarrollo de Software basado en el estándar ISA-95.
Finkelstein, A. (1992). A Software Process Immaturity Model, ACM SIGSOFT Software Engineering Notes, 17(4), 22-23. https://doi.org/10.1145/141874.141878
Galan Manuel, J., de la fuente, S., Alonso de Armiño, C., & Alcalde Delgado, R. (2021). 15th International Conference on Industrial Engineering and Industrial Management. Retrieved from https://pressbooks.pub/cioxxv
