Maturity Model for Analysis of Machine Learning Operations in Industry

被引:1
作者
Mateo-Casali, Miguel Angel [1 ]
Fraile, Francisco [1 ]
Boza, Andres [1 ]
Nazarenko, Artem [2 ,3 ]
机构
[1] Univ Politecn Valencia UPV, Res Ctr Prod Management & Engn CIGIP, Camino Vera S-N, Valencia 46022, Spain
[2] Nova Uniters Lisbon, Fac Sci & Technol, P-2829516 Lisbon, Portugal
[3] Nova Uniters Lisbon, UNINOVA, P-2829516 Lisbon, Portugal
来源
IOT AND DATA SCIENCE IN ENGINEERING MANAGEMENT | 2023年 / 160卷
关键词
Machine learning; Manufacturing execution system; Zero-defect manufacturing; Manufacturing operations; CMM; ISA-95; MLops;
D O I
10.1007/978-3-031-27915-7_57
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through 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 have a good understanding of 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.
引用
收藏
页码:321 / 328
页数:8
相关论文
共 16 条
  • [1] Barbuceanu M., 1993, INTEGRATED SUPPLY CH, P13
  • [2] QUALITY-CONTROL TECHNIQUES FOR ZERO DEFECTS
    CALVIN, TW
    [J]. IEEE TRANSACTIONS ON COMPONENTS HYBRIDS AND MANUFACTURING TECHNOLOGY, 1983, 6 (03): : 323 - 328
  • [3] El Naqa I, Machine Learning in Radiation Oncology, DOI [10.1007/978-3-319-18305-31, DOI 10.1007/978-3-319-18305-31]
  • [4] Escobar C.A., 2020, SAE TECHNICAL PAPER, V4, DOI [10.4271/2020-01-1302, DOI 10.4271/2020-01-1302]
  • [5] Finkelstein A., 1992, SIGSOFT Software Engineering Notes, V17, P22, DOI 10.1145/141874.141878
  • [6] Industry 4.0 technologies: Implementation patterns in manufacturing companies
    Frank, Alejandro German
    Dalenogare, Lucas Santos
    Ayala, Nestor Fabian
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2019, 210 : 15 - 26
  • [7] Galan Manuel J., 2021, 15 INT C IND ENG IND
  • [8] Johnsson C., 2004, TECHNICAL PAPERS ISA, V454, P399
  • [9] Junin Duran de Leon A., 2016, DESARROLLO SOFTWARE
  • [10] Jurgen K., 2007, MANUFACTURING EXECUT, DOI [10.1007/978-3-540-49744-8, DOI 10.1007/978-3-540-49744-8]