The quality management ecosystem for predictive maintenance in the Industry 4.0 era

Authors

Sang M. Lee, University of Nebraska - LincolnFollow
DonHee Lee, Inha University, Incheon, South Korea
Youn Sung Kim, Inha University, Incheon, South Korea

ORCID IDs

Sang Lee http://orcid.org/0000-0003-3754-6077

DonHee Lee http://orcid.org/0000-0003-2799-8547

Date of this Version

2019

Citation

International Journal of Quality Innovation (2019) 5:4

https://doi.org/10.1186/s40887-019-0029-5

Comments

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

Abstract

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five realworld cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction. Such predictive quality management systems can become living ecosystems that can perform cause-effect analysis, big data monitoring and analytics, and effective decision-making in real time. This study proposes several practical implications for actual design and implementation of effective predictive quality management systems in the Industry 4.0 era. However, the living predictive quality management ecosystem should be the product of the organizational culture that nurtures collaborative efforts of all stakeholders, sharing of information, and co-creation of shared goals.