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

Sang M. Lee (), DonHee Lee () and Youn Sung Kim ()
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Sang M. Lee: University of Nebraska-Lincoln
DonHee Lee: Inha University
Youn Sung Kim: Inha University

International Journal of Quality Innovation, 2019, vol. 5, issue 1, 1-11

Abstract: 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 real-world 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.

Keywords: Predictive maintenance; Quality management; Big data analytics; Artificial intelligence (AI); Platform construction; Information and communication technology (ICT); Real-time (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1186/s40887-019-0029-5

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