By Sang M. Lee, DonHee Lee, & Youn Sung Kim
The Fourth Industrial Revolution, sometimes called Industry 4.0 or 4IR, originated from the German government’s high-tech strategy for developing smart manufacturing. At the core of Industry 4.0 is the convergence of advanced digital and other technologies through connectivity of operational systems for automatic processing and control of manufacturing activities. Its purpose is to enable organizations to effectively encounter the increasing complexity of the business environment resulting from globalization, digitalization, and many other disruptive market forces.
The concept of Industry 4.0 has spread beyond manufacturing to service industries, nonprofit organizations, and even governments. Today, organizations strive to develop dynamic capabilities through agility, flexibility, resilience, and adaptability. Many advanced digital technologies, such as artificial intelligence (AI), machine learning, big data analytics, the Internet of Things (IoT), cloud computing, augmented reality, and cybersecurity, support Industry 4.0.
An important part of Industry 4.0 is a new approach to quality management. As AI-enabled smart sensors and machine learning are now widely applied in manufacturing and service industries, Quality Management 4.0 (QM 4.0) should be implemented. QM 4.0 focuses on predictive rather than preventive maintenance.
In our paper titled "The quality management ecosystem for predictive maintenance in the Industry 4.0 era," recently published in the International Journal of Quality Innovation, we performed an extensive review of the literature and analysis of various real-world cases to develop a quality management ecosystem that is enabled by advanced digital technologies.
The results of our study led to the following conclusions. First, for effective predictive maintenance in the Industry 4.0 era, advanced digital technologies need to be applied to enhance productivity and value creation. Second, while application of big data analytics provides real-time information, experts who can control and make decisions on operations and quality management need policy support. Third, for predictive maintenance for quality management, implementation methods should be proposed through analyses of field ecosystems, cause-effect measurements, and expected outcomes. Fourth, in addition to applying digital tools, the introduction of blockchain technology has great potential for enhancing the effectiveness of predictive maintenance for quality management.
Sang M. Lee is University Eminent Scholar Emeritus at the University of University of Nebraska–Lincoln (USA). DonHee Lee and Youn Sung Kim are members of the Department of Business Administration at Inha University (South Korea).