Article: Lee, S. M., Lee, D., & Kim, Y. S. (2019). The quality management ecosystem for predictive maintenance in the industry 4.0 era. International Journal of Quality Innovation, 5(1), 1-11.
Introduction
Quality management is a vital process of the organization’s workflow that controls and supervises the quality of operations and activities. However, since the technologies rapidly advance, experts must adjust their approaches to quality management. Lee et al. (2019) discuss the implications of the innovation shift on various quality management methodologies and how big data analytics might affect the established frameworks in this field. Ultimately, the current summarizing report provides an overview of the article on quality management in the 4.0 industry era.
Topic Background
The authors initiate the discussion by emphasizing the necessity of businesses to adjust to the current realities of quality management in the era of big data analytics, neural networks, and artificial intelligence. According to Lee et al. (2019), the companies must create their personal quality management ecosystems that would utilize innovative methods to enhance the quality of operations, manufacturing, and control processes. It is the necessary step in quality management in the information age, and if the companies fail to adjust, they might lose their competitive advantage. Moreover, the authors mention that innovative approaches improve the relationships between the vital stakeholders, implying that customers might eventually lose their faith in the companies that do not adapt to new models of operations and quality management (Lee et al., 2019). To prove this point, the authors discuss the importance of predictive maintenance.
Predictive Maintenance
Predictive maintenance in quality management is the focal point of the article as the authors attempt to prove its effectiveness and positive impact on business. In general, this concept refers to the condition-based assessment of the assets’ working capabilities based on innovative technologies, such as smart sensors and big data analytics (Lee et al., 2019). In other words, this framework might effectively predict the upcoming failures of equipment, allowing the quality managers to respond to the problems faster. Extensive academic research is devoted to this topic; for instance, Çınar et al. (2020) discuss the role of machine learning in predictive maintenance. Sakib and Wuest (2018) analyze the various obstacles to this approach and the potential mitigation strategies. However, the examined article by Lee et al. (2019) is significant in the scope of the current assignment since the authors discuss the implications of predictive maintenance in quality management specifically. As a result, it is crucial to summarize the article and highlight the research outcomes.
Case Descriptions
The authors thoroughly examine predictive maintenance in quality management in the processes of five relevant corporations – Rolls-Royce, Hyundai Motors, BOSCH, John Deere, and Clova. The former four organizations have implemented innovative methods of quality management in their manufacturing operations, while Clova attempted to utilize predictive maintenance in public services (Lee et al., 2019). The authors have found that Rolls-Royce uses this approach in the quality management of aircraft engine maintenance via smart sensors and robotics (Lee et al., 2019). This methodology allows the company to identify any failures in the engines, which are crucial for aircraft safety. Similarly, Hyundai Motors utilizes AI and Knock Sensor Detection System (KDS) to identify abnormalities in engines (Lee et al., 2019). According to the analysis, the accuracy of AI predictions is approximately ten times higher than the analysis of working professionals (Lee et al., 2019). These examples demonstrate the utmost effectiveness of predictive maintenance in quality management.
The authors applied a similar analysis process to BOSCH and John Deere. The former uses Nexeed Production Performance Manager (NPPM) as a visualization tool to collect internal and external information for the consequent usage in quality management (Lee et al., 2019). John Deere utilizes SAP’s Predictive Maintenance and Service (PdMS) based on big data analytics to identify abnormalities (Lee et al., 2019). Lastly, Clova implements a unique approach to maintenance using a network-in-time (NIT) framework that analyzes information in real-time (Lee et al., 2019). Having examined the five cases, the authors conclude that innovative technologies are critical in quality management in the information age.
Results and Discussion
The authors have analyzed predictive maintenance in quality management and identified several distinct outcomes. First, they comment that innovative technologies are paramount in quality management, and the companies must adjust to the present realities. Consequently, they report that data analysts are vital stakeholders in this process. Third, the authors are confident that it is crucial to change the quality management frameworks with an emphasis on AI-supported ecosystems that enable a more productive workflow. Another critical conclusion states, “Quality management in the past was performed by data-driven decision-making, but currently, evidence-based decision-making has become more important” (Lee et al., 2019, p. 9). In other words, the authors are confident that quality management in the information age should be conducted in real-time as it allows for increased productivity and reduced downtimes in the processes. Moreover, this approach is only possible by utilizing contemporary instruments, such as big data analytics, machine learning, and AI. Ultimately, the examined article has thoroughly analyzed the role of predictive maintenance in quality management, evaluated the requirements for methodology implementation, and proposed a framework for quality management ecosystems.
References
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211.
Lee, S. M., Lee, D., & Kim, Y. S. (2019). The quality management ecosystem for predictive maintenance in the industry 4.0 era. International Journal of Quality Innovation, 5(1), 1-11.
Sakib, N., & Wuest, T. (2018). Challenges and opportunities of condition-based predictive maintenance: A review. Procedia Cirp, 78, 267-272.