Abstract
In this chapter, the authors intend to discuss Industry 5.0 and the emerging changes in quality management, in its planning, control and improvement components. It is intended to clarify that, in the context of smart manufacturing systems, quality management requires new approaches in monitoring and controlling manufacturing processes. This paradigm shift requires new skills, one of the most important of which is predictive quality. There is a gap between the development of theories, approaches, concepts and philosophies and their application in a real context. From this perspective, the chapter aims to present predictive quality as a contribution to the integration of creativity and human skills in improving manufacturing processes.
TopIntroduction
Several authors, like Lee et al. (2013), noticed during the 1990’s and 2010’s an extraordinary growth in the development and adoption of information technology and social media networks that have vastly influenced consumers’ perception on product innovation, quality, variety, and speed of delivery. They also reported that the commoditization of products has driven prices down creating a fierce competitive environment, and shorter product lifecycles from design to obsolescence have compelled manufacturers to reassess their adopted manufacturing paradigm in order to rapidly fulfill demand and regain market share. Especially, in the last two decades, there has been an extraordinary growth in the development and adoption of information technologies and social networks that have enormously influenced consumers' perception of product innovation, quality, variety, and delivery times. Furthermore, the commoditization of products has driven down the prices, creating a fierce competitive environment, and the shortening of product life cycles, from conception to obsolescence, has forced manufacturers to reevaluate their production paradigm in order to satisfy quickly increase demand and regain market share. Industry 4.0 (I4.0), a German government initiative in 2011, promoted the implementation of new technologies and the creation of cyber-physical systems composed of intelligent resources that communicate with each other (Xu et al., 2021; Saniuk & Grabowska, 2024). These systems consist in the combination of a software component with mechanical or electronic parts. Control, monitoring, and data transfer are generally carried out via the internet in real time. As in the three previous industrial revolutions, I4.0 results from many technological complementarities and allows for many innovations, as well as the cooperation of companies of all sizes in this digital transformation (Klingenberg et al., 2022).
Lee et al. (2013) anticipated that these conditions paved the establishment of reconfigurable manufacturing approach wherein a plant structure (including the productions systems and associated software) can be changed in a short time so that production capacity can ramp up and functionality can adapt more rapidly. From the perspective of more technology in processes, Klingenberg et al. (2022) point out that the increase in inequality in human resources in the face of the advantages of technology could bring social instability and a negative attitude towards new technologies, which could reduce the pace of their diffusion.
More recently, the European Commission has been promoting Industry 5.0 (I5.0) since 2021, centered on three interconnected fundamental values (Commission et al., 2021): human-centricity, sustainability, and resilience. Saniuk & Grabowska (2024) argue that the removal of human resources and the dehumanization of cyber-physical systems led to the emergence of the concept of industry 5.0. Industry 5.0 is centered on values, which distinguishes it from Industry 4.0, considered technology-oriented (Xu et al., 2021).
Key Terms in this Chapter
Smart Factory: is a digitized manufacturing facility that utilizes interconnected devices, machinery, and production systems to continuously collect and share data.
Industry 5.0: Also known as the Fifth Industrial Revolution, is a forward-thinking vision that goes beyond mere efficiency and productivity in industrial processes and emphasizes collaboration between humans and advanced technology. Unlike previous industrial revolutions, it places the wellbeing of workers at the center of production, aiming for prosperity, sustainability, and resilience.
Big Data: refers to large and complex data sets that cannot be effectively managed, processed, or analyzed using traditional methods. The three “Vs” of Big Data are Volume, Velocity and Variety.
Quality Management: refers to the systematic processes, practices, and methodologies employed by organizations to ensure that their products, services, or processes consistently meet or exceed established quality standards.
Predictive Quality: is the application of advanced data analysis, machine learning, and statistical modeling techniques to predict quality issues.
Quality 4.0: is a concept that emerges within the context of the Industry 4.0 and leverages technology to improve an organization’s quality, its products, services, and overall outcomes.
Process Monitoring: The primary goal of process monitoring is to ensure optimal performance, consistent quality, and efficient output throughout the entire manufacturing process. The three key aspects are real-time assessment, process variables and quality feedback.