In recent years, the concept of Industry 4.0 has been gaining significant attention. As the fourth industrial revolution, Industry 4.0 builds upon the advancements of mechanization, electrical energy, and digitalization. This revolution is characterized by the integration of cyber-physical systems through the Internet of Things (IoT). The term Industry 4.0 was coined in 2011 by German manufacturing organizations, politicians, and academics who sought to develop strategies to support the German industry in embracing this new wave of technology.
Key Concepts of Industry 4.0
Researchers have identified four commonalities across Industry 4.0. The first is the concept of a smart factory, where intelligent processes are independently controlled and linked. The second is real-time communication between customers, organizations, and suppliers. The third concept focuses on smart products that can hold and analyze information. Lastly, Industry 4.0 emphasizes the ability for customers to communicate new orders and changes in real-time.
Digital Technologies in Manufacturing
The rise of Industry 4.0 has led to an increase in digital technologies in manufacturing organizations. This digitalization has resulted in networked, intelligent, and decentralized manufacturing processes. Key concepts within Industry 4.0 include the Internet of Things (IoT), simulation through computer models, and the integration of Information Technology (IT) both within and between organizations. Another significant aspect of Industry 4.0 is the utilization of big data analytics, which involves processing massive amounts of complex data collected by sensors.
The Role of Artificial Intelligence (AI)
Artificial Intelligence (AI) plays a crucial role in Industry 4.0. It encompasses both artificial general intelligence, which is comparable to human intelligence, and artificial narrow intelligence, referred to as machine learning. Machine learning utilizes algorithms to analyze big data and make predictions. The implementation of AI in Industry 4.0 enables the development of smart factories where cyber-physical systems communicate through the IoT.
Quality 4.0: Aligning Quality Management with Industry 4.0
Industry 4.0 has paved the way for Quality 4.0, which involves aligning quality management methods with the advancements of Industry 4.0. However, the implementation of Quality 4.0 in Small and Medium Enterprises (SMEs) poses challenges, as many lack the capability to adopt the necessary technologies. Quality 4.0 emphasizes the integration of traditional quality methods with information technologies to enhance product quality, customer satisfaction, and competitiveness.
Adapting to the Changes
As organizations transition to Quality 4.0, they must ensure they have the necessary infrastructure to support it. This includes systematized thinking supported by machine learning and AI, automated processes, and automated information collection using sensor networks. While traditional quality methods will still have relevance, qualified quality professionals must acquire new technical competencies to analyze large amounts of data and perform failure analysis based on statistics.
The Role of Data Scientists in Quality 4.0
Data scientists play a vital role in Quality 4.0. Their expertise in collecting, preparing, and analyzing data using statistical methods and programming languages such as Python and R is invaluable. Quality professionals should aim to bridge the gap between data scientists and their own skill set, leveraging the overlap in statistical knowledge and problem-solving abilities to thrive in the era of Quality 4.0.
The Importance of Planning for the Transition
The majority of organizations have yet to make a decision or implement Quality 4.0. However, it is crucial for organizations to plan for this transition. By embracing Quality 4.0, organizations can reduce quality costs, improve product quality, enhance customer satisfaction, and gain a competitive advantage. Qualified professionals must adapt their skills, learn to analyze massive amounts of data, and utilize statistical programming languages to successfully navigate this transition.
Conclusion
Industry 4.0 and its accompanying Quality 4.0 revolutionize the manufacturing landscape. The integration of cyber-physical systems, IoT, big data analytics, and AI bring about significant changes in quality management. While traditional quality methods retain their value, they must be integrated with information technologies to thrive in the era of Quality 4.0. Organizations must plan and invest in the necessary infrastructure, and qualified professionals must adapt and acquire new technical competencies to embrace the future of quality management.
Reference
Barsalou M. (2023). Root Cause Analysis in Quality 4.0: A Scoping Review of Current State and Perspectives. TEM Journal, 12(1), 73-79, DOI: 10.18421/TEM121-10.