Abstract
The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.
Highlights
Recent technological innovation is evolving rapidly due to emerging technologies such as artificial intelligence (AI), Internet of Things (IoT), cloud computing, machine learning (ML), big data, and the manufacturing industries [1,2,3,4]
Several CEOs of the manufacturing industries worldwide are thinking about implementFirst, we look at the performance measurement in Data-Driven Industry 4.0 and the Quality ing the Industry 4.0 concept and have many real-time questions that need to be addressed
We address the development by the American National Standard ANSI/ISA-95 of an automated interface among control systems and enterprise systems found in factories [19,30,31,32]
Summary
Recent technological innovation is evolving rapidly due to emerging technologies such as artificial intelligence (AI), Internet of Things (IoT), cloud computing, machine learning (ML), big data, and the manufacturing industries [1,2,3,4] These stage technologies permeate the production process to make the industry smart enough to address current challenges such as increased personalized requirements, increased quality, and reduced production cost. The key focus of Industry 4.0 is on emerging technology that will have a huge effect on production processes These innovations include virtual reality, 3D printing, simulation, big data analytics, cloud computing, radio frequency identification, Internet of Things, cybersecurity, machine-to-machine communication, robots, drones, nanotechnology, and business intelligence (BI) [14,15,16]. This paper explores the tools, methods, and industry standards used in smart factories to measure performance and manage quality It discusses Industry 4.0’s research challenges and opportunities.
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