Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 1; p. 224
• Main Authors: Tambare, Parkash, Meshram, Chandrashekhar, Lee, Cheng-Chi, Ramteke, Rakesh Jagdish, Imoize, Agbotiname Lucky
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.12.2021; MDPI
• Subjects: Automation; Autonomous Vehicles; Big Data; Blockchain; cyber–physical production system; Factories; Industry; Industry 4.0; Internet of Things; Manufacturing; Performance evaluation; performance measurement system; Quality 4.0; Review; Technology; Industry 4.0; performance measurement system; Internet of Things; cyber–physical production system; Quality 4.0
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22010224

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.