Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM)

• Published in: International journal of production research Vol. 61; no. 12; pp. 4004 - 4021
• Main Authors: Bi, Zhuming, Jin, Yan, Maropoulos, Paul, Zhang, Wen-Jun, Wang, Lihui
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 18.06.2023; Taylor & Francis LLC
• Subjects: Adaptability; Advanced Analytics; Big Data; big data analytics (BDA); Complexity; cyber-physical systems (CPSs); Data analysis; Data Analytics; Data collection; Data handling; Decision making; Decision making support; Digital manufacturing; digital manufacturing (DM); Enterprise Architecture; enterprise architecture (EA); Enterprise information system; Entropy (Information theory); Flexibility; Globalization; Historical development; Industrial research; Information systems; Information use; Internet of Things; Internet of things (IoT); Manufacture; Manufacturing; Manufacturing applications; Manufacturing data processing; Manufacturing technologies; Resilience; Sustainable manufacturing; System stability; Systems stability
• DOI: 10.1080/00207543.2021.1953181
• ISSN: 0020-7543; 1366-588X

This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective.