Smart process manufacturing is an integral and growing part of modern industrial engineering;it may be most readily characterized as a domain at the intersection of traditional manufacturing and modern, technologically intensive capabilities that address a dynamic and global market. It has long been a goal to reduce the operating cost of the manufacturing process, in addition to improving quality [1] and ensuring timely delivery (traditionally referred to as "quality, cost, delivery" (QCD) [2]). Although QCD has been an outstanding initiative and an evolving management approach for manufacturing industries, new, technologically intensive enablers such as the Internet of Things (IOT), big data analytics, the integration of cyber–physical systems (e.g., digital twins), the use of virtual, augmented, and mixed reality, and the potential for the utilization of artificial intelligence (AI) [3] come together to make these goals achievable on shorter timescales, with greater reliability and flexibility. Product development innovation cycles are increasingly shorter as a result, allowing products to enter the marketplace faster and providing a competitive edge for early-adapting industries. What these technologies actually enable is the ability to efficiently analyze the manufacturing environment, optimize processes faster, and facilitate decision-making based on the accumulated information [4], hence reducing operating risks. This information may then be utilized through a robust enterprise resource planning (ERP) system to close the loop and progressively automate self-optimization of the manufacturing environment.
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