Digital twin: A scoping review, challenges and open research

Digital Twin technology is an emerging concept that has become the center of attention for industry and, in more recent years, academia. A digital twin is a digital representation of objects, people, or processes, whether real or intended. Digital twins utilize real-time data, simulation, machine learning, and reasoning to simulate real situations, demonstrating possible outcomes so organizations can make better decisions. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The Digital Twin has the potential to give real-time status on machines performance as well as production line feedback. It gives the manufacturer the ability to predict issues sooner. Digital Twin use increases connectivity and feedback between devices, in turn, improving reliability and performance. AI algorithms coupled Digital Twins have the potential for greater accuracy as the machine can hold large amounts of data, needed for performance and prediction analysis. The Digital Twin is creating an environment to test products as well as a system that acts on real- time data, within a manufacturing setting this has the potential to be a hugely valuable asset. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins will be presented. This paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins. The purpose of this paper is to do a literature review and explore how Digital Twins streamline intelligent automation in different industries. This paper defines the concept, highlights the evolution and development of Digital Twins, reviews its key enabling technologies, examines its trends and challenges, and explores its applications in different industries.