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TopIndustry 4.0, Iot And Ai
Digital twins have become a core component of Industry 4.0; they enable firms to effectively understand their data, optimise complex processes and facilitate operational optimisation (Aitken, n.d.). MacDougal (2014) defines Industry 4.0 as the technological evolution from embedded systems to cyber-physical systems. These cyber-physical systems (CPS) merge digital and real-time workflows and are key components of Industry 4.0 (Farsi, et al., 2018). Production technologies and smart production processes have embedded systems and sensors that encourage optimum performance of the systems; leading to the transformation of industries like production value chains as well as business models (Mohamed, 2018).
IoT sensors are attached to assets to provide firms access to huge amounts of data; this data forms the basis of DT (Atlam & Wills, 2020). IoT allows connectivity of machines and devices capable of interacting with each other (Hosseinian-Far, et al., 2017). Digital Twins consist of connected products, using IoT and a digital thread which provides connectivity through the system’s lifecycle; it also gathers data from the physical twin to update the models in the Digital Twin (Farsi, et al., 2020).
IoT encourages supervision of products in real time and facilitates communication between the CPS and users. By utilising the 4v’s i.e.; Volume, Veracity, Velocity and Value of the data captured, Internet of Services (IoS) which provides services through the Internet is effectively achieved (Hosseinian-far, et al., 2017). Digital twins were initially introduced with increasing volume of data affecting the IoT. Merging DT with IoT provides the needed data to gain understanding of the behaviour and performance of the physical twin in the operational environment (Kaur, et al., 2020). Gartner (2014) forecasted IoT to reach 26 billion units by 2020, up from 0.9 billion in 2009. The benefits of the IoT can be expanded through its integration with multiple DTs, each monitored from a central location that manages maintenance schedules and cycles (Mohammadi & Taylor, 2017).