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There are many DT definitions on the internet. Even companies that implement this technology define their own definition of DT. However, DT can be simply defined as a virtual real-time representation of a physical system to support decision-making. The term Digital Twin (DT) has become a buzzword, in particular when people talk about digitalization or digital transformation. The idea of twinning dates back to NASA’s Apollo 13 program, however, DT began gaining extensive attention due to the worldwide initiatives toward digital transformation such as “Industrie 4.0” in Germany, “Smart Manufacturing” in the US, and “Made in China 2025”. Although different countries use different terminologies, DT is seen as a prerequisite to achieving the goal which is to create substantial impact across the value chain (or activities by which a company adds value including production, marketing, and after-sales service).
Source: MACS
DT building blocks are made of different digital technologies like Internet of things (IoT), Cloud Computing, Artificial Intelligent (AI), simulation technologies, networking technologies, and advanced visualization technologies like Augmented Reality and Virtual Reality. All these technologies are integrated to form the functions of DT like sensing the condition or status of its physical counterpart, detecting the change of its performance and upcoming risk as well as demand and supply, simulating what-if scenario for generating insights, and controlling the or giving feedback to improve the operation of the physical system.
Boeing is using DT to change the way they design their aircraft. In their design process, a virtual replica of an aircraft can go through a simulated lifecycle of the environment and conditions that the aircraft will experience to identify which component will fail. The simulated lifecycle is created by digitally threading the data from in-service product lifecycle. Boeing CEO Dennis Muilenburg added that the use of DT has led to 40% improvement in the quality of parts and systems used in aircraft production.
Unilever is using IoT and intelligent edge services in the Azure IoT platform to create its Digital Twin of the Unilever Factory. Unilever DT factory contains a virtual representation of every machine and processes which a large variety of data from the factory can be collected to fed into. The algorithm in DT can then analyse the data and simulate the optimum parameters for process control to improve the overall manufacturing process. This generated algorithm can then be employed to control manufacturing operations to ensure the consistency like in a soap making and increase the production speed such as detergent or shampoo. This technology has brought significant benefits to Unilever by reducing 3000 alerts that requiring actions every day by 90%.
General Electric has used DT to predict, diagnose, forecast, and prevent equipment failures for components (pumps or compressors), critical assets (turbines), or systems of an asset (an entire power station) more proactively. Through DT, data coming from physical sensors are analysed together with the data from the virtual sensors (digital models) to increase the accuracy of the asset monitoring, structure analysis, and remaining useful life. According to GE, they have successfully used DT to detect failure early, and thus, enable maintenance planning more proactively to reduce downtime which has led to $1.6 B cost saving to date.
DT has also been used by several countries such as Singapore, China, and Australia to construct a virtual representation of a city. This DT will serve as single source of truth that can be used by many parties to improve road infrastructure, traffic flow, policy planning, urban planning, etc. By having DT of the city, decision makers can assess the impact of the changes before they are implemented without affecting infrastructure, buildings, and citizen. For instance, the city state of Singapore is using DT to gain insights into how ambient temperature and sunlight throughout the day which is useful for urban planners to visualise the effects of constructing new buildings or installation. Furthermore, DT can also be used to visualize which buildings that have a higher potential for solar energy generation through integrated information such as height of buildings, surface of the rooftops, and amount of sunlight available.
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A digital twin is a virtual representation of a physical object, system, or process. It combines real-world data and simulations to create a digital counterpart that mirrors its physical counterpart.
Digital Twins work by collecting data from sensors and real-world objects and using that data to create a virtual replica. This virtual model can then be used for analysis, monitoring, and optimization.
Digital Twins are used in various industries, including manufacturing, healthcare, construction, and IoT, for tasks such as predictive maintenance, process optimization, and design validation.
Technologies such as IoT, AI, cloud computing, simulation, and visualization tools are commonly used to create and support Digital Twins.
Benefits include improved operational efficiency, reduced downtime, enhanced product design, better decision-making, and more effective maintenance strategies.
Yes, Digital Twins are versatile and can be applied to a wide range of industries, from manufacturing and aerospace to healthcare and urban planning.
Yes, security and data privacy are important considerations when implementing Digital Twins, as they involve collecting and analyzing sensitive data from physical assets.
Digital Twins are dynamic and real-time representations of physical objects, whereas 3D models or simulations are typically static representations used for specific tasks, such as design or training.
The future of Digital Twins looks promising, with continued advancements in technology and their adoption in various industries. They are expected to play a significant role in the development of smart cities and Industry 4.0.
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