A digital twin is a virtual model of something in the physical world, such as a product (a car), a process (a production line), or a facility (a processing plant). Digital twins are frequently referred to as bridges between the physical and the digital.
What makes a digital twin different from a drawing, schematic, or other more traditional representation is that it’s dynamic. Digital twins don’t just represent the physical system itself, they also represent all of the information embedded in that physical system. As such, they change in response to contextual information, which means you can use them to assess how changing contexts (e.g., different inputs, environmental factors, the wear and tear of aging) will affect the physical product.
Another crucial aspect that sets a digital twin apart from drawings and schematics is that it allows stakeholders across the organization to have access to the same real-time picture of the physical asset. You don’t have to worry that one department is working with version 9 and another with version 10. Your digital twin is always up to date.
Here’s one perspective from Chris O’Connor, the general manager of IBM Watson. As he explains in this YouTube video, a digital twin can help you optimize a project across its entire life cycle:
- During the engineering phase, a digital twin allows engineers to test different designs and expose them to different contexts in advance. This means they can find any inefficiencies, mistakes, and other opportunities for improvement before any time, energy, or money is spent on production.
- During the build phase, a digital twin helps you improve efficiency, quality, and yield, for example, by understanding the effect of a production change.
- Finally, digital twins also facilitate the operations phase. Products, plants, and processes age, and as they age, things drift. A digital twin can drift as well, which helps you understand, adapt to, and correct the changes happening in the real world (e.g., planning maintenance schedules).
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