Digital twins are digitized representations of real-life objects, systems, or processes. They are typically used to monitor equipment throughout their whole lifecycle, from production to maintenance, and can provide valuable insight on how something can be implemented into the real world. Using artificial intelligence and real-world data, they can create scenarios, test possibilities, and predict possible outcomes, giving engineers and manufacturers valuable insight into current state operation as well as possible future iterations. Where a simulation typically replicates a single scenario or process, a twin can run multiple simulations simultaneously, studying various processes at scale. (1) Thus, it is a more thorough and sophisticated method for documenting knowledge and insight, as all resulting information can be tracked, traced, and referenced at later times.
Digital twins can manifest themselves in two distinct ways. First, they can simply consist of performance data, funneled from a real-world environment, product, or system; said data can be shown on a dashboard, for example, and then analyzed to draw conclusions about how a particular part, or the entirety of the system, is functioning. But, they can also consist of a literal digital replica of said real-world environment, product, or system. This 3D asset can be used for testing, prototyping, integration, and simulation, without impacting its real-world counterpart. Whatever the shape, digital twins usually make the life of engineers, maintenance workers, and general staff a lot easier. 🙌
While digital twins can offer profitable advantages, there are challenges to getting the technology off the ground and managing it through its lifecycle phases.
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