Any new technology brings new definitions. But to replace an existing technology, it also has to bring business or consumer value. Consider digital twins. How does a digital twin add value over existing monitoring and sensing systems, for example? Throw in new internet of things sensors and the definitions and use cases are even more complicated.
Most definitions of digital twins involve the virtual representation of some real-world process. But that might be the only thing observers agree upon.
If we assume the value of IoT is the ability to know what is happening, in real time, with processes and machines, we can understand the difference between real-time monitoring and non-real-time simulations, for example. The former tells you what is happening; the latter what could happen.
Most proponents of digital twins emphasize its value over a “simulation.”
“A simulation replicates what could happen to a product, but a digital twin replicates what is happening to an actual specific product in the real world,” says TWI Global. “A digital twin creates a virtual environment able to study several simulations, backed up with real-time data and a two-way flow of information between the twin and the sensors that collect this data.”
But we might also say a digital twin is a three-dimensional representation of “what is happening.” In that sense it is closer to the idea of a monitoring or sensing system able to fully represent many real-world conditions.
Some might say that “simulations refer to digital models that imitate the operations or processes within a system.” Others might emphasize the more-static nature of a simulation, versus dynamic nature of a twin.
But even those definitions are fuzzier than one might think. Digital twins can also be used for simulations, albeit with more realism and dynamism, even if most observers say the primary value is operations visibility. For some enterprises, the issue might be the amount of incremental value from a digital twin, compared to a robust IoT monitoring system.
Others might focus on the difference between “intent” and “status.”
“Simulations are created by the imagination of the designer, who will use them to analyze the cause and effect of different scenarios, mostly offline,” says Ericsson, also noting that the variety of definitions is “overwhelming.”
For example, a digital twin can be defined as software representation of assets and processes, which is enhanced with capabilities that are not present in the real-world entity. A digital twin “is a virtual copy of something, and processes associated with it, that can be used to predict or interact with reality,” Ericsson also says.
Again, note the complexity of the concept. A digital twin is a virtual copy of something. But its uses can range from monitoring to simulation to process control.
Some might argue that, in most cases, a simulation is a snapshot of a one-time task, which is then used to support design and analysis studies. But a digital twin might also support real-time simulations, in addition to monitoring and process control.
Digital twins, on the other hand, are driven by timely synchronization of information between the real and virtual worlds, and therefore adapt with changes in either world. Simulations are predominantly theoretical, whereas digital twins are specific and actual, some might say.
Similarly, digital twins share some similarities with metaverses: digital spaces where people can interact virtually. But metaverses are built intentionally, typically representing virtual realms.
Digital twins only have value as replicas of the real world. That is about the only definition we might all agree upon. The value and use cases will be matters of interpretation.
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