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Digital twins enable the simulation and analysis of physical systems in a digital environment. They assist companies in optimizing processes, generating predictions, and improving decision-making. By utilizing real-time data, they provide deeper insights and help reduce costs.
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A digital twin is a digital model of a real or planned object, process, or system that remains connected to its counterpart through data.
The idea grew from modeling, simulation, and industrial computing. With networked sensors and IoT it became a continuously updated representation of physical systems; industrial practice helped spread the term.
Think of a model with a return channel: state data flows in, simulations and forecasts flow out. A twin is only as useful as its data quality, freshness, and chosen boundary.
The real or planned object represented by the digital model.
The connection through which state data updates the model.
Trying possible states or interventions in the model.
Digital twins support monitoring, maintenance, and planning by exposing consequences before an intervention. They do not justify false precision: model boundaries and data gaps must remain visible.
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