Digital Twin definition
A digital twin is a virtual model of a physical object, process or system, such as a machine, building, supply chain or city, that is continuously updated with real data from its physical counterpart. Engineers use digital twins to monitor performance, simulate changes, predict failures and test decisions safely before applying them in the real world.
How does a digital twin work?
A digital twin has three parts: the physical asset, a virtual model of it and the data connection between them. The model can combine 3D geometry, physics-based simulation, business data and machine learning. Sensor data from IoT devices, plus records from systems such as ERP, maintenance and manufacturing execution systems, keep the model synchronized with reality, and insights flow back to operators or directly to control systems.
The concept is usually traced to Michael Grieves's work on product lifecycle management in the early 2000s, and NASA used the term for spacecraft modeling around 2010. Twins mature in stages: descriptive twins show what is happening, predictive twins forecast what will happen, and prescriptive twins recommend or automate what to do about it.
Types of digital twins
- Component twins: a single critical part, such as a bearing or battery cell.
- Asset twins: a complete machine, vehicle or turbine.
- System or process twins: a production line, plant or supply chain.
- Building twins: facilities built from BIM models plus live sensor data.
- City twins: transport, energy and infrastructure at urban scale.
- Product twins: a design tested virtually before physical prototypes exist.
- Patient flow twins in hospitals that test staffing and bed scenarios.
Examples of digital twins
Jet engine makers monitor engines in service against their twins to plan maintenance. Wind farm operators model each turbine to tune performance and predict component wear. Manufacturers simulate new production lines before installing equipment, using tools such as NVIDIA Omniverse and industrial simulation software, to find bottlenecks early. Building operators combine floor plans with occupancy and HVAC data to cut energy use, and Singapore created a detailed 3D city model for urban planning.
Digital twin vs simulation
A simulation models how a system could behave under assumed conditions, usually run as a study at a point in time. A digital twin is connected to a specific real asset and updated continuously with its data, so it reflects that asset's actual history and current state. Twins often run simulations internally, for example testing what would happen if a line ran faster, but starting from live conditions rather than assumptions.
How to build a digital twin
Start with one asset and one decision, such as when to service a compressor or how to schedule a bottleneck machine, rather than modeling an entire plant at once. Make sure the data exists and is trustworthy, choose the simplest model that supports the decision, and add fidelity only where it changes outcomes. Platforms such as Azure Digital Twins and open modeling languages like DTDL help structure the model, while Unity or Unreal can provide visualization.
Nexzem builds digital twins that connect IoT data, business systems and analytics, beginning with a focused pilot whose value can be measured before the model expands. Each phase adds data sources and fidelity only where the pilot shows a clear return.