Digital twins in industry
A clear-eyed look at industrial digital twins — what the term really means, the levels of fidelity, and where they deliver value versus hype.
What a digital twin actually is
A digital twin is a living digital model of a physical asset, process or system, kept in sync with the real thing through live data. The phrase is overused — a static 3D model is not a twin, and neither is a one-off simulation. What makes it a twin is the connection to operating data so the model reflects current reality and can be used to monitor, analyse or predict.
Levels of fidelity
Twins exist on a spectrum, and most value is captured at the lower, cheaper levels:
- Descriptive — a connected model that shows current state across data sources in context.
- Diagnostic — adds analytics to explain why something is happening.
- Predictive — forecasts future behaviour, such as remaining useful life or performance under a change.
- Prescriptive — recommends or automates actions.
Climbing the levels costs more and needs better data. Many successful projects stop at descriptive or diagnostic because that is where the payback is clearest.
Where twins earn their keep
- Operations decision support — a single contextual view of an asset for faster, better operator decisions.
- Performance and efficiency — comparing live behaviour to an expected model to find losses.
- Predictive maintenance — physics- or data-driven models of asset health.
- Scenario and what-if analysis — testing changes safely before touching the real plant.
- Training and knowledge capture — preserving expertise as experienced staff retire.
The data foundation
The hard part of a digital twin is rarely the model — it is the data. Industrial data sits in historians, control systems, ERP, maintenance systems, engineering documents and 3D models, often poorly connected. Contextualising that data so an asset's tags, drawings, history and live signals are linked is the real work, and it is why industrial DataOps platforms exist. Without that foundation, a twin is a demo that never reaches production. Start from a concrete decision the twin should improve, build only the data and fidelity that decision needs, and grow from there.
Frequently asked questions
What is a digital twin in simple terms?
A living digital model of a physical asset or process that stays in sync with the real thing through live data, so it can be used to monitor, diagnose, predict or optimise.
Is a 3D model a digital twin?
Not on its own. A static 3D model or a one-off simulation becomes a twin only when it is connected to operating data and kept in sync with the real asset.
What do digital twins need to work?
A solid data foundation. The main effort is contextualising data from historians, control systems, ERP, maintenance and engineering sources so the model reflects the real asset. The modelling is often the easier part.
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Software that helps
Cognite Data Fusion
Industrial DataOps and digital-twin foundation.
GE Vernova Proficy
MES, historian and digital-twin tooling for manufacturing.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.
Seeq
Advanced analytics for time-series process data.