Key takeaways
Short answer: A simulation is a model of a system you run offline against assumed inputs — design what-ifs, capacity planning, line balancing. A digital twin is a model continuously fed by real-time data from the live asset and updated as the physical system changes. The defining feature of a twin is the live connection; without it, you have a simulation. Most "digital twin" projects in industry are actually simulations because the live connection is hard to maintain. See also Digital Thread vs Digital Twin.
Simulation models a system in software. Inputs are assumed or sampled from distributions; outputs are computed. The model runs at human convenience — once, periodically, or on demand. Examples:
Simulation is powerful and mature. It supports better decisions before equipment is built or before policies are deployed. But it operates in a parallel universe — the model and the real world are not connected.
A digital twin is a model with a live data feed from the physical asset. As the real asset operates, the twin updates. The twin can be queried at any time to answer "what is happening" or "what would happen if" using current state, not assumed state.
Three defining features:
Without all three, it is simulation with a fancy name.
The marketing problem: vendors call every model a "digital twin" because the term sells. The engineering problem: most of those models are not connected to live assets and cannot deliver what twins actually deliver.
For an industrial buyer, the question is concrete: does this model update from real-time sensor data, or is it run against a manual export of yesterday's data? If the latter, it is simulation. If the former, it might be a twin (depending on how often it updates).
Both are valuable. They solve different problems.
1. Calling a static model a twin. The connection is the defining feature. Without it, the term is empty.
2. Buying twin technology before sensor instrumentation. A twin needs a live data feed. If the asset is not instrumented, no twin technology helps.
3. Treating twin and simulation as exclusive. Most plants need both. Simulation for design and what-ifs; twin for monitoring and prediction.
4. Overspecifying twin fidelity. A perfectly accurate twin is impossibly expensive. Pick the fidelity that supports the decision being made.
A modern OEE platform captures the live data feed that any twin needs. Many advanced OEE platforms include simplified twins for predictive use cases (predict next failure, predict next slow cycle) using the data they already ingest.
Fabrico's OEE module integrates with simulation and digital twin platforms by exposing live OEE data via API, and includes predictive analytics for common failure and degradation patterns — covering the everyday twin use cases without requiring a full physics-based model.
See how Fabrico captures this automatically — explore OEE for manufacturing or book a demo.
No. OEE works with live data and good math. A twin adds predictive and what-if capabilities; it is not required for OEE itself.
A live-data-fed dashboard with a simple model predicting next-hour behavior. Many OEE platforms qualify under this loose definition.
No. IIoT is the connectivity. Twin is the model that uses the data.
Depends on how custom the asset is. Standard equipment is best served by vendor twins. Custom processes often need in-house models.
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