Digital twin vs simulation: what a validated model is, and isn't.
"Digital twin" has become the default label for almost any model of a factory, which makes it hard to know what you are buying or building. The two terms do mean different things, and the difference is not about how realistic the graphics look. It is about the data connection, and whether the model has been validated against the real system.
What a simulation model is
A simulation model represents how a system behaves over time, so you can run experiments on the model instead of the real thing. What happens if the filler runs faster? If the accumulation table doubles? If preventive maintenance moves to a different shift? A model of a production line is built from the line's structure (machines, rates, buffers) and its behavior (how often each machine stops and for how long), and it is only as good as both.
The model can be generic, a teaching example, or a detailed representation of one specific line built from that line's own event data. Its inputs are refreshed when someone re-pulls the data.
What a digital twin is
A digital twin is a virtual representation of one specific physical asset, process or system that stays connected to it. The concept is usually traced to Michael Grieves's product-lifecycle work in the early 2000s, and the name was popularized through NASA technology roadmaps around 2010. The core idea has three parts: the physical system, its virtual counterpart, and the data link between them.
A widely cited research classification (Kritzinger and colleagues, 2018) separates three levels by how the data moves:
| Level | Physical → model | Model → physical | Example |
|---|---|---|---|
| Digital model | Manual | Manual | A line simulation built from exported downtime logs; a person acts on the results |
| Digital shadow | Automatic | Manual | The same model fed on a schedule from the plant historian; a person acts on the results |
| Digital twin | Automatic | Automatic | The model receives live state and sends settings or schedules back to the line |
By that definition, many products sold as "digital twins" are digital models or digital shadows. That is not a criticism. For many questions a model is exactly the right tool. It is a reason to ask three questions of any twin: what is connected, how often, and in which direction?
Simulation vs digital twin, side by side
| Aspect | Simulation model | Digital twin |
|---|---|---|
| Main purpose | Experiment: compare what-if alternatives | Mirror and act on one live asset or process |
| Data connection | Loaded from historical data, refreshed as needed | Automatic, continuing connection |
| Time horizon | Weeks to years of simulated operation | Often the current state and the near future |
| Typical decisions | Buffer sizing, line design, capital projects, maintenance policy | Next-shift scheduling, live monitoring, control adjustments |
| Infrastructure | The model and its input data | Model, data pipelines, integration and upkeep |
| Must be validated? | Yes | Yes, and kept valid as the asset changes |
Validation comes first
A live data feed does not make a model correct. If the model's structure is wrong, or a machine's failures are averaged into a single number, a connected twin simply reproduces the error faster and with more confidence. The discipline that matters is validation: run the model over a period you have measured, and compare.
Compare at the right level, too. An overall total can match for the wrong reasons, with one failure mode overstated and another understated so the errors cancel. Comparing simulated and measured behavior for each failure mode separately cannot hide that.
Done that way, a discrete rate model reaches the standard that matters to a plant: within 1% of measured OEE, when each machine's failure modes are kept separate, their time-to-failure and time-to-repair distributions are properly fitted from the plant's own event data, and the model is validated against history. The published proof point is Fischel and Lange's WSC 2020 study of a multi-line food plant, modeled in ExtendSim®. That model was rebuilt in ReliaSim and independently validated by Tom Lange: the ReliaSim model performed within 1% of both the plant's measured OEE and the original ExtendSim model. The details are in the published OEE validation case study.
When a live connection is worth it
- Worth it: decisions made hour to hour or shift to shift, where the current state (what is down right now, what is in the buffers) changes the answer, and where someone or something will act on the output quickly.
- Usually not needed: design and investment decisions such as buffer sizing, adding a machine, or changing a maintenance policy. These depend on months of failure and repair behavior, and a validated model refreshed from that history answers them fully.
- Either way: the model has to reproduce measured history before its predictions are worth acting on.
Where DiscreteRate fits
This site covers discrete rate simulation, a method for modeling how material flows through lines and processes (see simulation methodologies). The ChiAha tools referenced here, including ReliaSim, build validated simulation models of production lines from the event data plants already record. They are simulation models, in the sense defined above, not live-connected twins.
Frequently asked questions
What is the difference between a digital twin and a simulation?
A simulation model is a representation of how a system behaves, used to run experiments on it. A digital twin is a virtual representation of one specific physical asset or process that stays connected to it, so data flows automatically from the real system to the model, and in the strict definition back again. Many digital twins contain a simulation model; the connection is what makes it a twin.
Is every simulation model a digital twin?
No. A model of a specific line, built from that line's data and refreshed when someone re-pulls the data, is a simulation model, what the research literature calls a digital model. It becomes a digital shadow when data flows into it automatically, and a digital twin in the strict sense when data flows automatically in both directions. Vendors often use digital twin more loosely, so it is worth asking what is connected, how often, and in which direction.
Does a digital twin need real-time data?
It needs an automatic data connection, but how fresh the data must be depends on the decision. Operational decisions about the next shift benefit from data that is minutes old. Design decisions such as buffer sizing, line layout or capital investment depend on months of failure and repair history, and a model refreshed periodically from that history answers them just as well.
What does it mean to validate a simulation model?
It means running the model over a period for which you have measured results and comparing the two before trusting any prediction. A strong validation compares each failure mode separately, not just the overall total, because errors in individual modes can cancel out into an aggregate that looks right for the wrong reasons.
Can a simulation model become a digital twin later?
Yes, in principle: a validated model can be connected to live data feeds once the data pipeline and the business case exist. The order matters. Validating the model first means the connection carries a model already known to reproduce the real system, rather than feeding live data into a model that has never been checked.
Read more
Simulation methodologies
— which simulation method fits which system.
Monte Carlo vs discrete rate simulation
— when a spreadsheet is enough, and when you need a clock.
Discrete rate vs discrete event simulation
— the head-to-head comparison.
The history of discrete rate simulation
— created by Andrew Siprelle in 1990.
ExtendSim® is a registered trademark of Andritz Inc. It is referenced here for identification only; no affiliation or endorsement is implied.