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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →A simulation uses a model to explore how a system might behave under different conditions. A digital twin is a digital representation of a particular system or process, connected to its counterpart so it can reflect, analyze, or support decisions about it. The two are not alternatives in every case: a digital twin can use simulation as one of its tools.
How a digital twin differs from a simulation
The practical distinction is the model’s relationship to what it represents and the job it is meant to do. A simulation can stand alone as a way to test scenarios. A digital twin is tied to a defined counterpart and may use data from it for ongoing monitoring, prediction, or operational decisions.
| Question | Simulation | Digital twin |
|---|---|---|
| Main purpose | Explore system behavior or compare scenarios using a model. | Represent a counterpart and use its digital representation to monitor, analyze, predict, or support decisions. |
| Connection to a counterpart | Does not, by itself, imply a live connection to an operating asset or process. | In NIST’s manufacturing definition, synchronization or data exchange with the counterpart is a defining feature; broader definitions vary. |
| Typical time horizon | Often answers a planned analysis or scenario question. | Can support continuing operational observation and decisions, including near-real-time use cases. |
| Role of simulation | The model and method can be used on their own. | Simulation may be combined with monitoring, analytics, optimization, and decision support. |
These are useful working distinctions, not a universal taxonomy. NIST notes that there is no single unified definition accepted across industries and research fields. Describe what a particular twin represents, how it connects to its counterpart, and what it is used to do. NIST’s digital twins overview and its 2021 manufacturing report provide definitions and context.
When to use a simulation
Choose simulation when the main question is what could happen under different designs, operating assumptions, schedules, or policies. A simulation can compare possible outcomes without claiming its model is synchronized with a live system.
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- Compare design alternatives before selecting one.
- Test schedules or operating policies against a defined set of assumptions.
- Explore scenarios when the goal is analysis, rather than ongoing monitoring of a particular asset.
For example, a manufacturer could model alternate production schedules to compare their effects before adopting a plan. The value comes from making assumptions explicit and examining their consequences; it does not require a live data connection.
When a digital twin is worth considering
Consider a digital twin when a decision depends on the status or behavior of a particular system and there is a reason to connect its digital representation to data or events from that system. In manufacturing, NIST identifies applications including machine-health analysis, maintenance planning, evaluating alternate plans and schedules, and virtual commissioning. Its overview also describes monitoring status, detecting anomalies, predicting behavior, and prescribing operations.
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A digital twin is not just a 3D visualization. Depending on its purpose, it can be a computer model or other digital representation that supports prediction, monitoring, optimization, or decisions. The NIST manufacturing definition describes a twin as “a fit for purpose digital representation of an Observable Manufacturing Element (OME) with synchronization between the OME and its digital representation.” In that context, an observable element can include people, equipment, materials, processes, facilities, environments, products, or supporting documents. NIST’s report attributes the definition to the manufacturing context and ISO 23247 material.
Can a digital twin include simulation?
Yes. Simulation is one capability a digital twin may use; the concepts are related rather than mutually exclusive. NIST describes twins as relying on simulation, monitoring, optimization, or decision support. Manufacturing implementations may also combine modeling and simulation with data analytics and optimization. A twin can therefore use a simulation to explore future scenarios while also drawing on data from the system it represents.
How to choose the right approach
Start with the decision you need to make, not the label. A twin adds value only if its connection to a counterpart and its continuing functions support a real operational need.
- Define the system and decision. Specify what the model represents and the decision it should inform: for example, comparing schedules or planning maintenance.
- Determine whether a live connection is necessary. If scenario analysis is enough, a standalone simulation may fit. If decisions depend on the status of a particular system, identify the data or events needed to keep its digital representation current.
- Set the required function and update frequency. Decide whether you need scenario testing, monitoring, diagnosis, prediction, optimization, or operational recommendations—and how often information must be refreshed.
- Check model credibility and data readiness. Plan how the model will be validated, how uncertainty will be understood, and whether available data is suitable for the intended use.
- Plan for integration and responsible operation. Consider standards, interoperability, trust, and cybersecurity in proportion to the system and decisions involved.
NIST’s work on digital twins for manufacturing addresses requirements, data management, model development and validation, results analysis, and actionable recommendations. Its 2024 standards overview discusses use cases, benefits, challenges, standards organizations, and ISO 23247. NIST also released its final IR 8356 on security and trust considerations for digital-twin technology on February 14, 2025.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What digital twins may be worth—and what the estimates mean
NIST’s figures illustrate why manufacturers are exploring digital twins, but they are estimates for defined contexts, not a promised return for an individual organization. NIST’s overview cites estimates attributed to NIST AMS 600-16: downtime of 8.3% to 13.3% of planned production time and $245 billion in estimated losses for U.S. discrete manufacturing, plus $32 billion to $58.6 billion in additional estimated losses from defects. The overview does not state a publication year for those figures.
NIST’s Digital Twin Economics page estimates $37.9 billion in annual potential aggregated benefits if digital twins were adopted throughout U.S. manufacturing under its stated data-tracking and analytics investment assumption. It also reports a $27.2 billion median annual impact and a $16.1 billion to $38.6 billion 90% confidence interval in a Monte Carlo scenario with specified assumptions. These modeled figures are not a forecast for every manufacturer.
The same NIST page reports software-sales shares across five implementation areas: predictive maintenance, 39.9%; business optimization, 25.3%; performance monitoring, 17.8%; inventory management, 11.9%; and product design and development, 3.4%. They describe shares of software sales by use area, not the probability that a particular project will succeed.
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