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Build a digital twin by defining the physical system and the decision it must support, then specifying the data, models, interfaces, validation evidence, and maintenance process needed for that purpose. There is no universal stack or model: the right design depends on what you are representing, how current the representation must be, and what users will do with its outputs.
1. Define the use case and the twin’s boundary
Start with the decision the twin should help someone make—not with a platform or a sensor list. A twin for detecting abnormal machine behavior has different requirements from one used to compare production schedules or test a control strategy.
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Write down the physical element being represented, the people who will use the twin, the lifecycle stage in scope, and the action or decision its output should inform. Also define the system boundary: a single machine, a production line, a building, or a larger composed system. State what is outside the boundary, such as upstream suppliers, maintenance planning, or environmental conditions, unless those are necessary to the use case.
- Purpose: monitoring, diagnosis, prediction, optimization, control, or another specific function.
- Users and decisions: who interprets an output and what they can do with it.
- Time needs: how quickly information must arrive for the decision to remain useful.
- Consequences: what can go wrong if the twin’s output is late, incomplete, or incorrect.
- Boundary: which physical elements, processes, and lifecycle activities are included.
For manufacturing, ISO 23247-1:2021 frames a digital twin as a fit-for-purpose digital representation of an observable manufacturing element with synchronization between the element and its representation. The standard provides a manufacturing framework, not a universal implementation recipe for every industry. See the ISO 23247-1:2021 listing and NIST’s Digital Twin Lab report.
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2. Turn the purpose into requirements
Translate the use case into a short, testable requirements list before selecting a model or building integrations. Identify which properties and states matter, how accurately or frequently they must be represented, and what evidence would show that the twin is useful for its stated purpose.
- List the relevant properties and states. For a machine, these might include operating mode, measured temperature, throughput, or component condition—but include a property only if it affects the intended decision.
- Connect each property to an output. Record which user decision depends on it and whether the twin needs to display, estimate, predict, or recommend something.
- Set fidelity and timing needs. Specify the resolution, update cadence, and response time the use case actually needs. Faster updates or greater model detail may add cost without improving the decision.
- Define success and failure conditions. Set measurable acceptance criteria for supported outputs, plus conditions where the twin must show uncertainty, withhold a result, or alert a user.
Requirements identification and problem formulation are part of the development process described in NIST’s publication on standardized digital twins for advanced manufacturing. NIST’s paper on data requirements states: “Data requirements are the foundation for the development and validation of digital twins and for fulfilling the intended purpose.” See the NIST paper on digital-twin data requirements.
3. Specify the data and synchronization
Map each required property or state to an available source. Depending on the application, sources may include live operational measurements, equipment or process records, environmental observations, maintenance history, or design information. The important question is not how much data you can collect, but whether the data describe the parts of the physical system needed for the intended output.
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For each data stream or dataset, document the following as implementation decisions—not as a universal set of fields mandated by an ISO standard:
- What it represents and its source or owner.
- Units, valid ranges, and any required transformations.
- Timestamp meaning, clock handling, and how observations are aligned.
- Quality checks, including how outliers or implausible readings are flagged.
- Expected update cadence and the maximum acceptable age for a value.
- What the twin does when data are missing, delayed, duplicated, or invalid.
- How changes to the source, mapping, or update process are recorded.
Define synchronization explicitly: how physical observations enter the digital representation, how they are matched to the right asset or process state, and how users can tell whether a value is current. If a value is stale or a required source is unavailable, make that status visible rather than silently presenting the last known value as current. Log updates and corrections so you can trace which inputs produced an output.
4. Choose a model that fits the decision
A digital twin needs a digital representation and relevant data, but no single model family is required for every use case. NIST describes simulation-based and data-driven model approaches; a project may use either or combine them. Select the simplest defensible approach that meets the requirements, and record its assumptions, inputs, outputs, and known limits.
| Model approach | Useful when | Trade-off to assess |
|---|---|---|
| Physics-based or simulation model | Known mechanisms and system behavior can be represented, and users need to explore conditions or explain how a result arises. | Building and maintaining the model may require domain knowledge and assumptions about the system. |
| Data-driven model | Relevant historical or operational data are available and the task is to estimate or predict a defined outcome. | Performance depends on data quality and on whether the conditions represented in the data match current use. |
| Optimization model | The twin must compare feasible choices against stated objectives and constraints. | Results depend on the accuracy of the objective, constraints, and inputs—not just the solver. |
| Hybrid model | A combination of system knowledge and data is needed for the intended outputs. | Interfaces and assumptions across model components need to be understood and tested. |
These are selection categories, not prescribed standard choices. Before implementation, specify the inputs the model expects, the outputs it supports, the conditions under which those outputs are meaningful, and how its results will be compared with observations or another suitable reference.
5. Design the architecture and interfaces
Draw the flow from the physical element to a user decision. At minimum, show the physical system, data acquisition and management, the digital representation or model, the interfaces between them, and the users or systems that consume outputs. Include any return path if an output informs an operational action or control; do not imply automated control unless the project is designed and validated for it.
- Map the physical element to its digital identity. Establish how incoming data are associated with the correct asset, process, or state.
- Show data handling. Identify where observations are collected, checked, transformed, stored, and made available to the model.
- Define model interfaces. Specify required inputs, output meanings, units, timing, and how errors or unavailable results are surfaced.
- Show user and system interfaces. Make clear how a person or another system sees status, supported outputs, and relevant limits.
- Record the action path. If a twin output can trigger an action, identify who or what approves it and what safeguards apply.
ISO/IEC 30188:2026 specifies a general digital-twin reference architecture in terms of architecture views; it can help organize the design, but it does not make a particular technology stack universal. For manufacturing, ISO 23247 supplies a domain-specific framework. The preview for ISO 23247-6:2026 distinguishes integrated, unified, and federated composition and says the framework does not prescribe specific data formats or communication protocols; consult the full standard for normative detail. See the ISO/IEC 30188:2026 listing, the ISO 23247-1:2021 listing, and the ISO 23247-6:2026 preview.
6. Verify the implementation and validate its use
Verification and validation answer different questions. Verification checks whether the implementation follows its design and requirements. Validation checks whether the resulting twin is adequate for its intended use. Plan both before relying on outputs in operations. NIST’s digital-twins material discusses the need for verification and validation; the measures themselves must be selected for the application rather than treated as one universal metric set. See NIST’s digital-twins research page.
- Check data handling. Test mappings, units, timestamps, quality checks, missing-data behavior, and stale-input indicators with representative and deliberately problematic inputs.
- Check the model against its specification. Confirm that it receives the right inputs, produces the intended outputs, and handles defined edge cases and errors.
- Choose representative conditions. Include normal operation and relevant variation; include unusual or boundary conditions when they matter to the decision.
- Compare outputs with appropriate evidence. Use independent comparison data where appropriate, or another justified reference for the application. Record the operating conditions and data used in the comparison.
- Apply task-specific acceptance criteria. Choose error measures and thresholds that reflect the decision’s tolerance for error and the consequences of a wrong output. Do not adopt a metric simply because it is familiar.
- Set limits on use. Define what happens when the twin is outside tested conditions, has stale or missing inputs, or fails its acceptance criteria. Mark outputs as unsupported or unavailable where needed.
Keep the validation record tied to the intended purpose: state what the twin was tested to do, under which conditions, against what evidence, and which outputs were accepted. A result validated for one operating range or decision should not be presented as validated for a broader one without supporting evidence.
7. Operate, monitor, and maintain the twin
A twin changes as the physical system, data sources, and models change. Assign ownership for maintaining its data mappings and interfaces, reviewing model assumptions, and communicating limits to users. Monitor whether inputs are fresh and available, whether the physical system has changed, and whether the model’s outputs remain fit for the decisions it supports.
- Version and log material changes to data sources, transformations, models, and interfaces.
- Watch for stale inputs, missing streams, or changes in operating conditions that could affect outputs.
- Revalidate after material changes to the asset, process, data, model, or intended use.
- Make the twin’s validation scope and known limits available to the people relying on it.
- Define a fallback process for unavailable or unsupported outputs.
8. Use standards for the question they answer
Standards can provide shared concepts, architecture views, or assessment frameworks; they do not eliminate application-specific engineering choices. The editions and publication information below are current as listed in the cited sources on October 7, 2026.
| Standard | Scope | When it is relevant |
|---|---|---|
| ISO/IEC 30173:2023 | Cross-domain concepts and terminology for digital twins, including system context, lifecycle, types, stakeholders, and functional view. | When a team needs shared language and concepts beyond a single industry. |
| ISO 23247-1:2021 | Overview, terminology, and requirements for a digital-twin framework in manufacturing. | When defining a manufacturing-focused twin; it is not a cross-industry implementation specification. |
| ISO/IEC 30188:2026 | General reference architecture specified through architecture views. | When structuring or communicating a digital-twin architecture. |
| ISO/IEC 30186:2025 | Generic maturity model, assessment indicators, and guidance for maturity assessment. | When assessing organizational or project maturity and identifying areas to develop. |
ISO/IEC 30173:2023 was published in November 2023; ISO 23247-1:2021 in October 2021; ISO/IEC 30188:2026 is listed as published in July 2026; and ISO/IEC 30186:2025 is listed as published in July 2025. These references help with terminology, manufacturing scope, architecture, and maturity respectively; none by itself supplies the project’s complete data schema, model, protocol, or validation metrics.
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