Product twins
Product identity, specifications, configurations, components, lifecycle information, and service records.
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Digital representations built around real-world entities
A digital twin is a digital representation of a target entity connected with data in a way that supports an appropriate relationship between physical and digital states. Not every digital representation has every capability.
Types of digital twins
Depending on maturity and purpose, a twin may support integration, visualisation, monitoring, analysis, simulation, optimisation, or collaboration.
Component & material twins
Component/material properties, relationships, origin information, and changes across defined lifecycles.
Asset & equipment twins
Equipment records, technical documentation, service history, maintenance events, and connected operational data.
Building & infrastructure twins
Building, facility, spatial, asset, and operational information, including relevant BIM data.
Process & workflow twins
Process stages, activities, inputs, outputs, dependencies, and performance information.
Supply-chain & network twins
Suppliers, facilities, products, materials, logistics events, and other connected entities.
Environmental & resource twins
Relevant resource, energy, water, environmental, and performance data where suitable sources exist.
Operational & performance twins
Selected operational information, indicators, and lifecycle events for visibility and decisions.
These are practical solution categories, not mutually exclusive or universally standardised classifications.
Potential capabilities
- Entity and asset registration; identifiers and structured profiles.
- Data modelling and relationship mapping.
- Lifecycle events and record versioning.
- Data import/export and APIs.
- Dashboard and visualisation integration.
- Optional sensor/IoT integration.
- Maintenance and performance workflows.
- Simulation or analytics integration where technically supported.
- Role-based access and data governance.
Scope. Capabilities such as IoT connectivity, real-time monitoring, simulation, and predictive analytics are available only where the specific capability is implemented and demonstrated for your use case.
Implementation approach
- Assess the business problem, target entity, users, data, and intended outcome.
- Model entities, attributes, relationships, events, and data requirements.
- Connect available sources and define update frequency.
- Implement records, interfaces, workflows, and integrations.
- Evaluate data quality, operational usefulness, adoption, and expansion opportunities.
Start with one entity type and one clear outcome.
Discuss a Digital Twin pilot