Asset performance
Monitoring health and efficiency of critical equipment.
Technology / Infrastructure and Edge
Digital Twins
A digital twin mirrors a real asset, process or place in software, kept current with real data. Organisations use twins to understand what is happening, why, and what might happen if they change something.
What is Digital Twins?
Short answer
A digital twin is a virtual representation of a physical object, process, system or environment that is continuously or regularly updated with data from its real-world counterpart, and used to monitor, analyse, simulate and optimise performance throughout its lifecycle.
Digital twins vary widely. A component twin may model a single pump. An asset twin models a whole machine. A process twin represents a production line or supply chain. A system twin can span a facility, network or city district. Fidelity ranges from data-linked dashboards to detailed physics-based simulations.
Twins combine several technologies: IoT and operational data for current state, information models that describe assets and relationships, analytics and AI for insight, simulation for what-if analysis and visualisation for people. Their value depends on a clear purpose and trustworthy data, not on visual realism alone.
Related concepts
- IoT
- Simulation
- Information models
- Machine Learning
- BIM
- Edge AI
- Data Engineering
How it works
How does Digital Twins work?
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Model the entity
Assets, components, processes and relationships are defined in an information model.
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Connect data
Sensor, control, maintenance and business data are linked to the model.
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Represent current state
The twin reflects conditions and performance in near real time or regularly.
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Analyse and simulate
Analytics, AI and simulation explain behaviour and test scenarios.
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Inform action
Insights guide operations, maintenance, planning or design decisions.
Applications
Where is Digital Twins useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
Production planning
Simulating throughput and bottlenecks before changes.
Building operations
Linking spaces, systems and sensors for energy and maintenance.
Utility networks
Monitoring and simulating water, power or gas distribution.
Infrastructure health
Structural monitoring of bridges, tunnels and towers.
Training and design
Testing designs and training staff in virtual environments.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- High-fidelity twins are costly to build and maintain.
- Twins become misleading if data is poor or models drift from reality.
- Integration across many systems is complex.
- Visualisation without analytical purpose adds little value.
- Physical changes must be reflected promptly in the twin.
Common misconceptions
A digital twin is a 3D model.
Visualisation is optional; the defining feature is connection to real data for analysis and decisions.
Twins must model everything.
Effective twins model what is needed for specific decisions.
Digital twins are only for large enterprises.
Focused twins of single assets or lines can deliver value at modest scale.
Comparison
Types of digital twins
Scope determines complexity and the decisions supported.
| Type | Represents | Example decisions |
|---|---|---|
| Component twin | A single part | When to replace a bearing or seal |
| Asset twin | A complete machine or asset | Maintenance planning and performance tuning |
| Process twin | A production line or workflow | Throughput, scheduling and layout changes |
| System twin | Facilities, networks or districts | Capacity, resilience and investment planning |
Decision guide
How to evaluate Digital Twins for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| What decisions will the twin support? | Defines scope, fidelity and success measures. |
| What data is available and how reliable is it? | Twins magnify data quality issues. |
| Is simulation needed, or monitoring and analytics? | Simulation adds significant effort. |
| How often does the physical system change? | Determines maintenance effort for the twin. |
| Who will use the twin and how? | User needs shape interface and integration. |
AARHIT services
Services that apply Digital Twins
How we put this technology to work for organisations.
Advance Technology
Digital Twin Development
Create data-connected virtual models of assets, processes or facilities for monitoring, simulation and planning.
Explore service Advance TechnologyInternet of Things and Edge AI
Connect devices and sensors, and run inference close to where data is produced when latency or connectivity demands it.
Explore service AutomationIndustrial Automation
Connect plant systems, sensors and control data with analytics and AI for monitoring, quality and maintenance.
Explore service Hybrid IntelligencePredictive Analytics
Forecast demand, risk, failure or behaviour with models that are validated, monitored and explained to users.
Explore serviceRelated technologies
Industries
FAQ
Digital Twins FAQs
What is a digital twin?
A digital twin is a virtual representation of a physical asset, process or system that is updated with real data and used to monitor, analyse and simulate its behaviour.
What is the difference between a digital twin and a simulation?
A simulation models how a system could behave. A digital twin is linked to a specific real system's data, reflecting its actual state, and may include simulation capabilities.
What technologies are used in digital twins?
IoT and operational data platforms, information models or knowledge graphs, analytics and machine learning, simulation tools and visualisation, often including 3D or BIM integration.
How does AI enhance digital twins?
AI detects anomalies, predicts future states, creates fast surrogate models of complex simulations and recommends optimisations based on twin data.
How do you start a digital twin initiative?
Start with a specific asset or process and clear decisions, assess data availability, build a focused twin, validate it against reality and expand from there.
Next step
Exploring Digital Twins?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.