No unified view of complex operations
Information about assets and processes is spread across many systems.
01 / Advance Technology
Digital Twin Development
A digital twin is only useful if it answers questions people actually need answered: what is happening now, why, and what would happen if we changed something. We build twins with a clear purpose, the right level of detail and reliable connections to real data.
At a glance
- Service
- Digital Twin Development
- Pillar
- Advance Technology
- Typical starting point
- Digital twin feasibility and purpose definition
- Typical first phase
- 4 to 6 weeks for feasibility; first twin in 12 to 24 weeks
- Key technologies
- Digital Twins, Internet of Things, Machine Learning
Durations depend on scope, data readiness and integration needs.
What is Digital Twins?
Short answer
A digital twin is a virtual representation of a physical asset, process, system or facility that is kept up to date with data from its real counterpart, and is used to monitor current state, analyse behaviour, simulate scenarios and support operational or planning decisions.
The term digital twin covers a wide range, from a live dashboard linked to an asset model, through to physics-based simulations of an entire plant or city district. Much disappointment with digital twins comes from building more detail than the use case requires, or from 3D visualisations that are not connected to reliable data.
We start by defining the decisions the twin will support and the level of fidelity needed. A maintenance twin might need asset hierarchies, sensor data and failure models. A capacity planning twin might need process flows and simulation. A construction or facility twin might need spatial models linked to building systems. Architecture, data integration and model choices follow from that purpose.
Business problems
What problems does Digital Twins solve?
These are the situations where organisations most often find this service worthwhile.
Risky changes to physical operations
Process or layout changes are tested on the real system, causing disruption.
Reactive asset management
Teams cannot see the combined condition of assets or anticipate problems.
Planning without evidence
Capacity, energy and investment decisions rely on static spreadsheets.
Remote operations with limited visibility
Distributed sites cannot be monitored centrally in a meaningful way.
Knowledge lost when experts leave
Understanding of how complex systems behave sits with a few individuals.
Use cases
Where can Digital Twins be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Manufacturing | Production line twins for throughput analysis, bottleneck simulation and layout changes. |
| Facilities and buildings | Building twins linking spaces, equipment and sensors for energy and maintenance. |
| Utilities | Network twins for water, power or gas distribution monitoring and scenario analysis. |
| Infrastructure | Structural monitoring twins for bridges, tunnels or towers. |
| Logistics | Warehouse and yard twins to test layouts, staffing and flow scenarios. |
| Agriculture | Farm or greenhouse twins combining sensor, weather and crop data. |
Who should consider it
- Asset-intensive organisations managing complex equipment
- Operators of facilities, campuses or infrastructure
- Manufacturers planning process or capacity changes
- Organisations with IoT data seeking more value from it
How AARHIT approaches it
How we deliver Digital Twins
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Purpose and feasibility
We define the decisions the twin will support, users, fidelity required and data availability.
Output: feasibility report with a recommended scope and twin type.
Stage 2 of 6
Information model
Assets, processes, relationships and data sources are modelled in a structure that can grow.
Output: information model and data mapping.
Stage 3 of 6
Data integration
Sensor, operational, maintenance and business data is integrated with attention to quality and latency.
Output: live data pipelines feeding the twin.
Stage 4 of 6
Analytics and simulation
Monitoring logic, analytics, AI models or simulations are built for the defined use cases.
Output: working analytical and simulation capabilities.
Stage 5 of 6
User interface
Dashboards, 2D or 3D views and scenario tools are designed for the people who will use them.
Output: user-tested interface.
Stage 6 of 6
Validation and evolution
The twin is validated against real outcomes and extended to further assets or use cases.
Output: validation results and a capability roadmap.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Information modelling | Asset hierarchies, Digital Twins Definition Language, knowledge graphs |
| Platforms | Azure Digital Twins, AWS IoT TwinMaker, open-source twin frameworks |
| Data | IoT platforms, time-series databases, historians, data lakes |
| Simulation | Discrete event simulation, physics-based and hybrid models, SimPy, AnyLogic |
| AI | Anomaly detection, forecasting, surrogate models |
| Visualisation | Web dashboards, BIM integration, 3D engines such as Unity or three.js |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- A single, live view of complex assets or operations
- Lower-risk testing of changes through simulation
- Earlier identification of emerging problems
- Planning decisions based on evidence
- Captured knowledge of how systems behave
How long does implementation generally take?
Typical timeline
Feasibility and purpose definition typically take 4 to 6 weeks. A first operational twin for a defined asset or area commonly takes 12 to 24 weeks, depending on data integration and simulation requirements.
What determines complexity?
- Required fidelity and simulation depth
- Number and diversity of data sources
- Real-time data requirements
- Availability of engineering drawings or BIM models
- Number of assets and sites
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Avoid visualisation-first projects
Impressive 3D models without reliable data or clear use cases rarely deliver value.
Data quality is decisive
Twins magnify data problems. Sensor calibration, gaps and naming consistency need attention.
Start with one use case
A focused first twin builds credibility and reusable foundations for broader ambitions.
Plan for maintenance
As physical systems change, the twin's models and mappings must be kept current.
| Risk to consider | How we manage it |
|---|---|
| Scope too broad for value | Purpose-driven scoping and phased capability growth. |
| Twin diverges from reality | Validation against real outcomes and processes for updating models. |
| Integration difficulties | Early data source assessment and standard information models. |
| Low user adoption | Designing around specific user decisions and workflows. |
FAQ
Questions about Digital Twins
What is the difference between a digital twin and a 3D model?
A 3D model shows how something looks. A digital twin is connected to live or regularly updated data from the real asset or process, and is used to monitor, analyse or simulate its behaviour. It may or may not include 3D visualisation.
Do we need IoT sensors for a digital twin?
Most operational twins use sensor data, but some use data from control systems, business systems or periodic inspections. The data needed depends on the decisions the twin supports.
How accurate does a digital twin need to be?
It needs to be accurate enough for the decisions it supports. A maintenance monitoring twin may need reliable condition data, while a planning twin needs validated process simulations. We define fidelity during feasibility.
Can AI be used within a digital twin?
Yes. AI is commonly used for anomaly detection, forecasting and fast surrogate models that approximate complex simulations, while physics-based models remain important where behaviour must be explainable.
What does maintaining a digital twin involve?
Maintenance covers keeping data pipelines healthy, updating asset and process models when the physical system changes, recalibrating simulations against real outcomes and retraining any AI models. The effort depends on how often the real system changes, and we estimate it during feasibility.
Next step
Considering Digital Twins for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.