Digital Twin Solutions for Industrial Systems

A digital representation of a physical asset, machine, facility or process that, connected with the right data, helps teams understand behaviour and evaluate changes.

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Understanding Digital Twin Technology

A Digital Twin is more than a static 3D model. It can be linked to data from the physical system it represents, so teams view relevant information in a digital environment.

The level of connectivity and analysis varies by use case: some Digital Twins support design and visualisation, while others connect live or periodic data for monitoring, analysis and scenario evaluation.

At a Glance

  • Understand equipment behaviour
  • Evaluate process changes before production
  • Support maintenance planning
  • Improve resource utilisation
  • Support engineering decisions

Core Capabilities

Connected Operational Data

Bring selected equipment, process or facility information into a shared digital view.

Interactive 3D Environments

Explore digital representations of assets, spaces and processes interactively.

IoT-Based Connectivity

Connect relevant live or periodic data where suitable sensors and infrastructure exist.

Operational Data Analysis

Identify patterns, changes or areas that may need attention.

Virtual Scenario Evaluation

Assess operating conditions or proposed changes before applying them physically.

Equipment & Process Visibility

Make system information easier for teams to view and interpret.

Integration with Existing Systems

Connect suitable data sources or enterprise systems, subject to compatibility.

Shared Decision View

One digital view for engineering, operations and maintenance discussions.

How a Digital Twin Is Developed

  1. Define the physical system

    Identify the asset, process or facility to represent and clarify the intended use.

  2. Gather relevant information

    Collect drawings, 3D assets, equipment specifications, process data and other available information.

  3. Build the digital representation

    Create or adapt the model to the level of detail the project needs.

  4. Connect suitable data sources

    Integrate sensor feeds, databases or industrial systems where required and feasible.

  5. Enable analysis and visualisation

    Configure dashboards, views, analytics or simulation for the project objectives.

  6. Apply insights to planning

    Use the information for reviews, planning and decisions, and refine the model over time.

Understand and Improve Industrial Operations

  • Equipment Behaviour

    Understand how equipment behaves through a connected 3D view.

  • Process Evaluation

    Evaluate process changes before applying them in production.

  • Maintenance Planning

    Support maintenance and operational decisions when reliable data is available.

  • Shared Decision View

    One digital view for engineering, operations and maintenance discussions.

Digital Twin Applications Across Industries

Energy & Utilities

Assets and infrastructure with operating information for asset management.

Buildings & Infrastructure

Spatial models with building data for facility visibility and lifecycle planning.

Technologies Behind a Digital Twin

3D Model

Primarily represents the shape, layout or appearance of an object or environment.

Digital Twin

Can use a 3D model as its visual foundation, and may also connect it with engineering, operational, sensor or performance data for monitoring, analysis and simulation.

Frequently Asked Questions

A digital representation of a real asset, process or system. Depending on its design, it may also connect to data from the physical system to help people understand its condition or behaviour.

Digital Twins can represent machines, production lines or processes. With suitable data and models, they may support process visualisation, performance analysis, scenario evaluation and maintenance planning.

No. Some Digital Twins use engineering information, historical data or periodically updated data. Live sensor connectivity is useful for certain applications but is not required for every Digital Twin.

It can support maintenance and operational decisions when reliable condition or performance data is available. The impact depends on the use case, data quality, integration and how teams act on the insights.

Requirements vary by project. Inputs may include 3D models, drawings, equipment specifications, process data, sensor readings, maintenance records or information from existing industrial systems.

In many cases, through supported APIs, databases, protocols or data connectors. Feasibility depends on the systems involved, compatibility, cybersecurity requirements and available access.

Yes. A Digital Twin can be scoped around a single asset, machine or process, which keeps implementation aligned with available data, budget and operational needs.

A digital simulation evaluates a system or scenario using a model. A Digital Twin may include simulation, but can also maintain a digital representation connected to real-world data.
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