BMT | Demystifying digital twins

Smarter assets with digital twin technology

The 'digital twin' is now a recognised core component of the Industry 4.0 journey, helping organisations understand their complex processes, resources and data to provide insight into their business and help optimise their operations.

Contact Us

Key contact

Jake Rigby

Research and Development Lead

Jake Rigby

Research and Development Lead

Bath, United Kingdom


Ottawa, Canada

Lee Hedd

Business Development Manager

Lee Hedd

Business Development Manager

Ottawa, Canada


We see digital twins as a critical element of industry 4.0, seeing the benefits and savings achieved.

One delivered solution integrates precise terrain data, machine data, health data and production metrics into a single 3D immersive and intuitive interface, allowing production teams and planners to measure, monitor, report and optimise mining operations in real-time.

The system is built using our Remote Operations Automation and Robotics (ROAR) software platform and lidar mapping capabilities and expertise in 3D algorithmic decision making.

Mining Digital Twin

We continue developing and expanding our digital twin technology with ongoing projects delivering Autonomous TLO (Train Load Out) Digital Twins, Hopper Surface Mapping and Wagon Surface Mapping solutions for train unloading and rail operations.

Other technologies are developing for Stockyard Mapping and Ship Hold Surface Mapping for bulk materials handling customers in ports.

BMT Bluescope Digital Twin

Maritime defence

To support future maritime defence digital twins, we’re taking a multi-threaded approach through a range of internal strategic initiatives and R&D activities.

These solutions highlight our abilities and expertise in developing bespoke customer solutions and are also a testament to our commitment to Digital Transformation programmes in the industries we serve.

  1. Operations

    Models are triggered to run hundreds or thousands of "what-if" simulations using variables like weather, fleet size, energy costs or performance factors to evaluate readiness or make necessary adjustments to current system set-points. System operations can then be optimised or controlled during operation to mitigate risk, reduce cost or gain any number of system efficiencies.
  2. Predictive maintenance

    In industry 4.0 applications, models can determine the remaining useful life of an item of equipment and advise operators on the best time to service or replace it.
  3. Anomaly detection

    The model runs in parallel to the relevant real asset and immediately flags any action that deviates from expected (simulated) behaviour. For example, a petroleum company may stream sensor data from offshore oil rigs with continuous operations; the digital twin model will look for anomalies in the behaviour to help avoid catastrophic damage.
  4. Fault isolation

    Anomalies can trigger a battery of simulations to isolate the fault and identify its root cause, so that engineers, or the system, can take appropriate action.

Digital threads in the design

Enable the creation of the twin

We are mapping out a design toolset of the future for ship design, creating an integrated digital design twin with advanced integrated simulation and analysis capabilities.  Our solution will allow the shift of risk from physical testing back into the design phase.

Integrity and security

End-to-end (sensor to decision) secure data transmission, management and verification; digital twin cyber threat detection, protection, response and recovery; a digital twinship or fleet is susceptible to threats similar to those seen by physical ships.

Supply chain engagement

Federated twin high-level architectures and APIs enable prime contractors and OEMs to support a digital ship with ‘digital equipment’ whilst protecting I.P. and ensuring the ‘digital assets’ can work with other systems.

Digital twin return on investment

Research into future intelligent asset management - a digital twin creates value when embedded into a holistic, intelligent asset management system, supporting decisions and optimising the business model at the portfolio and organisational levels.

The complexity

A digital twin must be more than a single entity to provide benefits across an entire enterprise. Fully-featured digital twins will be realised as a family of twins, each maturing and growing in depth and value through its life.

The models used to support asset performance may differ from those used in design and development, but there will be clear data linkages across these model sets that need to be managed. Twins will both consume and create high volumes of data throughout their lives.

Several coherent twins will be created in the design phase, from basic 3D geometric and design information to complex high order simulation models and more comprehensive design information management. This "Design Twin" set will mature through the build phase to create a "Manufacturing Twin" - a key enabler for the future digital shipyard.

This evolution will continue through the in-service phase, where sensor inputs from the physical asset will help realise the final "Digital Twin." Further models will need to be created at this point, typically fast surrogate models that include a measure of their uncertainty to effectively provide predictive and prognostic outputs in response to real-time input data.

Therefore, creating a twin is a complex process and relies upon a continuous digital thread throughout the lifecycle.

Although IoT is a critical enabling technology for digital twins, twins are much more than a simple IoT implementation, integrating an intelligent sensor with a data platform.

Twins combine on-platform sensing with other data sources (e.g. environmental data, historical maintenance data) and match this data with a set of models, applying analytics and machine learning to enable predictive and prognostic insights. These insights provide practical decision support in an intelligent asset management context.

Due to the complexity and volume of data, the effective imaging of twin outputs is critical to enable usability and layered data consumption.

The future

Since the original inception of the twin concept in 2002, progress has been made, especially in industries such as mining, automotive and aerospace.  Typically, organisations develop twin elements to help achieve a particular aim for a specific system or sub-system. Since then, the digital twin concept has increased, taking on many interpretations and variations and increasing relevance across many other sectors.

There is a lot to do before the benefits of twins can be realised

There are no standards for such tools, and many challenges remain around open data capture, data storage, and performing practical analysis to support decisions with confidence.

A standard definition has not yet been established, and there are many variations in the accepted scope across sectors. To add further complexity, what began as a niche engineering model is increasingly being expanded and aligned to broader digital transformation initiatives.

Defence digital twin

Mining digital twin

Our solution delivers site-wide software for accurate knowledge of the state of a whole mine. The solution consolidates terrain data from various machine terrain mapping solutions to provide a single platform to measure, monitor, report and optimise your operation.

This solution provides the production team and planners with an intuitive 3D interface to interrogate precise real-time data, enabling quantitative decision making. 

The solution can integrate Terrain data, Machine data, Health data and Production metrics into a single 3D immersive and intuitive interface.

System features: 

  • Precision real-time and the continuous automated whole of mine survey
  • Precision machine tracking and reporting
  • Intuitive 3D virtual environment interface

Benefits realised by mine operators:

  • Automatic real-time as-built terrain
  • Support for quantitative decision-making
  • Planning based on actual terrain geometry.
Mining Digital Twin