Digital Transformation capability
A Smarter Way to Understand and Improve Physical Systems
A Digital Twin is a digital representation of a physical asset, process, system, or facility. It brings together engineering models, operational data, simulation, and analytics to provide a clearer picture of how a real system behaves.
Unlike a conventional simulation that is usually created for a specific study, a Digital Twin can remain connected to the physical system and evolve with it. As new operating data becomes available, the model can be updated to reflect actual conditions. This makes it possible to monitor performance, study different scenarios, detect unusual behavior, and support better decisions throughout the lifecycle of a system.
We provide Digital Twin services for a wide range of industrial applications. Depending on the project, the solution may cover a single piece of equipment, a production line, an industrial process, a utility system, or an entire facility.
Our work starts with understanding the real system, the available data, and the problem that needs to be solved. We then develop a Digital Twin that fits the technical and operational requirements of the project.
Our services may include:
The purpose is not simply to display data. The goal is to turn operating data into useful engineering information that can support design, operation, maintenance, and future improvements.
The service scope is presented as a capability matrix rather than a single undifferentiated list.
A Digital Twin can be developed at different levels, depending on the size and complexity of the project.
An equipment-level Digital Twin represents the behavior of a specific machine, component, or asset. It can be used to evaluate performance, monitor operating conditions, identify deviations, and study how the equipment responds to different loads or environmental conditions.
A system-level Digital Twin represents several connected assets and subsystems. It helps engineers understand the interaction between mechanical, electrical, control, and process elements and assess the overall behavior of the system.
A process-level Digital Twin focuses on the flow of materials, energy, information, and control actions. It can be used to improve production, identify bottlenecks, reduce energy consumption, and evaluate alternative operating strategies.
A facility-level Digital Twin combines several systems, production units, utilities, and data sources within one digital environment. It provides a broader view of plant performance and supports centralized monitoring, planning, and decision-making.
Not every project needs the same level of connectivity. We develop Digital Twin solutions in offline, online, and real-time configurations.
An offline Digital Twin is developed using design documents, engineering calculations, historical records, and simulation models. It can be used for design studies, feasibility analysis, operator training, system evaluation, and testing different scenarios without affecting the real system.
An online Digital Twin receives data from the physical system through sensors, PLCs, DCS platforms, SCADA systems, industrial networks, or databases. This allows the model to be updated regularly based on actual operating conditions.
A real-time Digital Twin runs alongside the physical system. It continuously receives field data, updates its calculations, tracks key indicators, and identifies changes or abnormal behavior as they occur.
The right solution depends on the project objectives, available infrastructure, data quality, required response time, and cybersecurity requirements.
Digital Twins can be built using different modeling methods.
Physics-based models use engineering principles and mathematical equations to describe how a system works. Depending on the application, these models may include mechanical, electrical, thermal, fluid, process, or control-system behavior.
Data-driven models use historical and real-time data to identify patterns and relationships within the system.
In many cases, the best result comes from combining both methods. A hybrid model uses engineering knowledge to define the basic behavior of the system and operational data to improve accuracy, calibration, and predictive capability.
We select the modeling approach based on the complexity of the system, the quality of available data, the required accuracy, and the intended use of the Digital Twin.
A Digital Twin becomes more useful when it can work with the systems already available at the site. Our solutions can be connected to:
A Digital Twin becomes more useful when it can work with the systems already available at the site. Our solutions can be connected to:
The solution can be deployed locally, at the edge, through a private network, or on a cloud platform.
Local deployment may be preferred where data security, response time, or network availability is critical. Cloud and remote solutions are useful for centralized monitoring, multi-site access, collaboration, scalable data processing, and advanced analytics.
Every project begins with a clear understanding of the physical system and the expected outcome.
Our typical workflow includes:
Reviewing the asset, process, or facility
Defining the main objectives of the project
Evaluating available data, instrumentation, and existing systems
Selecting the appropriate modeling approach
Developing the Digital Twin model
Connecting the model to data and control systems
Calibrating and validating the model
Developing dashboards and analytical tools
Testing and commissioning the solution
Improving and expanding the system over time
The final solution may include interactive models, real-time dashboards, performance indicators, anomaly-detection tools, simulation functions, maintenance insights, optimization features, and connections to other industrial platforms.
From Data to Better Decisions
Industrial systems already generate large amounts of data. The challenge is turning that data into something useful.
A Digital Twin provides a practical way to combine operational information with engineering knowledge. It helps organizations understand what is happening, why it is happening, and what may happen next.
By bringing together engineering, simulation, automation, industrial connectivity, data analytics, and artificial intelligence, we develop Digital Twin solutions that are built around the real needs of each project rather than a fixed, one-size-fits-all platform.
Related capabilities
Move between the connected capabilities that make industrial data operationally useful.
Digital Twin
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