Digital Twin

Engineering & Marketing International

Digital twin software that spans the entire asset lifecycle

Connect data from every layer of the technology stack for one contextualized, multi-visual experience from capital project to operations

What is digital twin software?

Data collected and aggregated with context: Digital twins rely on a continuous stream of data and information from multiple sources like sensors, assets, systems, devices and other sources to capture the real-time state and condition of the physical object or process.

Analytical capabilities: Digital twins leverage AI/ machine learning models to analyze the collected data and provide insights into the behavior, performance and potential issues of the physical object or system.

Data integration and modeling: The collected data is processed, integrated and transformed into a dynamic model that accurately represents the physical asset or process.

Multi-experience visualization layer: Role-based visualization allows the user to have access to data presented in a form that is fit for a specific purpose. The user on the shop floor sees the information relevant to their KPIs, while the C-suite is looking at the same data in a context more relevant to overall management. 


How does a digital twin work?

In the world of industrial organizations, a digital twin is a powerful tool that delivers a single pane of glass across the organization. But how exactly does a digital twin work? Data management and infrastructure as well as the collection, consolidation and aggregation of that data play a crucial role in the functioning of a digital twin. By gathering data from various sources such as:

  • Living data from the monitoring and control aspects of automation systems
  • Raw data—including engineering and operations, real-time and historical—from process equipment, utility systems and other external sources
  • Relevant equipment design data, market prices, environmental conditions, business indicators and consumer behavior data 

Any data which pertains to the business can be part of the digital twin. This contextualized 1D, 2D, 3D and operational data is then enhanced with first principle modeling or/and analytics and AI to get deeper insight into the challenge the organization is trying to solve. Analytics and AI form “the brain” of the digital twin. The analytics layer enables the digital twin to move beyond a static, real-time representation of the asset or process and forecast how the asset or process will behave in the future given a set of variables.

Real-time synchronization and simulation are essential features of a digital twin. As the physical asset or process undergoes changes, the digital twin updates itself in real time. This synchronization ensures that the virtual representation remains accurate and up-to-date. 
By simulating different scenarios and running predictive algorithms, companies can test and optimize their operations without any risk to the physical asset or process. This capability allows for proactive decision-making and the identification of potential issues before they occur.

With an advanced visualization experience, companies can gain a holistic view of their assets or processes in a virtual environment. 

Digital twins throughout the industrial lifecycle

Digital twin usage will vary from industry to industry, business to business – and each organization will make its own unique case for adopting them. A digital twin is best defined by the practical ways it can use data to provide insights supporting the design, operation and optimization of an industrial asset or process throughout its entire life cycle.

Here are four general areas where digital twin technology can make a real difference: 

  1. Design and build. The adoption of a data-centric approach improves visibility and control from capital project phase to plant start-up. EPCs can spend less time on design rework and delivery is more likely to be on schedule and on budget.
  2. Operations.  With access to real-time and historical data, the asset operator can compare, model and derive insights from actual behavior. The results can empower the workforce, improve productivity, reduce unnecessary work, and enhance reliability while maintaining safety and sustainability.   
  3. Optimization. AI/machine learning and first-principles models provide insights and guidance on plant operations. With useful benchmarks to refer to, operators can make more informed decisions, balance output and reliability with cost efficiency, and increase profitability.
  4. Sustainability. Real-time tracking of progress against sustainability KPIs and goals across the enterprise can help minimize waste, increase worker satisfaction, achieve environmental compliance and enhance corporate social responsibility.