29 April 2025
Understanding digital twin technology
Amazingly, digital twin technology allows us to visualize and interact with digital replicas of complex systems like massive manufacturing processes, entire supply chains, smart cities – and even natural systems like the Atlantic Ocean or the Amazon rainforest.
This technology is shaking up industries by transforming interactions with real-world processes and systems. While the technology is not new, its applications are expanding in parallel with the rise of artificial intelligence and generative AI (GenAI).
Dig in to understand more about how digital twins work, examine the technology’s capabilities and explore its effectiveness as it works in synergy with GenAI.
What are digital twins?
Digital twins – which are digital representations of a physical asset, process or system – mirror and synchronize with a physical system. Because they allow for real-time tracking and analysis, they can help organizations better understand, test and optimize performance.
“Digital twins are motived by outcomes, powered by interoperability of composable building blocks, guided by domain knowledge and implemented in architectures that are adaptive,” says Paul Venditti, Advisory Industry Consultant at SAS.
By using digital twins, organizations can conduct tests and visualize products digitally rather than relying solely on physical prototypes. This accelerates testing and development processes, contributing to an organization’s digital transformation and increasing agility in fast-paced and high-risk markets.
Consider these examples across industries:
- Construction. Plan and test major infrastructure upgrades to ensure the best outcomes.
- Health care. Create a digital twin of part or all of a hospital or clinic to test its operational efficiency or even model how different treatments can affect patients.
- Manufacturing. Optimize machine performance by detecting wear and tear early, reducing unexpected downtime and extending asset lifespans.
Digital twins are built using data collected from IoT sensors on the physical object. This data is then used to create a dynamic digital model. The purpose of such a model is not only to replicate the present state of its real-world counterpart but also to predict future states and retrospectively analyze past performance. Unlike digital shadows, digital twins go beyond mirroring a present state to predict, optimize and continuously improve operations.
Digital twins consist of three critical components:
- Data foundations. Real-time sensor data, synthetic data, historical records, AI-driven simulations and other operational data are collected as inputs.
- Virtual intelligence. AI models that learn from the past use real-time data to simulate behavior and predict outcomes.
- Real-time monitoring and predictions. Automated analysis and interactive 2D or 3D visualizations, often with gamelike interfaces, offer proactive recommendations aligned to business goals.
How GenAI enhances digital twins
GenAI enhances the capabilities of digital twins by creating new data and insights based on these dynamic models. While traditional AI excels at specific tasks, GenAI can generate new data and scenarios that help optimize and innovate processes.
For instance, GenAI can simulate various conditions and predict outcomes for digital twins, offering insights into potential improvements. This allows organizations to test and refine their operations digitally, speeding up development and reducing costs.
This synergy between digital twins and GenAI can accelerate digital transformation for organizations, making them more agile and competitive.
Here are some other ways in which GenAI contributes to digital twin functionality:
- Data processing and analysis. AI algorithms efficiently process the data streams generated by digital twins, filtering out noise to find valuable insights. GenAI can analyze vast amounts of data in new ways. This “synthetic data” can then be used to improve the accuracy and efficiency of existing AI systems and processes.
- Predictive modeling. Using machine learning, GenAI can forecast future conditions and behaviors, enabling preemptive action to avoid issues or optimize performance.
- Optimization and decision making. GenAI can propose optimizations for systems and processes, assisting decision makers with data-driven recommendations.
Digital twins use cases
Modeling complex processes with digital twin technology
Austrian brick maker wienerberger has been testing innovative ideas in manufacturing with digital twins. These virtual representations of the factory floor can help manufacturers monitor, analyze and optimize their operations.
By having a virtual model of the entire production process, the team at wienerberger uses digital twin technology to test new ideas without risking operational delays or failures. So far, they have tested strategies for improving key production elements like brick firing and brick drying in a virtual environment.
“The concept of the digital twin plays an absolutely crucial role in manufacturing,” explains Florian Zittmayr, Data Science Lead at wienerberger, “especially when use cases get a bit more complicated and they’re not focusing on an isolated production step.”
Revolutionizing tax analytics with digital twin technology
Federal Public Service (FPS) Finance in Brussels has transformed tax analytics by deploying digital twin technology, called Aurora, to eliminate biases and inefficiencies often found in traditional, sampled data methods. Aurora analyzes data from 7 million tax returns, providing policymakers with an unprecedented level of precision and insight into the nation’s tax system.
“We use Aurora whenever we need very accurate results or an analysis of a tax regime that is very rare,” says Adriaan Luyten, Head of the Tax Policy Unit in the research department of FPS. “The better our estimations are, the better our policymakers are informed and the better the results will be.”
Aurora doesn’t just replicate calculations – it predicts various tax scenarios with remarkable accuracy, supporting more effective policymaking and shaping the future of financial strategy.
The benefits of digital twins
The incorporation of digital twins in an organization's workflow brings a host of benefits, including:
- Improved efficiency and productivity. By accurately simulating real-world assets, it's possible to streamline processes and increase efficiency. Generative AI allows you to test multiple scenarios to ensure that you implement the best solution for your organization’s needs.
- Cost savings and reduced risk. Predictive analysis and maintenance reduce the likelihood of failures and downtime, saving potential costs. The ability to simultaneously model multiple options helps avoid potentially costly errors and provides more efficient services.
- Enhanced maintenance and monitoring. Continuous monitoring assists in maintaining the health of the assets, ensuring longevity and reliability. In turn, this allows you to identify and act on issues as they arise.
Other types of digital twins
The versatility of digital twin technology allows it to be applied in multiple fields and industries such as:
- Manufacturing and supply chain. Optimizing production lines and logistics will improve efficiency, product quality and customer satisfaction, regardless of whether it’s consumer products, large-scale industrial items or supply chain improvements. IoT sensors combined with digital twins have also improved predictive maintenance capabilities throughout the manufacturing sector.
- Public sector. Smart cities, governance and large infrastructure projects can all benefit from digital twins. From road planning and traffic flow to optimizing utilities and public resources, results are already being seen from the implementation of digital twin technologies.
- Finance and insurance. Implementing digital twin technology into the finance and insurance industry can provide large-scale, efficient data modeling and process improvements. This can increase profits, pass on savings to consumers and inform strategic and policy decisions.
- Health care and medicine. Personalizing medical treatments and managing hospital systems can improve patient outcomes and satisfaction, increase efficiency and reduce waste.
Imagining all the ways we might use digital twins to build digital replications of their physical counterparts is exciting. The ability to run simulations, predict outcomes and adjust plans for continual improvement can change our entire approach to problem solving and decision making.
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