What is a Virtual Twin?
A virtual twin is an advanced digital representation of a physical object, system, or process that goes beyond traditional digital twins by incorporating real-time data and simulations to mirror the entire lifecycle of a product or system.
The ultimate goal of a virtual twin is to enable manufacturers to adopt a more sustainable approach to product development by minimizing waste, reducing energy consumption, and optimizing resource use across the entire product lifecycle.
The role of virtual twin in digital engineering
Virtual twins provide a dynamic, real-time simulation environment where every aspect of a product’s lifecycle can be analyzed and optimized. From initial design to production, maintenance, and even end-of-life recycling, virtual twins allow manufacturers to make informed decisions at every stage.
Unlike a digital twin, which primarily focuses on one specific object, a virtual twin encompasses the full environment in which that object operates, allowing for more comprehensive analysis and optimization. This technology enables businesses to visualize, model, and simulate products or processes in a virtual space before they are physically created or modified.
During the design phase, engineers can use virtual twins to test different materials, configurations, and design options without needing to create physical prototypes. This significantly reduces the time-to-market by allowing teams to identify potential issues early in the development process.
Digital manufacturing
As products move into manufacturing, virtual twins help optimize production lines by simulating workflows and identifying bottlenecks. This ensures that resources are used efficiently and that production processes are as streamlined as possible. By continuously feeding real-world data from IoT sensors back into the virtual twin, manufacturers can monitor equipment performance in real time, predict maintenance needs, and prevent costly downtime.
Advanced simulation
Once products are deployed in the field, virtual twins continue to provide value through predictive maintenance. By simulating wear-and-tear scenarios based on real-time data from sensors embedded in physical assets, companies can predict when parts will fail and schedule repairs before breakdowns occur. This not only extends the lifespan of machinery but also reduces operational costs by minimizing unplanned downtime.
What are the benefits of virtual twin technology?
Accelerate product development
Simulate multiple design iterations without the need for physical prototypes. This significantly reduces time-to-market by enabling faster testing and validation of new concepts.
Enhance operational efficiency
Identify inefficiencies and optimize workflows before implementing improvements by simulating product updates or process changes within an accurate virtual environment.
Predict maintenance
Predict equipment failures before they happen with real-time data continuously feeding into the virtual twin from sensors embedded in physical assets. Reduce downtime and extend the lifespan of critical machinery and products.
Improve sustainability
Minimize waste by optimizing production processes ahead of time. Reduce energy consumption by simulating different operational scenarios and identifying the most efficient approach.
What's the best way to get started with virtual twin in your organization?
Implementing a virtual twin is complex due to challenges such as managing vast amounts of high-quality data, integrating with existing IT infrastructure, ensuring scalability, and addressing skill gaps in specialized areas like AI and IoT. Having an experienced partner guide you through the implementation process is essential for ensuring seamless integration, minimizing risks, and optimizing outcomes.
Gather data from physical assets using IoT sensors or other monitoring devices. This data should include key operational parameters such as temperature, pressure, speed, or energy consumption.
Use specialized software platforms to create an accurate digital representation of your asset or process based on collected data. Ensure that this model reflects both structural details and functional behavior.
Connect your digital model with real-time data sources so that it continuously updates based on actual performance metrics. Validate its accuracy by comparing simulation results with real-world outcomes.
Run simulations under different operating conditions to identify potential inefficiencies or risks. Use these insights to optimize your processes for better performance while minimizing resource use.
Need help with Virtual twin?
Johannes Storvik and team are on-hand to provide tailored guidance and support with a deep knowledge of the full Dassault Systèmes portfolio. Reach out for a free consultation today.
Essential software for deploying your virtual twin
For end-to-end PLM (product lifecycle management) and collaborative innovation.
For complex engineering design and assembly modeling.
For optimizing manufacturing processes, improving operational efficiency, and enhancing product quality.
For robust product data management, business analytics, and decision support.
For virtual testing, analyzing, and optimizing product performance.
View our extensive range of software solutions