Top Six Digital Twin Use Cases
The Digital Twin concept involves creating a virtual replica of a physical asset to monitor, predict, and optimize performance. This digital counterpart can mirror the physical entity's operations in real-time, allowing for better decision-making and predictive maintenance. The paper explores the use of Digital Twins in a large-scale smart manufacturing facility to prevent machine failures and optimize maintenance processes through the integration of IoT platforms, sensor technologies, and data analytics.
Based on a comprehensive study of 100 digital twin case studies by IoT Analytics, six main use cases have become the most prevalent. Let’s dive into these applications and see how they are shaping the future of industries.
Six most popular digital twin applications powering the future:
Twins for System Prediction (30% of projects)
These digital twins focus on predicting the future behavior and performance of entire systems, helping businesses plan and optimize based on accurate forecasts. By using current data and operational history, companies can anticipate future outcomes, ensuring smoother operations.
Example: Doosan Enerbility deployed a digital twin for its wind farms to predict power output based on IoT sensor data and weather conditions, improving operational performance and helping avoid costly fines by meeting power commitments.
Twins for System Simulation (28% of projects)
Simulating complex systems during the “build,” “operate,” or “optimize” phases allows engineers to test scenarios and make informed decisions. These twins help reduce risks and costs by running multiple simulations before changes are implemented.
Example: Siemens Mobility created a digital twin to simulate 40 different subsystems of a new train control architecture, reducing development costs by $1-8 million.
Twins for Asset Interoperability (24% of projects)
These twins streamline data formats and enable efficient data extraction across complex systems, ensuring real-time, standardized information flow. This is critical for companies managing many assets that need to communicate and function together seamlessly.
Example: Scuderia Ferrari uses a digital twin to integrate and analyze sensor data from their F1 cars, allowing different teams to collaborate on decisions around aerodynamics, power, and race dynamics.
Twins for Maintenance (21% of projects)
Digital twins designed for maintenance help predict equipment failures and guide maintenance personnel during repairs. Predictive maintenance reduces unplanned downtime and extends asset life, making these twins vital for operational efficiency.
Example: E.ON developed a digital twin to shift to predictive maintenance, evaluating failure modes and determining the remaining life of critical equipment, improving overall reliability.
Twins for System Visualization (20% of projects)
These digital twins provide visual representations, often in 3D, to improve transparency and understanding of system operations. Whether managing infrastructure or manufacturing processes, visualization twins offer clear insight into complex systems, helping to prevent issues and optimize performance.
Example: Ferrovie dello Stato, Italy's national railway, developed a digital twin of its 10,000-mile rail network, using sensors and algorithms to create a 3D model that improves remote monitoring and safety.
Twins for Product Simulation (9% of projects)
Primarily used in the design and development phases, product simulation twins allow companies to test product designs virtually, avoiding the need for costly prototypes. This accelerates development and ensures higher product performance.
Example: Krones used a digital twin to test new packaging robot designs by simulating factors like friction and gravity, reducing development time and improving efficiency.
My Take: What This Means for Companies Adopting Digital Twins
The buzz around them is well-deserved, but what a lot of people miss is that digital twins can be used across almost every process and platform, not just the flashy 3D visualizations everyone loves to show off. Sure, the 3D visuals are great – who doesn’t like a good, detailed rendering of their factory or wind farm? But here’s the kicker: digital twins are far more than just eye candy.
The real power of digital twins is their ability to work quietly in the background, almost like invisible shadows, constantly analyzing, optimizing, and providing insights. They’re there to assist with decision-making by simulating, predicting, and adjusting system performance in real time. And this can be for anything – manufacturing processes, supply chains, equipment maintenance, you name it. Whether it’s the ERP system, the CRM, or even IoT sensors monitoring your machines, a digital twin can hook into all of it.
It’s About the Data, Not Just the Display
Here’s the reality: a digital twin doesn’t have to come with a snazzy 3D interface. In fact, some of the most impactful twins operate without one. Think about it – what really matters is how these twins ingest, process, and interpret data to provide actionable insights. They could be silently running algorithms in the background, helping you predict failures, optimize production schedules, or even simulate "what-if" scenarios without you ever needing to see a 3D model.
As more companies adopt digital twins, I predict we’ll see them become integrated into almost every software platform – from the cloud-based tools you're using to manage production to the AI systems helping you design new products. They’ll act like the brain behind the curtain, constantly crunching numbers and giving us real-time feedback on the best course of action.
Scaling Digital Twins Beyond Single Projects
The potential is massive. Most companies start with one specific use case – maybe a digital twin for predictive maintenance or system simulation – but the real magic happens when they scale. Imagine an interconnected network of digital twins – each one representing different parts of your business, from supply chain logistics to factory floor operations – all working together, feeding data into each other, and creating a holistic digital ecosystem.
And as the technology matures, we’ll likely see a shift from individual digital twin projects to comprehensive digital threads that link every part of an organization’s operations. This means decisions will no longer be made in silos; instead, data and insights will flow freely across departments, providing a unified approach to optimizing everything from production to customer service.
The Future of Digital Twins: Invisible, but Invaluable
In the future, I see digital twins becoming so embedded into everyday operations that you won’t even notice they’re there – but you’ll definitely notice the benefits. The cost savings from predictive maintenance, the efficiency boosts from optimized simulations, and the innovation from faster product development cycles are just the tip of the iceberg. As companies continue to digitally transform, digital twins will be the quiet but crucial players behind the scenes, guiding every major decision.
So, if you’re thinking about adopting digital twins, remember: it’s not about having the coolest visual interface. It’s about leveraging data to drive smarter, faster, and more informed decisions. Start small if you have to, but don’t be afraid to scale up, because soon enough, digital twins will be essential to every digital transformation strategy.
References:
IoT Analytics - Decoding Digital Twins: Exploring the 6 main applications and their benefits, 2023: https://iot-analytics.com/6-main-digital-twin-applications-and-their-benefits/
