Interscale Content Hub – The digital twins performance is a hot topic in the AEC industry these days. Why? A Digital Twins (DTs) is only as good as the data it’s built on and how efficiently it runs.
Since digital twin are increasingly used in industries such as manufacturing, healthcare, smart cities, and aerospace, it’s become crucial to ensure that they work as well as possible so that we can get the most out of them.
Digital twin have the potential to transform industries by providing a detailed, dynamic understanding of systems and processes.
Are Digital Twins Effective?
Digital twins have shown they’re really effective in lots of different fields. Let’s take a look at a few facts about it.
According to a 2019 Gartner study in “Gartner Survey Reveals Digital Twins Are Entering Mainstream Use,” 75% of organizations working on IoT projects were already using or planning to use digital twins.
We’re seeing some pretty impressive gains in operational efficiency and predictive maintenance capabilities at companies like Siemens, GE, and Airbus, thanks to digital twins.
Shri. Vidit Kumar and his colleagues, in their paper “Exploring the Performance and Accuracy of Digital Twin Models,” say how digital twins can help us monitor, simulate, and optimize systems in manufacturing, energy, and healthcare.
Their study shows how digital twins can boost productivity and quality by spotting and fixing problems before they happen.
Similarly, Ioan Petri and his colleagues in “Digital twins for performance management in the built environment“ talk about how digital twins can help buildings use energy more efficiently, making them more adaptable and resilient to climate change.
The 2023 Grand View Research report says the global digital twin market was worth about USD 16.75 billion in 2023. It’s projected to grow at a 35.7% compound annual growth rate (CAGR) from 2024 to 2030.
Digital twin technology is gaining traction because it can bridge the gap between the physical and virtual worlds.
The global market is expected to grow a lot over the forecast period, as more and more companies start using the Internet of Things (IoT) and big data analytics.
The companies also need to make sure they’re spending their money wisely, and that their processes are as efficient as possible.
So, these numbers show how much more people are relying on digital twins to make things better, cut down on downtime, and make things run more smoothly.
Factors Affecting Digital Twins Performance
Data Quality and Integration
Good data is the foundation of effective DTs. Kumar and others say that it’s important to integrate data from different sources to make sure the virtual model accurately reflects the physical entity.
They point out some of the challenges, which include managing data from different sources and making sure it’s all consistent in real time.
Similarly, Petri et al. talk about how important it is to have precise data in their digital twin model for buildings.
They combine data from sensor networks to make sure buildings use energy more efficiently and reduce their carbon footprint.
For more on the challenges of DTs, kindly read “Don’t Get Blindsided: A Playbook at Digital Twins Challenges and Solutions.”
Real-Time Data Processing
If we’re going to get the most out of digital twins, we need to be able to process data in real-time.
Kumar et al. say we can make better decisions and take action faster if we use advanced analytics and machine learning to understand data quickly and accurately.
Petri et al. also point out the importance of real-time data in their model. In their approach, sensor data is processed every 15 to 30 minutes to enable quick responses to building management issues.
Computational Power and Scalability
Running simulations and processing large datasets for DTs requires a lot of computing power.
If the infrastructure can’t scale with the complexity and volume of data, it’ll affect performance.
Kumar et al. make a good point: as digital twin models get more complex, the computational resources needed to maintain accuracy and performance also increase.
Petri et al. talk about a digital twin model with a layered architecture that includes a cloud-based system for real-time simulations and optimization tasks.
Network Latency and Connectivity
You need a reliable and fast network to make sure all your data can be shared seamlessly between the physical and digital sides of things.
Network latency can affect how quickly the digital twin responds, which might delay important insights.
Both Kumar et al. and Petri et al. stress the importance of having a solid network setup in place to support real-time data transmission and processing.
Security and Privacy Concerns
We’re seeing an uptick in the use of digital twins, so it’s more important than ever to make sure we’re protecting sensitive data.
Kumar and others have pointed out that DTs can contain sensitive data about the real-world systems they represent, making them vulnerable to cyber attacks.
To keep data safe and secure, it’s essential to have solid cybersecurity measures and data privacy protocols in place.
Petri et al. also point out the importance of keeping data secure, especially in applications involving critical infrastructure like buildings.
Key Performance Indicators (KPIs) for Digital Twins
The idea of Key Performance Indicators (KPIs) gives us a way to assess how well these digital models are doing.
One of the most important KPIs is accuracy. This ensures a digital twin accurately reflects the physical entity.
Kumar and his colleagues say precision is the name of the game when it comes to making these models reliable in real-world applications.
For instance, in manufacturing, an accurate digital twin can predict equipment failures before they occur, which helps to cut down on downtime and maintenance costs.
Latency is another important KPI, which refers to the delay in data transmission and processing.
Ioan Petri and colleagues point out how low latency is key for real-time applications, like monitoring building systems to adjust environmental controls right away.
In one example, a digital twin of a smart building was able to adjust the HVAC settings in real-time based on occupancy data, which led to a significant reduction in energy consumption.
Scalability is how well the digital twin can handle more data and more complex data without it affecting performance.
Petri and colleagues point out how scalability is important for the digital twin to be able to process a lot of data from lots of sensors and devices in large-scale setups, like city-wide energy management systems.
Another important KPI is reliability, which is about how consistent the performance and uptime are.
Absolutely. A reliable digital twin of a hospital’s infrastructure can ensure an uninterrupted power supply and optimal operation of critical systems, which is great for patient care.
Efficiency is all about how DTs can make operational processes more efficient. Both Kumar et al. and Petri et al. point out that efficient digital twins make the best use of resources and improve overall system performance.
Tools and Technologies for Performance Measurement
First of all, IoT platforms are pretty much essential. They help us collect, integrate, and analyze data from all kinds of sensors and devices.
These platforms are the foundation of digital twin operations, as they provide the real-time data needed for precise modeling.
Software like ANSYS and Dassault Systèmes is used to create detailed models of physical entities.
These tools let you test and validate digital twins in different situations to make sure they’ll work well in real life.
For instance, in the aerospace industry, simulation software can show how aircraft components behave under different stress conditions. This helps engineers design safer and more reliable systems.
Big data analytics platforms are key to crunching and understanding all the data that digital twins generate.
The advanced analytics are another big thing for improving how digital twins predict what’s going to happen.
Artificial intelligence (AI) and machine learning (ML) are other helping tools to make digital twins perform better.
Petri et al. talk about how using AI and ML in digital twin models lets you make more advanced predictions and optimizations.
Interested in learning more about the technology behind DTs? kindly read “Don’t Miss the Digital Twins Technology: A Sneak Peek Behind the Scenes.”
Strategies to Enhance Digital Twins Performance
Putting your money into top-notch sensors is the best way to make sure you get accurate and reliable data from physical things.
Petri et al. say that using precise sensors is key to getting the detailed data we need for effective DTs.
Next, we need to use edge computing to cut down on latency and make things more responsive in real time.
Kumar et al. show how processing data closer to the source makes decisions faster and reduces the load on central servers.
This approach is especially useful in industrial settings, where quick responses are essential.
Security protocols are a must to keep data in digital twins safe and private.
Both Kumar et al. and Petri et al. stress the importance of cybersecurity measures to prevent unauthorized access and data breaches.
In critical infrastructure systems like power grids, it’s crucial to ensure the security of digital twins to avoid potential cyber attacks that could disrupt operations.
To get the most out of data integration, we need to use standardized protocols and platforms for seamless data exchange.
Good data integration means that all the info from different sources gets put together and used properly, which makes digital twins work better overall.
At the end of the day, you’ve got to have the right infrastructure in place to handle all that data and keep digital twins running smoothly.
Scalable infrastructure is key for supporting the growing scope of digital twins as they’re used in larger and more complex systems.
Take smart cities, for example. Scalable infrastructure lets digital twins handle data from millions of sensors, which helps make urban management and planning more efficient.
How to Make Your Digital Twins Performance Strategy More Effective?
Let’s be real. Those strategies are pretty complex and they take your focus away from the core business
So, you might want to think about using Interscale as your BIM management support system.
We at Interscale offer a range of solutions that integrate data analytics, real-time monitoring, and robust cybersecurity measures to help you manage digital twins effectively.
We’re talking about accuracy, reliability, and data that isn’t just abundant, but also full of insights—which is what’s needed for your digital twin dreams to become a reality.
Our BIM and digital twins team of experts aren’t just tech whizzes; they know how to use it, making sure your digital twin projects are built on a solid foundation of data.
Now it’s time to look at the specifics and see how we can adjust to your company’s needs and goals.
As a starting point, we suggest you check out our Interscale BIM Management Support Service page.
Or we could get straight to the point. Schedule a one-on-one consultation with us. Let’s talk to figure out your specific challenges and create a digital twin strategy.
In Closing
By focusing on key performance indicators, using advanced tools and technologies, and applying effective strategies, organizations can improve the performance of digital twins.
But you know it’s not enough, right? You need a team of experts. That’s why you can count on Interscale as a partner.
How well a digital twin performance works out depends on you and the stakeholders. And yes, Interscale is ready to help you out.
