Interscale Content Hub – Digital twins in manufacturing help companies make better products, improve quality, and cut down on downtime. These advantages could lead to big cost savings and efficiency gains.
In recent years, there’s been a big increase in the use of digital twins, thanks to major advances in IoT, AI, and machine learning.
A 2024 report by Global Market Insights says that the global market for digital twin technology will grow from $9.9 billion in 2023 to $125.1 billion by 2032. That’s a compound annual growth rate (CAGR) of 33%.
This impressive growth shows that more and more manufacturers are realizing that digital twins are a great way to gain a competitive edge.
What are Digital Twins in Manufacturing?
Digital twins are basically virtual replicas of physical assets, processes, or systems that use real-time data and simulations to mirror and predict how their physical counterparts will perform and behave.
This technology is a big deal in manufacturing. It lets companies create digital models of everything from individual components to entire production lines.
With digital twins, manufacturers can visualize, monitor, and optimize their processes without needing physical prototypes.
For instance, a manufacturer can use a digital twin to test out different production scenarios to find the most efficient process or to predict when machinery will need maintenance to avoid costly downtime.
There are some great benefits to using digital twins in manufacturing.
According to McKinsey in “Digital twins: The key to smart product development,” Companies that use digital twins have slashed product development times by 20-50%, cut the number of prototypes needed, and up product quality, which means fewer defects when products hit the market.
On top of that, these companies have seen a 3-5% boost in sales thanks to the new features and happier customers.
How Digital Twins Work in Manufacturing
Digital twins are made possible by combining different technologies, including sensors, the Internet of Things (IoT), big data analytics, and artificial intelligence (AI).
Sensors built into physical assets collect data on how they’re performing, what condition they’re in, and what the environment is like.
This data is sent to the digital twin, which uses advanced analytics and machine learning algorithms to process it, run different scenarios, predict outcomes, and give you actionable insights.
This closed-loop system keeps the digital twin up to date, so it always reflects the current state of the physical asset.
For example, a digital twin of a manufacturing robot can keep an eye on how efficiently it’s working, predict when it might need maintenance, and make adjustments to improve its performance in real-time.
This not only makes the robot more efficient but also helps it last longer and avoid downtime.
In the “Digital Twins in Manufacturing” webinar presentation slides, Dr. Milind Siddhpura said the idea behind digital twins is to connect the real and virtual worlds.
They collect real-time sensor data from IoT-connected sensors, store it in the cloud, and then use it to perform simulations that optimize product performance.
You might find it helpful to take a look at “Beyond Your Average Virtual Replica: Real Types of Digital Twins in Project.”
What is the Framework for a Digital Twin in Manufacturing?
If you’re looking to implement a digital twin in manufacturing, you’ll need to have a solid framework in place. This will involve a few key steps.
The process kicks off with data collection, where we gather real-time data from various sensors and sources.
This data includes info about how well the physical assets are performing, how they’re holding up, and what the environment is like around them.
For example, in a manufacturing setup, sensors attached to machinery collect data on things like temperature, vibration, and operational efficiency.
Next, data integration is really important. We then bring all the data together from different systems and sources to create one big, comprehensive dataset.
For instance, combining data from production lines, supply chain management systems, and maintenance logs gives you a complete picture of the manufacturing process.
The third step is modeling and simulation. Advanced simulation tools and algorithms are used to create a digital model of the physical asset or system.
This digital model is basically a replica of the physical one.
For instance, a digital model of a robotic arm can simulate its movements, flagging potential bottlenecks and inefficiencies before they happen in the real world.
Once you’ve got the digital model up and running, you can start using analytics and optimization.
Machine learning and predictive analytics are used to analyze the data and optimize processes.
This step helps us spot patterns, predict future performance, and suggest improvements.
For instance, predictive maintenance can be scheduled based on the analysis, which helps to reduce downtime and extend the lifespan of machinery.
The last thing we need to do is set up a feedback loop. The digital twin gets updated with real-world data all the time, so it always stays an accurate representation of the physical asset.
This dynamic update mechanism lets you monitor and optimize in real time, adapting to changes and ensuring peak performance.
Dr. Milind Siddhpura breaks down how digital twins work at three levels: the digital model, the digital shadow, and the digital twin.
The digital model is a static representation that doesn’t automatically exchange data. The digital shadow has a one-way data flow from the physical object to the digital counterpart.
The digital twin is different because it has a two-way data flow, which means the physical and digital entities can interact and optimize in real time.
This advanced level makes sure that any changes to the physical asset are reflected in the digital twin right away, and vice versa. This helps with proactive maintenance and operational efficiency.
As a reference to how good Azure is in digital twins, please refer to “How Azure Digital Twins is Your Building’s Doppelganger & Why It’s Great,”
Challenges of Digital Twin Adoption in Manufacturing
While digital twins are great, they’re not without their challenges when it comes to adoption.
The high initial costs, lack of standardized frameworks, and the need for significant changes in organizational processes are the main obstacles.
Building a digital twin takes a lot of hardware, software, and skilled personnel.
Also, it can be tough to integrate digital twin technology with existing IT infrastructure.
On top of that, there are data security and privacy issues because digital twins involve a lot of data collection and sharing.
Manufacturers need to make sure they have good cybersecurity measures in place to protect sensitive information from being breached or accessed by the wrong people.
In the McKinsey & Company article, Roberto Argolini, Federico Bonalumi, Johannes Deichmann, and Stefania Pellegrinelli say overcoming these challenges often requires a phased approach.
This includes the first steps, which are about choosing the right technology, designing the architecture, and integrating it all. Then, there’s the organizational transformation to support the new processes and working practices.
Dr. Siddhpura also points out some other hurdles, like making sure the data is good quality, keeping it safe and secure, and building trust in digital twin technologies.
He says it’s crucial to have standardized and domain-specific modeling to make sure everything works together and is effective.
How to Tackle Those Digital Twins Challenges?
There’s no doubt we can achieve great things with digital twins, but it’s not without its challenges.
From getting all the data together to real-time analytics, managing a digital twin takes some special know-how and a solid platform.
What if there was a way to make the whole process easier and get the most out of digital twins?
At Interscale, we’ve got you a complete BIM management system that makes your whole process of implementing and managing digital twins a breeze.
Our platform makes it easy for you and your manufacturer’s team of all sizes to use advanced simulation tools, real-time data analytics, and cloud-based collaboration features.
Wouldn’t it be great to make your processes more efficient, cut costs, and improve product quality?
The customized solutions from Interscale give your manufacturing teams the power to achieve just that.
Our work with K2LD Architects shows what we can do when we put our minds to it.
We create solutions that are just right for our clients and manage complex digital twin projects.
So, kindly visit our Interscale BIM Management Support Service page to learn more about our comprehensive solutions.
Or, if you have any questions or need some one-on-one discussions, just let us know—we’re here for you.
In Closing
As with anything new, there are still a few hurdles to overcome, like data security and initial investment costs.
But the advantages are clear: digital twins can really help you work more efficiently, cut costs, and stay ahead of the competition in the fast-changing digital world.
And consider bringing Interscale into your whole operation as a great way to make digital twins in manufacturing a critical component.
