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Every digital twin is different, so you can achieve improved efficiency by understanding how they work and appropriate applications.
Just like real-life twins, every digital twin is different. That’s because a digital twin is a virtual replica of a physical asset — a living replica.
A digital twin as a concept doesn’t reflect one universal definition. For instance, we’ve identified at least 11 different types of digital twins typically applied in primarily three distinct phases — design, operation and maintenance. That equals more than 30 possible use cases — and that’s just in the manufacturing space. There are even more use cases if you also consider the installation/commissioning and decommissioning phases.
For example, some users rely on a digital twin to optimize the design of a product or manufacturing process, while others use it to optimize product production or production line maintenance.
Let’s clarify what digital twins are and how they’re referenced and applied in different scenarios so you can more quickly and uniformly realize the value.
When we use simulation, we focus on the factors important to us, such as the key performance indicators (KPIs) we want to improve. For example, you might simulate the operation of a car to predict its fuel efficiency measured in miles per gallon (mpg).
It’s the same idea with a digital twin, but a digital twin is a living “digital replica.” That means it’s learning and changing. So, with a digital twin, not only could you predict the car’s mpg when it’s driven off the lot, but you also could predict how and when the mpg will degrade.
In addition, you could predict when the car would need preventive maintenance to restore its peak performance, not just by mileage driven or time elapsed, but by maintaining a model of the wear based on many factors (mileage, time, temperature, driver behavior, etc.).
A digital twin provides great opportunity for use in numerous manufacturing applications.
Earlier I mentioned three phases in which digital twins are typically applied: design, operation and maintenance. Add to the complexity of those options the reality that you can have a digital twin of a device (such as drive or motor), process, manufacturing cell or machine, entire production line, plant or a series of plants (enterprise), people and customer behavior, and that gives you countless scenarios. Plus, no two are exactly the same.
To tap the potential of a digital twin, first make sure you and its users concur on the goal. With the car and mpg, for example, are you looking at the car’s engine, the exhaust, the gas tank, the entire car, or even the driver? Value comes from talking about the problem and agreeing on how to use the digital twin to solve that problem.
Now, how can you use the power of your digital twins? Remember, there are typically three phases — design, operations and maintenance — in which a digital twin is relevant.
Could the digital twin you developed to design a product be used to predict when maintenance will be required? leverage
Could the digital twin of a device be used in a digital twin of the operation of a machine or production line?
If you are using a digital twin now, I’m sure you’re already realizing benefits. However, there’s more if you expand your use and find ways to leverage the digital twin between phases.
Digital twins can create greater opportunity for manufacturing efficiency and create the foundation for predictive maintenance so you can maximize productivity.
The Journal From Rockwell Automation and Our PartnerNetwork™ is published by Putman Media, Inc.
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