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.
Digital twin as a concept does not reflect one universal definition — and we get into trouble when we assume our audience shares our view.
I’ve discovered at least 11 different types of digital twin applied to three distinct phases — design, operation and maintenance — and that equals more than 30 possible use cases, just in the manufacturing space.
For example, some users rely on digital twin to optimize the design of a product or manufacturing process, while others use it to optimize production of a product or maintenance of a production line.
My goal here is to improve clarity around digital twin – how it’s referenced and applied in different scenarios – so users can more quickly and uniformly realize the value.
When we use simulation, we focus on the things that are important to us, the KPI we want to improve. For example, you might simulate the operation of a car to predict its miles-per-gallon (MPG).
It’s the same idea with digital twin, but it is a living “digital replica,” so that means its learning and changing. So with a digital twin, not only could you predict the car’s MPG when it drives out of the showroom, but you could predict how and when the MPG will degrade.
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 and driver behavior are just a few).
Leveraging the Difference
Digital twin has great opportunity to be used in manufacturing in so many different ways.
Earlier I mentioned three phrases. Now add to the complexity of those options the reality that you can have a digital twin of a device (drive or motor), process, manufacturing cell or machine, entire production line, plant or a series of plants (enterprise), people and customer behavior, and you have countless scenarios — and no two are exactly the same.
To tap the potential of digital twin, first make sure you and your audience concur on the view. With the car and MPG example, are you looking the car’s engine, the exhaust, the gas tank, the entire car — or even the driver? Value comes from talking about the (same) problem, and agreeing on how digital twin will be used to solve that problem.
Extend Your Digital Twin
Now, how can you extend your digital twins? Remember, there are three phases (design, operations and maintenance) where digital twin is relevant.
Could the digital twin you developed to design a product be used to predict when maintenance will be required?
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 digital twin now, I’m sure you’re already realizing benefits. But there’s more if you expand your use and find ways to leverage digital twin between phases.
Digital twins will create greater opportunity for manufacturing efficiency, and create the foundation for predictive maintenance so you can maximize productivity.