In case you haven’t noticed, manufacturing and heavy industry are changing.
A combination of mounting market pressure to reduce costs and innovate faster, and the accessibility of technology like the industrial internet of things (IoT), digital threads, digital twins, and model-based engineering has forced adoption and transformation.
In this article, we drill into one of these game-changing ideas and look at digital twins: what they are, how they work, and why manufacturers should care.
What Are Digital Twins?
Digital twins (usually written as “a digital twin”) is based on the idea that you have a digital copy of a tangible object you’re building. And this digital copy exactly reflects the real thing.
It also reflects its tangible counterpart in real-time, updating its digital representation as new information flows in, and reflecting the process that the real-world object is going through.
Consider this analogy: it’s as if you have a handheld model of a Ferrari that mirrors a full-size Ferrari being built, and also a model of the manufacturing process and the post-sale life of the product.
So if the person who buys the Ferrari crashes it into a telephone pole, your handheld model also crumples. Except in the case of a digital twin, it’s a digital representation — perhaps a 3D model you can view in AR — rather than a handheld model.
That’s the basic idea, but it’s worth exploring this a little more. To do that, we need to understand how complex products like cars, jet planes, and iPhones are actually made.
Generally speaking, they’re not made by the person or brand you buy them from. Apple doesn’t make the iPhone and GM doesn’t really make cars.
Design? Absolutely.
But manufacture? Not really.
Rather, brands like Apple and GM are assemblers. They contract out all of the bits to their products, then assemble them at a factory. For GM, this means different companies might build the chassis, the wheels, the steering column, the lights, the seats, etc., and GM comes in at the end, assembles the car, and sells it.
The process is similar with Apple and iPhones.
That’s part of the reason the paradigm of digital twins is increasingly popular: supply chains are growing increasingly more complicated, so creating a shared digital version of the real object that updates in real time makes everyone’s lives easier (not to mention it reduces errors and mistakes).
Second, digital twins help complete the product loop. Normally, once a product is sold, the organization who sold it stops collecting data. It means that when they’re building the next version, they’re doing it without quantitative real-world feedback.
Digital twins, which provide feedback via the IoT to manufacturers, provide a data stream that can be leveraged to inform future product innovation.
For example, if you had a digital twin of the crashed Ferrari, you might receive data from sensors on the vehicle about the wreck, and reinforce the front bumper on future versions to better withstand force.
How Do Digital Twins Work?
Now that the benefits of digital twins are clear, let’s explore how they work.
The basic idea for getting digital twins to work is by loading your products up with a multitude of sensors providing feedback, which are passed into the cloud, and which you then collect in real-time to create an image of how the product is being used.
A simple application makes this easy to understand. Let’s say you’re a company that makes sensors. Specifically, you make the temperature sensors used inside industrial ovens (say, to cure the paint on a car). The paint being cured correctly and lasting the right length of time depends on your sensor being accurate.
Normally, you test and retest before deployment, and then… that’s it. It will probably work – after all, you make good sensors. But if/when it starts to fail, you won’t actually know.
This problem has traditionally been solved in two ways:
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Making products that last a LOT longer than their use-by day (e.g. saying ‘this sensor will last 10 years’ when your testing suggests it’ll last 20).
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Auditing and reviewing parts and processes manually on a regular basis (e.g. someone going in and annually testing the sensor).
Obviously, neither of these is efficient.
Now imagine you were going the digital twin route. You would load a sensor onto your sensor, providing real-time feedback on performance.
Thus, you can complete the product innovation loop, using that data to inform future product development.
Over time, digital twins have become much more complicated, pulling and aggregating data from multiple sources. The goal is to create such a robust environment that products can be tested in a completely digital way, vastly improving time-to-market and thus, a company’s bottom line.
Why Digital Twins are Happening Now
The short answer is that digital twins haven’t been possible until recently. Secure, inexpensive, and readily available cloud technology, enormously expanded IoT, sensor, and data gathering capabilities, and the digitalization of everyday products like cars has made the dream of digital twins more of a reality.
Over time, the connection of different aspects of our lives, including smart devices, smart cities, and smart homes, will drive digital twins even further.
Final Thoughts: Why Digital Twins Matter
So why do digital twins matter? The short answer is: there’s money to be made.
Consumers and businesses demand ever-faster innovation and new product development. There’s a limit if you need to test products “in real life”.
If you can go digital – and have enough data to do that well and in a completely regulatory-compliant way – then you can achieve enormous efficiencies in the new product innovation timeline.
