Digital Twins Are Here to Save the World

“Digital twin” is more than just a cool-sounding word – it’s got the cool functionality to match.

As a digital replica of a physical asset, system, or process, digital twins are able to take a static digital model and update it with information gathered from the Internet of Things (IoT) sensors or other information sources in order to create a “living, breathing” digital model that reflects the condition of the physical object.

Digital twins can provide benefits for a multitude of different use cases across multiple industries, but are we only just getting started when it comes to fully tapping into their potential?

“As built” vs. Actual Condition

Digital twins have already been put to good use in a variety of settings, from manufacturing to architecture, engineering, and construction (AEC). A shipbuilder, for example, might produce four identical sister ships that all were constructed using the same blueprint and whose “as built” condition is documented in a digital model. 

As those ships travel around the world on their various journeys, they’ll all experience different environmental conditions, stresses, and strains – and overlaying that information on the original digital model creates an accurate and up-to-date digital twin that informs what shape the ship is in, what kind of proactive repairs it might require, and how many more years of service it likely has in front of it.

Buildings, Bodies, and Better Decisions

Meanwhile, in the AEC space, the owner of a newly built office tower can create a digital twin by taking the building information modeling (BIM) model and layering in information gathered from sensors that are located throughout the building. These sensors can keep tabs on everything from the temperature and humidity level, to the number of round trips the elevators have made and how much vibration they’ve generated – all the better to effectively maintain the building and stay ahead of any operational issues.

Why stop with objects and buildings, though? You can also create a digital twin of a person. This isn’t nearly as far-fetched as it seems: Many people already wear some kind of smartwatch or device that constantly monitors various factors – from heart rate to blood oxygen level – and feeds it into an aggregated health profile of the individual.

The net result in all these scenarios is that end users are able to make better-informed decisions because they are basing their decisions on actual data rather than guesses. Technically speaking, however, what does it take to reach this transformative nirvana?

Know Your Digital Twins

The foundation of any digital twin is a model, whether it’s a CAD model, a BIM model, or some other form of data. If customers aren’t able to effectively access this data, they can’t create a digital twin, which is why the ability to share detailed and information-rich models is so important.

Once users are able to get their hands on this data, however, they can start to create digital twins of varying levels of complexity. For example, in a manufacturing context, the most straightforward type of digital twin would be for a single part, like the component inside a machine. One level up in complexity, you would have an asset that contains multiple parts, like a piece of equipment. 

The next level of complexity is a system, which might be a building that has multiple assets operating within it. And at the top of the heap, from a complexity standpoint, is a process. Think here of a factory or a production line that has lots of different assets and systems interacting with one another at all times. 

In addition to the level of complexity being modeled, we can also classify digital twins by how “automated” they are at pulling in data. In this regard, digital twins can exist on a spectrum from “descriptive” – where a model is manually updated with information by a human to reflect any changes – all the way to “autonomous”, where the twin isn’t just automatically pulling in data from sensors to update the model, but is capable of performing simulations and making predictions. 

It's once digital twins head away from “descriptive” towards the “autonomous” end of the spectrum that the magic happens.

Predict and Prevent

In the case of a bridge, predictive capabilities might mean the ability to foretell when a crucial component within the bridge structure is going to fail so that it can be replaced before disaster strikes. 

In a manufacturing plant, this might look like the ability to measure the quality of outputs in real-time and adjust various aspects of the entire process accordingly – from the speed of the conveyor belt, to the ambient temperature in the facility, to the amount of pressure applied by a stamping machine – so that resources aren’t wasted producing widgets that don’t adhere to required tolerances. 

And with bodies, these types of predictive capabilities might mean the ability to analyze an incoming stream of health data and spot the signs of a potential stroke or heart attack hours or even days before they happen. As the saying goes, an ounce of prevention is worth a pound of cure.

The Best is Yet to Come

From an initial beachhead in the manufacturing and AEC spaces, digital twins are poised to play an increasingly large role not just in shaping the world we live in but in our actual lives, making inroads into areas like healthcare as well as spaces we haven’t even imagined yet. As long as digital twins continue to find innovative new ways to serve up real-time information about people, objects, and processes in the physical world, it’s likely that the future of digital twins is still unwritten – and the best is yet to come.