Do you have a twin?
Perhaps you do, perhaps not. But I’d bet maybe you don’t have a digital twin.
And that’s too bad, because digital twins are really pushing into exciting territory. Imagine being able to predict exactly what you’d look like in decades to come, or being able to pre-emptively circumvent health concerns before they became an issue.
We’re not quite there yet in terms of healthcare. But for developing products, building infrastructure, or even constructing dynamic smart cities, this is an absolute reality. As technology evolves, businesses are discovering new worlds of sustainability through virtual counterparts.
What is a digital twin?
But let’s start with the basics. Simply put, a digital twin is a virtual representation of a physical object.
This goes far beyond a mere image or CAD model of an object. In fact, a more accurate term would probably be ‘data twin’: a virtual representation in a virtual environment that can feed back information and analytics in near real time.
Feeding back information is the important bit. Because it essentially means you have a live model of an object that you can test and experiment on to your heart’s content – and then apply the data you receive back to real objects.
It could be a children’s toy. A car. A piece of manufacturing equipment. Even an entire building. With enough processing power, a digital twin can be created of any object – and crucially, it can feedback on the status of that object at any point of its lifecycle.
The traditional use case of digital twins has been in manufacturing and utilities. Just think about how many stages there are in the product lifecycle: conception, design, build, quality control, and even aftercare. The data gathered from digital twins can be fed back into each of these stages, and used to dramatically reduce costs, improve productivity, and operational resilience.
But the uses don’t stop there. The real potential of digital twins come when multiple models work together.
For example, the real innovators aren’t just building virtual cars. There’re building virtual traffic lights that interact with those cars. Not to mention virtual railways, virtual garbage collectors, and even virtual roadworks to build an entire digital twin of a transport system.
The future is digital twins interacting with digital twins. From utilities to transport, finance and construction, industries can construct deep virtual environments, and make key businesses decisions based on data gathered from simulations. Moving beyond understanding what has happened, or being predictive about what is about to happen to what is the next based action and being prescriptive about what to do next.
A sustainable future
Sustainability is increasingly important in today’s world. Whether it be from an environmental or business viewpoint, the key tenets of creating a sustainable future are the same: power consumption, longevity, and the use of human and natural resources.
Optimisation is a key driver of sustainability, and this is where digital twins excel. All too often, a well-intentioned process change has unintended consequences further down the line. But by building an eco-system of digital twins, resource usage can be monitored holistically. Organisations can assess the full impact of any proposed changes, thus helping them make a better-informed decision.
Another aspect of optimisation is predictive maintenance. Imagine the impact when a key piece of equipment fails in a factory. With a digital twin, businesses can understand the resilience of that equipment. This enables them to anticipate its failure, and pre-emptively order a replacement – thus avoiding costly downtime as loss of productivity.
The human factor
Technology is of course key to digital twins. Sensors, networks (including NB-IoT and 5G), edge, cloud, and AI all play important roles. But just as important is the cultural element.
A digital twin is only as good as it’s data. But all too often, proper data governance isn’t standard. That’s why I encourage businesses to incentivise employees to collect good quality, timely data. Reward them, or at the very least make the process as frictionless a possible.
Another obstacle I often see is initial unwillingness to invest. Business buy-in is always a challenge with technology. And to that I say: start small. Building digital twins can be a gradual, iterative process. Start with a business problem. Prove the twin’s worth. Then gradually evolve your project to tackle more ambitious challenges.
One step at a time
By starting small, businesses can start building digital twins without any difficultly – particularly when working with experts like Fujitsu who hold many patents in this area. Engaging with technology like AI may feel daunting. But by doing so, businesses can unlock the sustainability and optimisation that’s so important in today’s competitive, eco-conscious age.