Business twinspiration — the rise of the ‘digital twin’

Organisations the world over are turning to the digital twin to improve their decision-making, but exactly why are they doubling up?

Catriona Campbell
4 min readJan 21, 2022

One of the most amazing things about certain emerging technologies, including Artificial Intelligence and the Internet of Things, is that they’re bringing to life, in the most unexpected ways, older innovations previously hamstrung by the technological constraints of their day. One such innovation is digital twins — and I’m not talking about the sort of digital twins, aka avatars, you might expect to find in the metaverse.

Digital twins are virtual copies of real-world entities (either objects or systems like trains, wind turbines, factories or supply chains), which span their lifecycle, and they’re commonly applied in a range of industries from manufacturing to logistics. The concept was developed two decades ago, and after a long period of relatively slow progress, it has finally taken off in recent years thanks to not only rapid advances in AI and IoT but also the vast troves of data available today.

Accenture named digital twins one of its top five strategic tech trends to watch in 2021, and EY recognises the innovation as especially important in the wake of disruptions caused by the pandemic. Discussing now outdated supply chains built for a simpler, less volatile world that no longer exists, Sachin Lulla and Dheera Anand say digital twins help companies pivot faster thanks to quicker, improved decision-making, while Jim Ashby and Matthew Brown argue it helps them change at a rate suitable for their business model.

Lulla and Anand also cite a couple of different EY clients whose supply chains have been improved by the use of digital twins, one of which is a global chemical manufacturer. EY helped the firm leverage satellite analytics to monitor key ports and run lane analytics to allow for better decision-making and service levels.

Organisations across the globe are increasingly turning to digital twins to gain a competitive advantage, which has created a huge and growing mirrored world. In this new world, change is powered by unlocking the trapped value of data, allowing companies to close the gap between digital and physical through previously impossible simulation, prediction and automation.

Supply chains aside, let’s look at an example of a physical asset that a digital twin can improve: a plane. A plane can be outfitted with sensors that generate real-time data about any aspect of the aircraft’s performance, which can be mapped onto a digital twin. Using that twin, simulations can then be run, taking variables like weather and differing load into account, and operational improvements can be identified using algorithms.

All of this can be achieved without the need to disrupt the operation of the actual vehicle, meaning it’s a safer, more efficient way of understanding how assets behave, how to extend the lifespan of existing assets, and how to design enhanced next-generation assets.

For a real-world example of this, just look at the US Air Force. In 2020, it wrapped up a project involving the use of digital twins as a prelude to designing, building and testing a full-scale prototype of its future fighter jet in just over a year. Unsurprisingly, conducting tests and ‘what-if’ scenarios on a virtual replica of a fighter jet proved far less expensive, time-consuming and labour-intensive than trialling multiple physical aircraft as it has in the past.

In the fictional and real-world examples above, I often refer to assets in the singular, but the plural is more appropriate. That’s because the reason for this tech gaining real momentum of late is organisations learning how to scale it to get more out of entire asset networks — vehicle fleets, offshore wind farms, etc — and not just single assets.

This, in itself, is absolutely incredible. However, what if I told you that we can improve the performance of the core algorithms that underpin these and most other physical simulations and consequently run these simulations faster? Well, we can — and not just by a little, but instead by an epic 1000 times. Such enhanced capability is down to a discovery made by MIT researchers a decade ago, which turned into a company called Akselos, now helping other firms improve their digital twins and access all sorts of business advantages.

And that brings us nicely back to innovations bringing other innovations to life. Much like AI helped allow the concept of digital twins to prosper in the first place, this speed-up means businesses can apply AI to its full potential within their simulations, thus leading to better digital twins. Love a bit of symbiosis!



Catriona Campbell

Behavioural psychologist; AI-quisitive; EY UK&I Client Technology & Innovation Officer. Views my own & don't represent EY’s position.