Twinning myths

There is a lot of hype around digital twins and at least two misconceptions that raise questions and throw up barriers to adoption. The first of them is that digital twins are simply virtual representations of assets, and by that I mean a sensor, component or IT system. The second is that the technology cannot be applied to either large-scale enterprise systems, processes, people or places.

The concept of a true digital twin is remarkably simple and can be summed up in one sentence: a digital twin is a virtual representation of any physical entity. In manufacturing, they are simply a digital evolutionary extension of connecting a dial or a meter to a thermocouple or a pressure sensor, for example. Or for IT managers, it’s about monitoring what’s going on in the network. And that’s the basis of the first myth. The transition is smarter than just the replicating legacy technologies.

The notion of digital twinning opens up a new world interacting with the physical world. This is because digital twins broker access to data sources, add meaning through semantic data models and extract events to be shared within and beyond organizational boundaries. This generates actionable insights in real-time that can be communicated to stakeholders in the supply chain, clients and partners. It’s a lot more that a digital display.

The digital twin creates a virtualisation of any data point – people, places, processes, things – that can communicate with other data points efficiency and without compromising the original data source. These data-based virtualisations also include alle the controls necessary for the data to be read and utilised directly y machines, which is integral to a twin’s ability to autonomously inter-operate and, as a result, enrich customer-centric services. Their power lies not in what they can physically show us, but how they can securely and meaningfully interact with each other to create secure, scalable, adaptable digital ecosystems.

Digital twins aren’t intended to replace existing technology. Instead, they extend capabilities, increase flexibility and mitigate the risk of businesses failing. Rolls-Royce-Power Systems is harnessing digital twin and event data technology, to unlock more than 200 data sources, brokering interactions to create digital twins of theirs in-field assets and to receive real-time event insights across customer, supplier and partner boundaries.

The second myth is that scale is a limiting factor. Whatever the blend of technology, people and assets, enterprises can be modelled at any scale. From there, the digital twins grant access to each of these data points individually or as a network of sources that can combine in unlimited ways. It is leveraging data in a way that provides real-time insight into demand, supply, performance and operations. It is the ability for connected objects to work together to provide entirely new services. It is theoretically possible to extend the twinning ecosystem to an entire business, country or planet, while NTT is currently working on digital twins of human organs.

But it is the combination of scale and interactivity that holds the real potential for digital twins, beyond simple visualisations or live versions if entire processes, assets and environments. But as interactions between twins increases, so does the complexity. Paul Miller at Forrester, in his paper Untangle the Digital Twin as Part of Your Product Strategy, uses the analogy of Russian nesting dolls to highlight how complications can stack up in hierarchical levels.

Transport example takes us to the core of it

In the transport sector, such nesting can be visualised in the following sequence: individual components (for example, fan blades) from a part of more complex assets (turbines), nested inside larger system assets (engines), inside asset platforms (planes), which then form part of a service (airline route), which takes its place as part of a digital ecosystem (transportation).

Irrespective of its size, each twin plays a role of equal importance and has its value, depending on a user’s role in the supply and demand chain, needs and focus. Factor in that the twins could be owned and operated by multiple entities and the difficulties of navigating or mapping their interrelations and interactions becomes apparent. The doll analogy, while useful, is nowhere near perfect because in the world of digital twinning, not everything is the same ‘shape’. A way of looking at this is that a train is not a carriage, an engine or turbocharger, and these assets probably won’t be made by the same company or be registered on the same systems. Also, the assumption that everything nests one-on-one breaks down when we consider that the train under scrutiny has multiple carriages. Then, there may be more than one dimension to nesting. Manufacturers make trains and operations run trains: but manufacturers lease trains more than one operator, while operators run trains from more than one manufacturer.

Ultimately, the key benefit lies not so much with what the technology can do or what problem it solves, but with what it enables us to create by harnessing the power of connection. Suddenly, a whole new world of data is available, liberating us from operational silos. If we can avoid misconceptions around scale and function, digital twinning lets us focus on what matters.