The logic of digital twin technology can be applied to challenges as diverse as supply chains, retail merchandising, customer churn propensity, risk modelling, fraud detection, cybercrime, fluid dynamics and personalised gene therapy.
In sectors like energy and manufacturing, digital twins is already helping to design better products with improved resource management, which will benefit the environment.
Updated with real-time data, digital twins will also allow companies to assess and predict the performance of components and complex systems throughout their lifecycle.
Increasingly, the technology will be used to optimise processes, enable improved operator training and fine tune predictive maintenance. Running “what if” scenarios within digital twins enables potentially unlimited horizons for optimisation.
The sector is predicted to grow by around 37% during the next five years, as enterprises seek to ensure they are not left behind.
Dhana Vadivelan, EMEA Partner SA Leader, AWS, says efficiency is a key driver for clients.
He says: “First, they are focused on how to produce a better product in terms of higher quality and lower cost. Second: how rapidly they can actually come up with new products and new designs that cater to customer need.”
Vadivelan describes the technology as being reliant upon “a range of other technologies that have evolved independently and in parallel”.
The list of these technologies includes automation, Internet of Things, 5G and the cloud.
For example, digital twins involve much more data-driven complexity than a 3D model of the kind CAD software might create. The velocity, variety and volume of real-time data required to drive the technology creates challenges in terms of ingesting, curation and aggregation.
Vadivelan foresees the emergence of industry-specific LLMs trained on “billions or trillions of parameters” that will be able to explore “countless permutations and combinations” for operational processes. In some cases, these LLMs will process real-time data sets thrown off by fully instrumented equipment on the factory floor or shop floor. And in cases where seamless real-time data isn’t always available, enterprises will rely upon LLMs to generate synthetic data.
So how can this work in practice?
Telecom tower companies used IBM’s Digital Twin platform on AWS to improve efficiency and reduce time-to-market.
The technology enabled companies to capture high-resolution images of telecom towers, 3D reconstructions, 3D business information modelling and more. IBM extensively used AWS cloud native services for clients on that platform.
Businesses were also able to integrate this data – which also included AI models, analytics and weather services – in their enterprise systems.
The combination of digital twin technology and AI-driven decision-making could benefit a wide range of industries.
However, digital twins are best described as a coalition technology, heavily dependent upon on adjacent competencies, including highly scalable computational resources, extremely high levels of end-to-end digitisation and skilled management of huge real time data flows.
As a result, the organisations that win the race to reap the benefits of this technology will be those with a significant level of pre-existing investment in data-driven technologies that make digital twin simulation possible.
How digital twin technology is changing complex industrial processes forever
Changing complex business processes or trying to innovate amid a sophisticated ecosystem poses risks of disruption and escalating costs. This explains why IT leaders with a remit to transform their businesses are increasingly turning to digital twin technology to effectively simulate big changes before they go live.
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