Black Founders Programme
The Black Founders Programme is an accelerator targeted at pre-seed or seed stage, Black-founded companies creating innovative products and services...
Imagine you are the owner of a manufacturing company and one of your most important pieces of machinery breaks down. What do you do? Your first port of call would usually be to find a specialist nearby who may be able to fix it. You book an available time slot with them. They travel to your premises, take apart the machine, and diagnose the problem.
But what if, instead, you could establish the problem quickly and remotely, without the need for someone to travel to your site or dismantle your machinery? Or what if you had been able to predict that your machine might fail and had replaced the necessary parts ahead of time? These are just a few examples of how digital twins will soon transform how many industries operate.
What are Digital Twins?
Put simply, a digital twin is a virtual representation of a real-world asset or process. This could be anything from a piece of software to a machine, a building, or federated digital twins addressing an entire industrial supply chain.
From an oilfield or a construction site, to a network of buildings or even an entire city, the value of advanced cyber physical systems is gaining recognition across the board. The same digital twin technologies used in many industrial applications have also been used by doctors to create digital versions of human organs, they’ve been used by forestry management companies to model forests, and they’ve been used to create omniverses that simulate physical processes. They can be applied to many situations, and combined to address many issues.
Digital twins share the same qualities as existing tools like digital models, but they take this technology one step further, taking information and updates from its real counterpart using sensors and other IoT and 5G devices and connectivity. Using AI/ML and simulation, the digital twin can be used to remotely monitor processes and productivity, identifying improvements and malfunctions, carrying out ‘what if’ analyses, predicting events and suggesting solutions.
The twin is essentially a living and breathing pre- and post-production model that evolves along with the physical asset for the entire duration of its lifetime. Tests and simulations or even training, however, can be carried out on the digital counterpart without affecting the item or system in the real-world.
How are Digital Twins useful?
One of the most obvious benefits of using digital twins is their efficiency in predictive maintenance. The process of maintaining and repairing an asset is enhanced significantly, automating the prediction of when parts will fail and the diagnostics and for unknown issues remote access and visualisation allowing it to be analysed in real time by the best qualified person anywhere in the world. And quicker diagnostics means less downtime.
Even the likelihood of malfunction is minimised. With a digitised version available for testing, simulations can be conducted at any time, enabling engineers to effectively predict and prevent problems before they have a chance to arise.
The digital twin can gather information integrating IoT sensors, visualisation and high performance networks. The virtual model can analyse this information to monitor ‘wear and tear’ and simulate deterioration on an ongoing basis, without the need for periodic visual inspections, which can have large margins of error.
This combination of monitoring and simulation is particularly useful for large assets, such as bridges, buildings. These entities can be tested through simulations throughout their life-cycle: at the design phase, during construction to map against design ambitions, and then during operation – to ensure everything is working optimally.
Taking action on supply crises
With supply chains in disarray and resiliency measures rendered uncertain, many businesses have struggled to adapt to shifts in global supply networks. Changes to supply chain processes are fraught with risk, with companies often unwilling or unable to take the chance and move to a new model.
Digital twins can use data simulations to gather information and test the robustness of supply chains during the digital modelling phase – prior to their implementation. Adjustments made at this stage can significantly reduce risks by allowing companies to predict and solve issues early on, saving them time, money, and their reputation further down the line. This can be extended in life with digital models connected across organisational boundaries to optimise and adjust the supply chains as the supply and demand shifts in different places.
