Digital twins could be more than doubly good for efficiency, safety and sustainability at work, if harnessed with foresight.
Digital twins are defined as virtual representations of an object or system, designed to reflect the physical object accurately. Typically, it will span the true object’s lifecycle and will be updated in real-time using simulations, machine learning and reasoning, in order to make smart decisions. The twin is then used as a source of research, information and activity, in order to make improvements to the original model.
That is basically a long way of saying that digital twins have great potential to impact how we live, work and innovate. There is really no industry that their reach can’t touch and with that in mind, how might digital twins affect or alter the world of work as we move further into 2025?
Predictive maintenance
For many STEM industries, the ability to fix what isn’t yet broken and avoid errors is of key importance. Predictive maintenance does just that. Sensors embedded into physical models can send data to their digital counterparts, predicting if and when certain components are likely to experience wear and failure.
By compiling data and building up evidence in order to identify a pattern, digital twins can identify when a model is going to break down, thus allowing for regular maintenance and a longer life for the object. It also means there are less costs for an organisation and engineers may not be as overworked.
Remote monitoring
Remote and hybrid working has become an incredibly important perk for professionals, yet for many people, particularly those in areas that demand the use of highly advanced technologies, it may not have been a possibility. Digital twins are ushering in a new era of remote monitoring and control.
Powered by IoT devices and sensors, digital twins can collect data from physical locations anywhere in the world, which is then fed into the digital model, allowing for continuous monitoring of assets.
Not only will this give employees the chance to engage with work in different parts of the world, remotely, it also means that there is less risk to employees who need to acquire resources, data etc from dangerous locations. This could have an impact on industries that largely depend on assets located in unsafe places, for example in regions with dangerous weather events such as active volcanoes, or high seas.
Leaner, greener workforce
Digital twins are a part of the automatisation of the workforce, but rather than replacing human employees, they have the potential to be a useful tool to be wielded by a skilled professional, not a replacement for one. By integrating AI and automation tools with digital twins, organisations can better manage workflows, identify bottlenecks, suggest optimisations and even make decisions based on what would be a more effective route.
They can also drive sustainability for organisations looking to reduce their carbon footprint. Digital twins can optimise energy usage, monitor waste and record resource consumption, enabling companies to improve how they implement sustainability initiatives. Also, by building digital twins in lieu of real-world assets, organisations are using less resources and are therefore less likely to waste materials in the early stages of a new project.
New and improved learning
As industries advance, the skills needed to stay informed grow alongside them, therefore STEM professionals may find that they need to become adept in a broad range of skills quickly. Digital twins are an ideal vehicle through which employees can build foundational knowledge.
By practicing on virtual models and processes, instead of holding back for fear of damaging equipment or even causing injuries, professionals can work confidently knowing they are not incurring any real risk. This is a great option for professionals who work in particularly hazardous fields, for example in a lab with toxic or corrosive materials.
In the right hands, digital twins could be a powerful training mechanism, issuing in an exciting new way to learn and engage with work.
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