The Industries Doubling Down on Digital Twins

The age of industry 4.0 is well underway, and the powerful impact of leveraging data to measure, predict, and constantly improve operations is well-accepted in modern business. 

One of the most common ways to do this is through leveraging the Internet of Things (IoT), which creates a network of internet-facing objects across a business, whether that be within a warehouse, a water processing plant, or a shop floor. These devices then feed vital data across the network and to a central hub to inform decisions around productivity. 

But the rise of digital twins in industry 4.0 takes this concept one step further. Digital twin technology uses a range of sensors and data to build a real-time digital replica of a physical object or process, one that can predict outcomes with a high level of accuracy, spot inefficiencies, and can even trial new approaches or operational strategies without impacting on output. 

It is no surprise, then, that digital twins are finding their way into the digitalisation strategies of all kinds of organisations, including companies like Renault and Unilever and even whole cities, such as Shanghai. So, what are the use cases?

Boosting productivity

Efficiency is what drives success in all businesses, but in the industries that make up Critical National Infrastructure, efficiency and reliability is vital. 

General Electric (GE)’s renewable energy business uses a digital twin of its wind farm, allowing the company to optimise each active wind turbine and even inform the design of future turbines to generate higher output. GE reports that doing this can increase energy production by as much as 20%, equating to an incredible $100 million in additional revenue per turbine. 

And of course, one way to improve efficiency is to remove downtime. The sensors on each wind turbine can also relay live data on when one turbine may need maintenance. This allows field engineers, equipped with rugged, reliable mobile devices, to be proactive in maintaining turbines, while ensuring network uptime with maximum predictability. For offshore and remote windfarms, this is especially helpful in allowing field engineers to prioritise time and resources, only making the trips when they are truly needed, helping to reduce overall downtime and waste.