Parallel universe: how digital twins will populate the planet

concept of digital twin or internet of things
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Next year sees us celebrate 100-years since the first Sci-Fi robot movie in 1921. Although it was of course a silent black and white production, The Mechanical Man was one of our first chances to see robots in action. Had the early Italian filmmakers behind this project known what lay ahead, they might have concocted a storyline more creative than two robots fighting each other. But hey, you’ve got to give the audience a thrill.

Between 1921 and 2021 a lot has happened. The upheavals of 2020 notwithstanding, this has been a period of immense change and digitisation. As we now put hardware and software robots to work in digitally transformed data-driven organisations, we need to stand back and consider what roles they are going to play, what responsibilities we are going to task them with and what human workflows they are going to straddle, join and in some cases replace.

We said hardware and software robots there quite deliberately. The development of hardware robots with pneumatic and hydraulic powers is a story in its own right; where we are focusing our attention here is the development of software robots, automation bots and digital twins. Ultimately, many of these software robots ‘feed’ the hardware robots with intelligence and control actions anyway, but let’s consider digital twin technologies in more detail and look for the major defining factors on this sector of the total industry roadmap.

Most people think of digital twins in the context of the Internet of Things (IoT). We can create a software model-based replica of an electronic device or industrial machine and then manipulate and test this model in a historical or real-time context. This is a process which is substantially cheaper, faster, safer – and often more insightful at a granular level - than modifying the physical component out in the field itself.

The impact of changes can be quickly determined, various combinations of input values and settings can be tested without requiring complex setup changes or access to the device itself and outputs can be optimised according to the goals of the twin before any change is made to the physical component.

Digital twins 2.0 take on human roles & processes

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