With exponentially increasing compute power, the proliferation of data producing sensors, and data production and consumption through devices like mobile phones, the ability to measure aspects of individual objects (cars, people, buildings) or processes (e.g., factory production lines, delivery logistics) is rapidly accelerating. Models can now be highly complex, and with a combination of high-performance computing and advances in real-time data streaming, physical objects can have a model that is close to itself both in time, and bespoke to an individual instance of a physical object. Welcome to the new kid on the block in the universe of mathematical models - the Digital Twin.
So how do Digital Twins fit into the world of modelling physical reality? A good way to think about this is to consider complexity versus uncertainty. Some systems have a simplicity about them whilst still being very uncertain. For example, in computational fluid dynamics, the rules governing the dynamics of an object moving through a medium, with fluidity like air, are simple, but there are a lot of unknowns when that object is propelled into space upwards through a thinning atmosphere.
Conversely, manufacturing a car is very complex but extremely well understood - which is helpful when something goes wrong in the manufacturing process. The situations where highly complex systems become very uncertain are the ones where digital twins play a unique role. In the subtle dance between the digital twin model and its physical counterpart, the model can tap into its predictive capability to test alternative more optimal possibilities, adjust reality, observe the subsequent outcomes and reduce uncertainty on the fly. As Dan Klein, Director of Data and AI at Zühlke, notes, "that fast feedback loop changes the game". When the physical objects are things like cars, ships, people, buildings, that speaks to radically modifying an approach to risk - and therefore changing the insurance paradigm.