Rehearsing a change before it touches the real system
Because a digital twin tracks the actual current state of a specific robot or system, it can be used for something a generic simulation can't do as convincingly: testing a proposed real-world change against the exact configuration and condition the physical system is in right now, before committing to it. Suppose an operations team wants to increase a robot's cycle speed by fifteen percent to meet higher demand. Instead of simply trying it on the shop floor and watching for problems, they can run that change against the twin, which is already carrying the robot's real current wear state, real payload characteristics, and real environmental constraints, and observe what the twin predicts will happen.
This matters because the twin's starting point isn't a clean, idealized version of the robot, it's the robot as it actually is today, including whatever wear or drift has already accumulated. A change that would be perfectly safe on a brand-new unit might push an already-worn joint past a safe stress threshold, and a twin that has been tracking that specific unit's real condition is positioned to catch that, where a generic simulation of 'a robot of this model' would have no way to know the unit in question isn't factory-fresh.