In event-based control systems, actuator operations are typically triggered by physical sensor signals to ensure process safety. However, this can lead to systematic delays, especially in pneumatic systems, due to system-related reaction times. This paper presents a method to compensate such delays by statistically advancing control signals without the need for additional sensors. A bidirectionally coupled digital twin, based on virtual commissioning models, continuously evaluates live data and derives optimized advance signals during operation. The method integrates event-based control with time-based triggering and employs a hybrid approach combining empirical quantiles with analytical confidence intervals to maintain safe operation. Validated in a testbed under realistic industrial conditions, the approach achieves an average time saving of 315ms per compensated transition. The system architecture decouples real-time PLC execution from non-real-time analysis, enabling scalable integration with diverse control and simulation platforms. The results demonstrate the potential of digital twins for active, data-driven optimization in discrete manufacturing systems.