Business process robustness describes a process’s ability to meet performance targets despite variations in parameters such as arrival rates or resource availability. Assessing business process robustness quantitatively is challenging because dependencies between process parameters and performance are complex and stochastic. Business process simulation avoids the need to specify those dependencies by simulating the process and observing the resulting behavior. However, the space of possible parameter combinations grows quickly, which makes it challenging to identify parameters that still meet the required performance targets. In this paper, we address this challenge by proposing a simulation- based approach that systematically explores parameter configurations to efficiently identify those that maintain acceptable performance. Our evaluation demonstrates that the proposed approach substantially reduces assessment complexity compared to our baselines while accurately iden- tifying parameter combinations under which a process remains robust. The approach provides users with insights into how parameter variations influence performance, enabling a step toward a quantitative foundation for assessing and improving business process robustness.

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