This paper presents a new net-reduction methodology to facilitate the analysis of large workflow models. We propose an enhanced algorithm based on reducible subnet identification which preserves both soundness and completion time distribution. Moreover we outline an approach to model the dynamic behavior of business processes by exploiting the power of a class of non-Markovian stochastic Petri net models.