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Workflow management becomes increasingly important in today's information-oriented society. An important research area of workflow management is performance analysis that is driven by the need for improved efficiency of business processes. The study of the performance of a workflow process, as the focus of this paper, requires the estimation of the duration of tasks, which is often unpredictable and nondeterministic. Current research in this field has focused on workflow stochastic Petri nets (PNs)-which are a class of workflow nets with exponential distributed execution times assigned to transitions. In this paper, in order to deal with this uncertainty, we use fuzzy estimators constructed from statistical data to describe time, and we present an analytical method to proceed with the performance evaluation of workflow stochastic PNs based on block reduction. A comparison example is provided to show the benefits of the proposed method.