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Predicting the mean cycle time as a function of throughput and product mix is helpful in making the production planning for cluster tools. To predict the mean cycle time, detailed simulation models may be used. However, detailed models require much development time, and it may not be possible to estimate all model parameters. Instead of a detailed simulation model, we propose to use a so-called aggregate model to predict the mean cycle time as a function of throughput and product mix. The aggregate model is a lumped-parameter representation of the queueing system. We estimate the parameters of the aggregate model from arrival and departure data using the Effective Process Time (EPT) concept. The proposed method is illustrated for a simulation test case and a Crolles2 cluster tool workstation. The method accurately predicts the mean cycle time in a region around the workstations' operational product mix.