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Capacity planning in the regeneration of complex capital goods faces major challenges because it is affected by a high level of uncertain workload information. A methodology is developed here to predict the regeneration workload on the basis of the CRISP-DM model using Bayesian networks. The forecasts are validated for the different capacity planning levels. The results support the conclusion that capacity planning can gain permanent benefits from the methodology developed.doi:10.24178/ijbamr.2015.1.2.01 fatcat:p6fexwr5gzdm3og72u67npahra