Comprehensive Validation of the Regeneration Workload Forecast for Complex Capital Goods Using Data Mining
نویسندگان
چکیده
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.
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