Validation of data fusion as a method for forecasting the regeneration workload for complex capital goods

The regeneration of complex capital goods is afflicted with a high degree of uncertainty. Neither the extent of the damage to the goods nor the resulting maintenance workload is known in advance, and that poses challenges for capacity planning. Data fusion in the form of Bayesian networks is used to prepare forecasts in order to estimate the workload in maintenance processes. The objective is to optimize the planability of the capacities required.

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