Precise estimation of part failures, and hence system breakdowns, is required to reduce or avoid the downtimes of machines and stock-outs. If detected sufficiently early, potentially broken parts of a machine may even lead to not necessarily having to store the respective spare part. For estimating the spare parts demand as accurately as possible, this article conceptualizes an approach for integrating condition monitoring information provided by Intelligent Maintenance Systems and forecasting methods. This concept is validated based on an example evaluation. Thus, the research objective is to improve the forecasting quality of estimated machine breakdowns in order to enhance the planning of the spare parts supply chain.
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