Analyzing and processing intelligent maintenance systems data to use for planning spare parts supply chains

An Intelligent Maintenance System (IMS) provides data about the status of technical devices. Based on sensorial input, IMS analyzes the data and forecasts failures of these devices. The forecasted failures can be used to improve the spare parts demand forecast results as the gathered condition monitoring information provides a more accurate prognosis about the status of the technical devices. The enhanced demand forecasts in the next step build the foundation for planning the activities like inventory, transport, and maintenance management planning along a spare parts supply chain more adequately. To use the data provided by an IMS for forecasting the spare parts demand, the IMS data has to be analyzed and processed as the current existing IMS does not generate condition monitoring information in the data type, which is needed for enhancing the spare parts demand forecast. For that reason, this paper proposes how the IMS can be adapted for generating the needed data type. This is done by developing an IMS simulator based on the same IMS architecture used in the field. An IMS simulator is developed as in real scenarios restrictions are existing concerning the data collection by the installed IMS in the field.