Spare Parts Demand Forecasting and Inventory Management: Contributions to Intermittent Demand Forecasting, Installed Base Information and Shutdown Maintenance

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Capital goods are expensive machines or products that are used by manufacturers to produce their end-products. Examples include computers, production equipment, aircrafts and lithography machines that are used by semi-conductor manufacturers. Availability of spare parts is essential to facilitate their maintenance both to correct failures as well as to prevent these. Large spare parts inventories however, tie up significant capital and face the risk of obsolescence. Hence smart decisions are needed on inventories: when to stock and in which quantity. These decisions should be based on good forecasts.

In this dissertation we present three contributions to this problem. First, a new method based on extreme-value theory is developed to aid companies in forecasting the spare parts demand distribution. Next, we analyze the inventory control problem for on-condition maintenance and shutdown maintenance. We propose a new approach for joint forecasting and inventory control based on probabilistic information on the maintenance plan. We found the value of this plan to be significant in preparing the repair shop by catching the irregularity and lumpiness of spare parts demand. Finally, we model the spare parts ordering problem against the background of shutdown maintenance project planning. Decision makers need strategies which consider the interdependence of maintenance activities. Our new stochastic programming approach is able to give much better advice than traditional methods and hence meets the requirement of real-life shutdown projects.