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A method is developed to forecast material demand caused by aircraft non-routine maintenance. Non-routine material consumption is linked to scheduled maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the non-routine maintenance forecast method, comprising the non-routine material consumption forecasts linked to scheduled maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming maintenance checks.
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