Forecasting methods for lumpy demand of aircraft spare parts

An accurate demand forecasting is a critical issue of the industrial plants management. This study analyses the behaviour of forecasting techniques when dealing with lumpy demand, measured by the square coefficient of variation (CV) and the average inter-demand interval (ADI). In particular different forecasting techniques are considered: actual historical data from the Italian national flag airline are used for their performance analysis and comparison. This study demonstrate that the item lumpiness is a dominant parameter. The results attest that demand forecasting for lumpy items is a very complex problem and results obtained by existing approaches are not very accurate. Anyway, the Seasonal Regression Model (SRM), the Exponentially Weighted Moving Average (EWMA(i)) and Winters model reveal the best approaches for the prediction of spare pat-is demand of airline fleet.