Computers and Operations Research
The inventory management system for automobile spare parts in a central warehouse
Expert Systems with Applications: An International Journal
Extended beta-binomial model for demand forecasting of multiple slow-moving inventory items
International Journal of Systems Science - Production Coordination and Inventory Policies
Decision support system for optimizing spare parts forecasting for training aircrafts
MATH'09 Proceedings of the 14th WSEAS International Conference on Applied mathematics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
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Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline fleets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to different experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used, in order to compare different forecasting methods when facing intermittent demand. The results confirm the continued superiority of the weighted moving average, Holt and Croston method for intermittent demand, whereas most commonly used methods by airlines are found to be questionable, consistently producing poor forecasting performance. We have, however, devised a new approach to forecasting evaluation, a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand. A simple example is presented to illustrate the performance of the mathematical model. It is suggested that these findings may be applicable to other industrial sectors, which have similar demand patterns to those of airlines.