Intermittent demand forecasting for aircraft inventories: a study of Brazilian’s Boeing 737NG aircraft´s spare parts management

Abstract

This paper aims to compare and evaluate five different methods for predicting intermittent demand using spare parts recorded series of the 737 Next Generation aircraft, manufactured by Boeing, of the largest Brazilian air fleet managed by VRG Airline Company S/A. The Winter, Croston, Single Exponential Smoothing, Weight Moving Average and Poisson Distribution Methods were tested on a history data of 53 spare parts, and each one has a demand history of thirty-six months (January 2013 to December 2015). The results showed that the Weight Moving Average, Poisson Distribution and Croston methods presented the best adjustments. Also, it was observed that most of the demands for spare parts presented a smooth pattern unlike the result obtained by the study of Ghobbar and Friend (2003) that presented a lumpy pattern. On the other hand, it showed that the Winter Method presented the worst adjustment in both studies. It was possible, therefore, to conclude that Weight Moving Average and Poisson Distribution methods are the most suitable to evaluate the intermittent demand for the VRG Airline Company S/A case.

Keywords

• Intermittent demand
• Boeing 737NG
• Spare parts
• Aeronautical Maintenance.