머신러닝 기반 Lumpy 수요형태의 항공기 수리부속 수요예측 정확도 개선 연구

Abstract

For the spare parts of aircraft "A" operated by ROKAF(Republic of Korea Air Force), this research suggests the application of machine learning technique rather than that of time series technique in order to improve demand forecast accuracy of spare parts whose demand pattern is lumpy. Demand patterns are divided into four categories by applying Average Inter-Demand Interval(ADI) and Coefficient of Variation(CV). Only for the lumpy parts, this study applies various analysis models selected from demand history, and the probability of demand occurrence for each period for lumpy parts is predicted by applying machine learning techniques. As a result of the experiment, lumpy or erratic pattern appeared to be higher in aircraft spare parts, and smooth parts with stable demand appeared relatively lower. In addition, logistics regression, linear discriminant analysis, and decision tree technique showed a relatively better accuracy, and machine learning technique improved prediction accuracy about 5% higher than time series technique, thus proving that machine learning technique is an effective technique to for lumpy spare parts demand forecast.