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http://dx.doi.org/10.12985/ksaa.2018.26.4.101

An Empirical Study on Aircraft Repair Parts Prediction Model Using Machine Learning  

Lee, Chang-Ho (한서대학교 항공운항관리학과)
Kim, Woong-Yi (한서대학교 항공정보산업대학원)
Choi, Youn-Chul (한서대학교 항공교통물류학과)
Publication Information
Journal of the Korean Society for Aviation and Aeronautics / v.26, no.4, 2018 , pp. 101-109 More about this Journal
Abstract
In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.
Keywords
Aircraft repair parts; Demand forecasting; Time-Series analysis; Machine learning; Input variable;
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