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http://dx.doi.org/10.5762/KAIS.2021.22.3.234

A Study on Intermittent Demand Forecasting of Patriot Spare Parts Using Data Mining  

Park, Cheonkyu (2nd Air Defense Missile Brigade, Airforce)
Ma, Jungmok (Department of Defense Science, Korea National Defense University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.3, 2021 , pp. 234-241 More about this Journal
Abstract
By recognizing the importance of demand forecasting, the military is conducting many studies to improve the prediction accuracy for repair parts. Demand forecasting for repair parts is becoming a very important factor in budgeting and equipment availability. On the other hand, the demand for intermittent repair parts that have not constant sizes and intervals with the time series model currently used in the military is difficult to predict. This paper proposes a method to improve the prediction accuracy for intermittent repair parts of the Patriot. The authors collected intermittent repair parts data by classifying the demand types of 701 repair parts from 2013 to 2019. The temperature and operating time identified as external factors that can affect the failure were selected as input variables. The prediction accuracy was measured using both time series models and data mining models. As a result, the prediction accuracy of the data mining models was higher than that of the time series models, and the multilayer perceptron model showed the best performance.
Keywords
Data Mining; Intermittent Spare Parts; Demand Forecasting; MultiLayer Perceptron; Patriot;
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