Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

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

데이터 마이닝을 이용한 패트리어트 수리부속의 간헐적 수요 예측에 관한 연구

  • Park, Cheonkyu (2nd Air Defense Missile Brigade, Airforce) ;
  • Ma, Jungmok (Department of Defense Science, Korea National Defense University)
  • 박천규 (공군 제2방공유도탄여단) ;
  • 마정목 (국방대학교 국방과학학과)
  • Received : 2020.12.11
  • Accepted : 2021.03.05
  • Published : 2021.03.31
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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.

군에서는 수요예측에 대한 중요성을 인식하여 수리부속에 대해 예측 정확도 향상을 위한 많은 연구가 이루어지고 있다. 수리부속 수요예측은 예산 운영과 장비 가동률 측면에서 매우 중요한 요소가 되고 있다. 그러나 현재 군에서 적용중인 시계열 모형으로는 수요량의 변동과 발생주기가 일정하지 않은 간헐적 수요에 대해서는 예측에 한계가 있는 실정이다. 따라서, 본 연구는 공군 패트리어트 수리부속의 간헐적 수요에 대한 예측 정확도를 제고하는 방법을 제시하고자 하였다. 이를 위해서 2013년부터 2019년까지의 701개의 수리부속 소모개수를 토대로 수요 유형을 구분하여 수리부속의 간헐적 수요 자료를 수집하였다. 또한, 장비 고장에 영향을 줄 수 있는 외부 요인으로는 기온, 장비운영시간을 식별하여 입력변수로 선정하였다. 그 후, 소모개수와 외부 요인을 통해 군에서 적용하는 시계열 모형과 제안하는 데이터 마이닝 모형으로 예측을 실시하여 모형별 예측 정확도를 판단했다. 예측 결과로 기존의 시계열 모형과 비교하여 데이터 마이닝 모형의 예측 정확도가 높았으며, 그 중 다층 퍼셉트론 모형이 가장 우수한 성능을 보였다.

Keywords

References

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