Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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An Empirical Study on Improving the Accuracy of Demand Forecasting Based on Multi-Machine Learning

다중 머신러닝 기법을 활용한 무기체계 수리부속 수요예측 정확도 개선에 관한 실증연구

  • Received : 2023.12.12
  • Accepted : 2024.04.25
  • Published : 2024.06.05
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Abstract

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

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References

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