• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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A Study on Forecasting Spare Parts Demand based on Data-Mining


Jaedong Kim, Hanjun Lee, Journal of Internet Computing and Services, Vol. 18, No. 1, pp. 121-130, Feb. 2017
10.7472/jksii.2017.18.1.121, Full Text:  HTML
Keywords: demand forecasting, Data Mining, logistics, spare part

Abstract

Demand forecasting is one of the most critical tasks in defense logistics, because the failure of the task can bring about a huge waste of budget. Up to date, ROK-MND(Republic of Korea - Ministry of National Defense) has analyzed past component consumption data with time-series techniques to predict each component's demand. However, the accuracy of the prediction still needs to be improved. In our study, we attempted to find consumption pattern using data mining techniques. We gathered an 18,476 component consumption data first, and then derived diverse features to utilize them in identification of demanding patterns in the consumption data. The results show that our approach improves demand forecasting with higher accuracy.


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Cite this article
[APA Style]
Kim, J. & Lee, H. (2017). A Study on Forecasting Spare Parts Demand based on Data-Mining. Journal of Internet Computing and Services, 18(1), 121-130. DOI: 10.7472/jksii.2017.18.1.121.

[IEEE Style]
J. Kim and H. Lee, "A Study on Forecasting Spare Parts Demand based on Data-Mining," Journal of Internet Computing and Services, vol. 18, no. 1, pp. 121-130, 2017. DOI: 10.7472/jksii.2017.18.1.121.

[ACM Style]
Jaedong Kim and Hanjun Lee. 2017. A Study on Forecasting Spare Parts Demand based on Data-Mining. Journal of Internet Computing and Services, 18, 1, (2017), 121-130. DOI: 10.7472/jksii.2017.18.1.121.