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http://dx.doi.org/10.7472/jksii.2017.18.1.121

A Study on Forecasting Spare Parts Demand based on Data-Mining  

Kim, Jaedong (Center for Defense Management, Korea Institute for Defense Analyses)
Lee, Hanjun (Center for Defense Acquisition, Korea Institute for Defense Analyses)
Publication Information
Journal of Internet Computing and Services / v.18, no.1, 2017 , pp. 121-129 More about this Journal
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.
Keywords
demand forecasting; data mining; logistics; spare part;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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