Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks

Jae-Dong Kim, Tae-Hyeong Kim and Sung Won Han ()
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Jae-Dong Kim: School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of Korea
Tae-Hyeong Kim: School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of Korea
Sung Won Han: School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of Korea

Mathematics, 2023, vol. 11, issue 3, 1-10

Abstract: The proportion of the inventory range associated with spare parts is often considered in the industrial context. Therefore, even minor improvements in forecasting the demand for spare parts can lead to substantial cost savings. Despite notable research efforts, demand forecasting remains challenging, especially in areas with irregular demand patterns, such as military logistics. Thus, an advanced model for accurately forecasting this demand was developed in this study. The K-X tank is one of the Republic of Korea Army’s third generation main battle tanks. Data about the spare part consumption of 1,053,422 transactional data points stored in a military logistics management system were obtained. Demand forecasting classification models were developed to exploit machine learning, stacked generalization, and time series as baseline methods. Additionally, various stacked generalizations were established in spare part demand forecasting. The results demonstrated that a suitable selection of methods could help enhance the performance of the forecasting models in this domain.

Keywords: spare parts; demand forecast; deep learning; logistics; stacking (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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