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

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  • Jae-Dong Kim

    (School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of Korea
    Center for Defense Resource Management, Korea Institute for Defense Analyses, Seoul 02455, Republic of Korea
    These authors contributed equally to this work.)

  • Tae-Hyeong Kim

    (School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of Korea
    These authors contributed equally to this work.)

  • Sung Won Han

    (School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of Korea)

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.

Suggested Citation

  • Jae-Dong Kim & Tae-Hyeong Kim & Sung Won Han, 2023. "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks," Mathematics, MDPI, vol. 11(3), pages 1-10, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:501-:d:1038703
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    References listed on IDEAS

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    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    2. Regattieri, A. & Gamberi, M. & Gamberini, R. & Manzini, R., 2005. "Managing lumpy demand for aircraft spare parts," Journal of Air Transport Management, Elsevier, vol. 11(6), pages 426-431.
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    1. Ernesto Armando Pacheco-Velázquez & Manuel Robles-Cárdenas & Saúl Juárez Ordóñez & Abelardo Ernesto Damy Solís & Leopoldo Eduardo Cárdenas-Barrón, 2023. "A Heuristic Model for Spare Parts Stocking Based on Markov Chains," Mathematics, MDPI, vol. 11(16), pages 1-21, August.

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