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

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.

A Heuristic Model for Spare Parts Stocking Based on Markov Chains

“…Baghizadeh [31] introduced an inventory model for essential spare parts based on a Markov chain process model for the situation of supplier interruption. Kim et al [32] proposed demand forecasting models for spare parts by applying artificial intelligence. Other relevant research works [33][34][35][36] can also be consulted by the readers.…”