Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/501 |
| _version_ | 1827759969327382528 |
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| author | Jae-Dong Kim Tae-Hyeong Kim Sung Won Han |
| author_facet | Jae-Dong Kim Tae-Hyeong Kim Sung Won Han |
| author_sort | Jae-Dong Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:35:17Z |
| format | Article |
| id | doaj.art-27bbedf515d34b44956bd97be43b16be |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:35:17Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-27bbedf515d34b44956bd97be43b16be2023-11-16T17:20:29ZengMDPI AGMathematics2227-73902023-01-0111350110.3390/math11030501Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X TanksJae-Dong Kim0Tae-Hyeong Kim1Sung Won Han2School of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of KoreaSchool of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of KoreaSchool of Industrial and Management Engineering, Korea University, Seoul 02481, Republic of KoreaThe 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.https://www.mdpi.com/2227-7390/11/3/501spare partsdemand forecastdeep learninglogisticsstacking |
| spellingShingle | Jae-Dong Kim Tae-Hyeong Kim Sung Won Han Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks Mathematics spare parts demand forecast deep learning logistics stacking |
| title | Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks |
| title_full | Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks |
| title_fullStr | Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks |
| title_full_unstemmed | Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks |
| title_short | Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks |
| title_sort | demand forecasting of spare parts using artificial intelligence a case study of k x tanks |
| topic | spare parts demand forecast deep learning logistics stacking |
| url | https://www.mdpi.com/2227-7390/11/3/501 |
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