Analysis of Aircraft Spare Parts Supply Chain Networks Using Machine Learning for Detecting Delivery Delay Patterns in Repair Processes
DOI:
https://doi.org/10.38035/sjam.v4i1.695Keywords:
Aircraft on Ground, Lead Time, Machine Learning, MRO, Supply ChainAbstract
The aircraft spare parts supply chain is highly complex and vulnerable to delivery delays that may trigger Aircraft on Ground (AOG) conditions and increase operational costs. This study aims to analyze the characteristics of the aircraft spare parts supply network and to model delivery delay patterns in the repair process using a data-driven machine learning approach. The dataset consists of 4,962 shipment records with variables including delivery status (on-time/delay), ship vendor, origin point, destination point, lead time, and lead time category. Three classification algorithms, namely Decision Tree, Random Forest, and Logistic Regression, are applied to build and compare delay prediction models. The research stages comprise data preprocessing, splitting data into training and testing sets, model development, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that operational variables in the supply chain significantly influence delivery status and that the Random Forest model provides the best performance in capturing complex and non-linear delay patterns. These findings offer a basis for developing predictive decision support systems to mitigate delivery risks and enhance the reliability of Maintenance, Repair, and Overhaul (MRO) processes in the aviation industry.References
Bills, K., Costello, L., & Cattani, M. (2023). Major aviation accident investigation methodologies used by ITSA members. Safety Science, 168. https://doi.org/10.1016/j.ssci.2023.106315
Daghistani, T., & Alshammari, R. (2020). Comparison of statistical logistic regression and randomforest machine learning techniques in predicting diabetes. Journal of Advances in Information Technology, 11(2), 78–83. https://doi.org/10.12720/jait.11.2.78-83
Damayanti, R., Ginting, G., Sudrajat, A., Md, H., & Islam, M. (2025). Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management. Jurnal Teknik Informatika (Jutif), 6(3), 1443–1456. https://doi.org/10.52436/1.JUTIF.2025.6.3.4689
Heijenrath, L. M., & Verhagen, W. J. C. (2023). The Influence of Repair Quality on Aircraft Spare Part Demand Variability. Aerospace 2023, Vol. 10, 10(8). https://doi.org/10.3390/AEROSPACE10080731
Khedr, A. M., & Sheeja, S. R. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100379. https://doi.org/10.1016/J.JOITMC.2024.100379
Monoarfa, M. I., Hariyanto, Y., & Rasyid, A. (2021). Analisis Penyebab Bottleneck pada Aliran Produksi Briquette Charcoal dengan Menggunakan Diagram Tulang Ikan. Jambura Industrial Review, 1(1), 2021. https://doi.org/10.XXXXX/jirev.vXiX.XX-XX
Nafiurridha, & Choiruddin, A. (2024). Classifying MRP Strategy of Aircraft Spare Parts Using Supervised Machine Learning. Procedia Computer Science, 234, 470–477. https://doi.org/10.1016/J.PROCS.2024.03.029
Polo Triana, S., Gutierrez, J. C., & Leon-Becerra, J. (2024). Integration of Machine Learning in the Supply Chain for Decision Making: A Systematic Literature Review. Journal of Industrial Engineering and Management, 17(2), 344–372. https://doi.org/10.3926/jiem.6403
Quinlan, J. R. (1986). Induction of Decision Trees. In Machine Learning (Vol. 1).
Rezki, N., & Mansouri, M. (2024). Machine Learning for Proactive Supply Chain Risk Management: Predicting Delays and Enhancing Operational Efficiency. Management Systems in Production Engineering, 32(3), 345–356. https://doi.org/10.2478/MSPE-2024-0033
Sattar, M. U., Dattana, V., Hasan, R., Mahmood, S., Khan, H. W., & Hussain, S. (2025). Enhancing Supply Chain Management: A Comparative Study of Machine Learning Techniques with Cost–Accuracy and ESG-Based Evaluation for Forecasting and Risk Mitigation. Sustainability 2025, Vol. 17, 17(13). https://doi.org/10.3390/SU17135772
Shafi, I., Sohail, A., Ahmad, J., Espinosa, J. C. M., López, L. A. D., Thompson, E. B., & Ashraf, I. (2023). Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety. Applied Sciences 2023, Vol. 13, 13(9). https://doi.org/10.3390/APP13095475
Soellaart, D. H. (2019). The Impact of Spare Parts Service Measures on the Performance Measures of a Wafer Fabrication Process. Retrieved June 21, 2025, from https://pure.tue.nl/ws/portalfiles/portal/118036442/Master_Thesis_Daniel_Soellaart.pdf
Vázquez-Veloso, A., Toraño Caicoya, A., Bravo, F., Biber, P., Uhl, E., & Pretzsch, H. (2025). Does machine learning outperform logistic regression in predicting individual tree mortality? Ecological Informatics, 88. https://doi.org/10.1016/j.ecoinf.2025.103140
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