A Proposed Demand Forecasting Method for Aircraft Maintenance Spare Parts in Aircraft Maintenance Operations: A Case Study
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Abstract
This study aims to analyze the demand patterns of aircraft spare parts used in unscheduled maintenance and to propose appropriate forecasting methods tailored to each demand type. Historical usage data spanning five years (2018 to 2022) were collected for 58 distinct spare parts. The demand classification was conducted using two key indicators, namely the Average Demand Interval (ADI) and the Squared Coefficient of Variation (CV²), which facilitated the categorization into Smooth, Intermittent, Erratic, and Lumpy demand patterns. Following the classification, suitable forecasting techniques were applied to each demand type. Exponential Smoothing was used for Smooth demand, the Bootstrap Method was applied to Intermittent demand, and Croston’s Method was adopted for both Erratic and Lumpy demand. Forecast accuracy was assessed by comparing the Mean Squared Error (MSE) of each technique with the conventional Moving Average method currently in use. The results indicate that the majority of the spare parts, accounting for 65.52 percent, exhibit an Intermittent demand pattern. The proposed pattern-specific forecasting techniques demonstrated notable improvements in accuracy: for the dominant Intermittent group, the Bootstrap Method reduced forecast error (MSE) by approximately 42.61%; Exponential Smoothing reduced MSE for Smooth demand by 15.91%, and Croston’s Method yielded an 11.75% improvement for Erratic demand. However, accuracy for the Lumpy item declined due to model limitations. Overall, this tailored approach led to an average forecast error reduction of 18.37% across all items compared to the conventional Moving Average method. These findings emphasize the importance of aligning forecasting techniques with actual demand characteristics to improve prediction accuracy. The proposed approach improves inventory decision-making in aircraft maintenance operations and shows potential for broader application in industries facing similar demand irregularities.
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References
Baisariyev, M., Bakytzhanuly, A., Serik, Y., Mukhanova, B., & CT, P. (2021, 15–18 June). Demand forecasting methods for spare parts logistics for aviation: A real-world implementation of the bootstrap method. Paper presented at the 30th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2021), Athens, Greece.
Bookbinder, J. H., & Lordahl, A. E. (1989). Estimation of inventory re-order levels using the bootstrap statistical procedure. IIE Transactions, 21(4), 302–312. https://doi.org/10.1080/0740817890 8966236
Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289–303
Fan, L., Liu, X., Mao, W., Yang, K., & Song, Z. (2023). Spare parts demand forecasting method based on intermittent feature adaptation. Entropy, 25(5), 764. https://doi.org/10.3390/e25050764
Harimansyah, F. R., & Imaroh, T. S. (2020). Aircraft spare parts inventory management analysis on airframe product using continuous review methods. Dinasti International Journal of Management Science, 2(1), 81–90.
International Air Transport Association. (2015). IATA guidance material and best practices for inventory management. Montreal, Canada: IATA.
Jahin, M. A., Shahriar, A., & Al Amin, M. (2025). MCDFN: Supply chain demand forecasting via an explainable multi-channel data fusion network model. Evolutionary Intelligence, 18(66). https://doi.org/10.1007/s12065-025-01053-7
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, 13(9), 5475. https://doi.org/10.3390/app13095475
Thummathid, S. (2020). A comparison of forecasting methods for rotable spare parts in a Thai low-cost airline Chulalongkorn University Theses and Dissertations (Chula ETD). 185. https://digital.car.ch ula.ac.th/chulaetd/185
Watanabe, W. C. (2023). Logistics and supply chain quantitative analysis and its applications [in Thai]. Naresuan University Press.
Willemain, T. R., Smart, C. N., & Schwarz, H. F. (2004). A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20, 375–387.
