Forecasting Consumable Part for Aircraft Maintenance using Time Series Method
Main Article Content
This study discusses the inventory of consumable parts at an aircraft maintenance company in Bandung. The problem that occurs is that companies often face stock shortages. Another issue is that the warehouse has excess stock of materials that have an expiration date, which is financially detrimental to the company. To overcome this, the study categorizes materials using the ABC and ADI CV2 analysis methods. After obtaining the demand patterns including smooth demand, intermittent demand, erratic demand, and lumpy demand forecasting is carried out according to the demand pattern using Exponential Smoothing, Croston, Syntetos Boylan Approximation, and Teunter Syntetos Babai. The data used is PT XYZ consumable part data from January 2022 to December 2023. After analysis and testing the level of forecast accuracy using MAD, MSE, and MAPE, the results showed that the Croston, SBA, and TSB forecasting methods each offer their own advantages based on the specific characteristics of the demand pattern applied.
Keywords:
Inventory, Consumable Parts, Aircraft Maintenance Company
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?ahin, M., Eldemir, F., & Turkyilmaz, A. (2022a). Inventory Cost Minimization of Spare Parts in Aviation Industry. Transportation Research Procedia, 59, 29–37. https://doi.org/10.1016/j.trpro.2021.11.094
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Sondeng, M. U. H. F., Mandagie, K. L., & Tedja, D. A. N. W. (2021). Analisis Permintaan Nose Tire Boeing 737-300 Menggunakan Metode Peramalan Dan Economic Order Quantity (Eoq). Jurnal TeknikIndustri, 10(2), 86–94. https://doi.org/10.35968/jtin/v11i1/789
Svetunkov, I., & Boylan, J. E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265(August), 109013. https://doi.org/10.1016/j.ijpe.2023.109013
Xiao, J., Ma, S., Wang, S., & Huang, G. Q. (2024). Meta-inventory management decisions: A theoretical model. International Journal of Production Economics, 275(July), 109339. https://doi.org/10.1016/j.ijpe.2024.109339
Yang, Y., Ding, C., Lee, S., Yu, L., & Ma, F. (2021). A modified Teunter-Syntetos-Babai method for intermittent demand forecasting. Journal of Management Science and Engineering, 6(1), 53–63. https://doi.org/10.1016/j.jmse.2021.02.008
Babai, M. Z., Dallery, Y., Boubaker, S., & Kalai, R. (2019a). A new method to forecast intermittent demand in the presence of inventory obsolescence. International Journal of Production Economics, 209(January 2018), 30–41. https://doi.org/10.1016/j.ijpe.2018.01.026
Babai, M. Z., Dallery, Y., Boubaker, S., & Kalai, R. (2019b). International Journal of Production Economics A new method to forecast intermittent demand in the presence of inventory obsolescence. International Journal of Production Economics, 209(January 2018), 30–41. https://doi.org/10.1016/j.ijpe.2018.01.026
Cakmak, E., & Guney, E. (2023). Spare parts inventory classification using Neutrosophic Fuzzy EDAS method in the aviation industry. Expert Systems with Applications, 224(March), 120008. https://doi.org/10.1016/j.eswa.2023.120008
CAO, K., ZHANG, Y., & FENG, J. (2024). Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion. Chinese Journal of Aeronautics, 38(1), 103219. https://doi.org/10.1016/j.cja.2024.08.050
Chien, C. F., Ku, C. C., & Lu, Y. Y. (2023). Ensemble learning for demand forecast of After-Market spare parts to empower data-driven value chain and an empirical study. Computers and Industrial Engineering, 185(October), 109670. https://doi.org/10.1016/j.cie.2023.109670
da Silva, L. F., Costa, I. P. de A., de Oliveira, T. E. S., Rangel, G. C., Lucas, F. F., da Costa, L. M. A., … dos Santos, M. (2024). Demand Forecasting for Steel Company Spare Items with Time Series Templates. Procedia Computer Science, 242, 57–64. https://doi.org/10.1016/j.procs.2024.08.230
Doszyn, M. (2021). Expert systems in intermittent demand forecasting. Procedia Computer Science, 192, 3598–3606. https://doi.org/10.1016/j.procs.2021.09.133
Doszy?, M., & Dudek, A. (2024). Outliers in intermittent demand forecasting. Procedia Computer Science, 246(C), 4114–4122. https://doi.org/10.1016/j.procs.2024.09.250
Ekin, E. (2022). An Application on Demand Forecasting and Stock Control for Aircraft Components. Havac?l?k ve Uzay Çal??malar? Dergisi, 3(1), 1–40. https://doi.org/10.52995/jass.1122940
Flávio, J., Almeida, D. F., Cássia, J., & Lima, D. (2024). heuristics and bootstrapping ?. Decision Analytics Journal, 10(December 2023), 100415. https://doi.org/10.1016/j.dajour.2024.100415
Halteh, K., AlKhoury, R., Adel Ziadat, S., Gepp, A., & Kumar, K. (2024). Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry. Transportation Research Interdisciplinary Perspectives, 24(April 2023), 101043. https://doi.org/10.1016/j.trip.2024.101043
?fraz, M., Aktepe, A., Ersöz, S., & Çetinyoku?, T. (2023). Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet. Journal of Engineering Research (Kuwait), 11(2). https://doi.org/10.1016/j.jer.2023.100057
Kaya, G. O., Sahin, M., & Demirel, O. F. (2020). Intermittent demand forecasting: a guideline for method selection. S?dhan?, 45(1), 51. https://doi.org/10.1007/s12046-020-1285-8
Kumar, L., Sharma, K., & Khedlekar, U. K. (2024). Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method. Results in Control and Optimization, 15(November 2023), 100432. https://doi.org/10.1016/j.rico.2024.100432
M. H. Kibria. (2020). Inventory Cost Minimization Through Item Categorization and Demand Forecasting: A Case Study-based Approach. International Journal of Engineering Research and, V8(12), 823–827. https://doi.org/10.17577/ijertv8is120386
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4), 1346–1364. https://doi.org/10.1016/j.ijforecast.2021.11.013
Marpaung, K. T. P., Rusgiyono, A., & Wilandari, Y. (2023). Perbandingan Metode Holt Winter’S Exponential Smoothing Dan Extreme Learning Machine Untuk Peramalan Jumlah Barang Yang Dimuat Pada Penerbangan Domestik Di Bandara Utama Soekarno Hatta. Jurnal Gaussian, 11(3), 439–446. https://doi.org/10.14710/j.gauss.11.3.439-446
Mz, B., Tsakalerou, M., & Ct, P. (2021). ScienceDirect ScienceDirect Demand forecasting methods for spare parts logistics for aviation?: a real-world implementation of the Bootstrap method. Procedia Manufacturing, 55(2019), 500–506. https://doi.org/10.1016/j.promfg.2021.10.068
Nafiurridha, & Choiruddin, A. (2024). Classifying MRP Strategy of Aircraft Spare Parts Using Supervised Machine Learning. Procedia Computer Science, 234(2023), 470–477. https://doi.org/10.1016/j.procs.2024.03.029
Pinçe, Ç., Turrini, L., & Meissner, J. (2021). Intermittent demand forecasting for spare parts: A Critical review. Omega (United Kingdom), 105. https://doi.org/10.1016/j.omega.2021.102513
Pradini, D., & Kusumastuti, R. D. (2023). Inventory Analysis using Multi-Criteria ABC and ISM Method – A Case Study in Indonesia’s Aviation MRO. Quantitative Economics and Management Studies, 5(1), 22–31. https://doi.org/10.35877/454ri.qems2166
Ramadhan, D. I., & Santosa, B. (2021). Analisis Kinerja Peramalan dan Klasifikasi Permintaan Auto Parts Berbasis Data Mining. Jurnal Teknik ITS, 9(2). https://doi.org/10.12962/j23373539.v9i2.54168
Ramadiani, Syahrani, R., Astuti, I. F., & Azainil. (2020). Forecasting the number of airplane passengers uses the double and the triple exponential smoothing method. Journal of Physics: Conference Series, 1524(1). https://doi.org/10.1088/1742-6596/1524/1/012051
Reid, R. D., & Sanders, N. R. (2011). Operations Management. In Journal of Chemical Information and Modeling (Vol. 53).
Ridwan Harimansyah, F., & Tukhas Shilul Imaroh. (2020). Aircraft Spare Parts Inventory Management Analysis on Airframe Product Using Continuous Review Methods. Dinasti International Journal of Management Science, 2(1), 81–90. https://doi.org/10.31933/dijms.v2i1.528
?ahin, M., Eldemir, F., & Turkyilmaz, A. (2022a). Inventory Cost Minimization of Spare Parts in Aviation Industry. Transportation Research Procedia, 59, 29–37. https://doi.org/10.1016/j.trpro.2021.11.094
?ahin, M., Eldemir, F., & Turkyilmaz, A. (2022b). ScienceDirect ScienceDirect Inventory of Spare Spare Parts Parts in in Aviation Aviation Industry Industry Inventory Cost Cost Minimization Minimization of. Transportation Research Procedia, 59, 29–37. https://doi.org/10.1016/j.trpro.2021.11.094
Sareminia, S., & Amini, F. (2023). Computers in Industry A reliable and ensemble forecasting model for slow-moving and repairable spare parts?: Data mining approach. Computers in Industry, 145(December 2022), 103827. https://doi.org/10.1016/j.compind.2022.103827
Shejul, K., Harikrishnan, R., & Gupta, H. (2024). The improved integrated Exponential Smoothing based CNN-LSTM algorithm to forecast the day ahead electricity price. MethodsX, 13(April). https://doi.org/10.1016/j.mex.2024.102923
Sondeng, M. U. H. F., Mandagie, K. L., & Tedja, D. A. N. W. (2021). Analisis Permintaan Nose Tire Boeing 737-300 Menggunakan Metode Peramalan Dan Economic Order Quantity (Eoq). Jurnal TeknikIndustri, 10(2), 86–94. https://doi.org/10.35968/jtin/v11i1/789
Svetunkov, I., & Boylan, J. E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265(August), 109013. https://doi.org/10.1016/j.ijpe.2023.109013
Xiao, J., Ma, S., Wang, S., & Huang, G. Q. (2024). Meta-inventory management decisions: A theoretical model. International Journal of Production Economics, 275(July), 109339. https://doi.org/10.1016/j.ijpe.2024.109339
Yang, Y., Ding, C., Lee, S., Yu, L., & Ma, F. (2021). A modified Teunter-Syntetos-Babai method for intermittent demand forecasting. Journal of Management Science and Engineering, 6(1), 53–63. https://doi.org/10.1016/j.jmse.2021.02.008
