Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review

Nabil, Omnia M; Afia, Nahid H; Ismail, T

doi:10.21608/jesaun.2025.386170.1518

	Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review
JES. Journal of Engineering Sciences
Article 6, Volume 53, Issue 6, November and December 2025, Page 187-210 PDF (1.05 MB)
Document Type: Review Paper
DOI: 10.21608/jesaun.2025.386170.1518
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Authors
Omnia M Nabil ¹; Nahid H Afia²; T Ismail³
¹Design and Production Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt
²Design and Production Engineering Department, Ain Shams University, Cairo, Egypt
³Basic and Applied Sciences Department, Arab Academy for Science, Technology and Maritime Transport
Abstract
Forecasting Intermittent demand for spare parts is essential for enhancing inventory management, particularly in industries where unplanned equipment downtime and inventory holding costs are significant. Conventional forecasting methods often underperform in handling the sporadic and nonlinear nature of intermittent demand. This paper presents a focused literature review on the use of Artificial Neural Networks (ANNs) and Deep Learning (DL) techniques for forecasting intermittent demand. Unlike general reviews that survey all forecasting approaches, this study concentrates specifically on neural and learning approaches to capture nonlinear patterns. The findings demonstrate that ANN and DL-based models generally outperform classical methods in forecasting accuracy, especially under highly irregular demand. Despite the advances, the availability and quality of datasets remain a significant limitation in developing robust models. Future research directions are identified, including the need for improved feature engineering, architecture optimization, and model interpretability. This review aims to support researchers understanding the potential and challenges of neural approaches for forecasting of intermittent demand for spare parts.
Keywords
Intermittent demand; Spare parts; Forecasting; Artificial Neural Networks; Deep Learning


References
[1] M. Tusar and B. Sarker, "Spare parts control strategies for offshore wind farms: A critical review and comparative study," WIND ENGINEERING, vol. 46, pp. 1629-1656, 2022-05-03 2022, doi: 10.1177/0309524X221095258. [2] T. Lucht, V. Alieksieiev, T. Kämpfer, and P. Nyhuis, "Spare Parts Demand Forecasting in Maintenance, Repair & Overhaul," in Proceedings of the Conference on Production Systems and Logistics, 2022, pp. 525-534, doi: 10.15488/12179. [3] N. Kontrec and S. Panic, "Spare Parts Forecasting Based on Reliability," 2017. [4] A. A. Syntetos, J. E. Boylan, and J. D. Croston, "On the categorization of demand patterns," Journal of the Operational Research Society, vol. 56, no. 5, pp. 495-503, May 2005, doi: 10.1057/palgrave.jors.2601841. [5] Ç. Pinçe, L. Turrini, and J. Meissner, "Intermittent demand forecasting for spare parts: A Critical review," Omega-International Journal of Management Science, vol. 105, Dec 2021, Art no. 102513, doi: 10.1016/j.omega.2021.102513. [6] S. Zhang, K. Huang, and Y. Yuan, "Spare Parts Inventory Management: A Literature Review," Sustainability, vol. 13, p. 2460, 02/25 2021, doi: 10.3390/su13052460. [7] P. Jiang, Y. B. Huang, and X. Liu, "Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model," International Journal of Production Research, vol. 59, no. 24, pp. 7423-7440, Dec 2021, doi: 10.1080/00207543.2020.1842936. [8] S. F. Faghidian, M. Khashei, and M. Khalilzadeh, "Improving intermittent demand forecasting based on data structure," Journal of Engineering Research, vol. 9, no. 1, Mar 2021, doi: 10.36909/jer.v9i1.8667. [9] M. Negmeldin, M. Heshmat, and A. Eltawil, "A SYSTEM DYNAMICS APPROACH FOR STRATEGIC PLANNING OF CONSUMER ELECTRONICS INDUSTRY IN DEVELOPING COUNTRIES: THE CASE OF THE TELEVISION MANUFACTURING INDUSTRY IN EGYPT," SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING, vol. 32, no. 2, pp. 133-149, 2021-08-01 2021, doi: 10.7166/32-2-2468. [10] G. Q. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," International Journal of Forecasting, vol. 14, no. 1, pp. 35-62, Mar 1998, doi: 10.1016/s0169-2070(97)00044-7. [11] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," Bulletin of Mathematical Biology, vol. 52, no. 1, pp. 99-115, 1990/01/01/ 1990, doi: 10.1016/S0092-8240(05)80006-0. [12] G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science (Washington D C), vol. 313, no. 5786, pp. 504-507, Jul 28 2006, doi: 10.1126/science.1127647. [13] B. A. D. Menezes, D. D. Braga, B. Hellingrath, and F. B. D. Neto, "An Evaluation of Forecasting Methods for Anticipating Spare Parts Demand," 2015 Latin America Congress on Computational Intelligence (La-Cci), 2015. [14] N. Kourentzes, "Intermittent demand forecasts with neural networks," International Journal of Production Economics, vol. 143, no. 1, pp. 198-206, May 2013, doi: 10.1016/j.ijpe.2013.01.009. [15] C. Xiang, S. Q. Ding, and T. H. Lee, "Geometrical interpretation and architecture selection of MLP," Ieee Transactions on Neural Networks, vol. 16, no. 1, pp. 84-96, Jan 2005, doi: 10.1109/tnn.2004.836197. [16] R. S. Gutierrez, A. O. Solis, and S. Mukhopadhyay, "Lumpy demand forecasting using neural networks," International Journal of Production Economics, vol. 111, no. 2, pp. 409-420, Feb 2008, doi: 10.1016/j.ijpe.2007.01.007. [17] F. Lolli, R. Gamberini, A. Regattieri, E. Balugani, T. Gatos, and S. Gucci, "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, vol. 183, pp. 116-128, Jan 2017, doi: 10.1016/j.ijpe.2016.10.021. [18] D. J. C. MacKay, "Bayesian Interpolation," Neural Computation, vol. 4, pp. 415-447, 1992. [19] M. Z. Babai, A. Tsadiras, and C. Papadopoulos, "On the empirical performance of some new neural network methods for forecasting intermittent demand," Ima Journal of Management Mathematics, vol. 31, no. 3, pp. 281-305, Jul 2020, doi: 10.1093/imaman/dpaa003. [20] A. R. S. Parmezan, V. M. A. Souza, and G. Batista, "Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model," Information Sciences, vol. 484, pp. 302-337, May 2019, doi: 10.1016/j.ins.2019.01.076. [21] D. Kiefer, F. Grimm, M. Bauer, and C. van Dinther, Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods. 2021. [22] S. Mukhopadhyay, A. O. Solis, and R. S. Gutierrez, "The Accuracy of Non-traditional versus Traditional Methods of Forecasting Lumpy Demand," Journal of Forecasting, vol. 31, no. 8, pp. 721-735, Dec 2012, doi: 10.1002/for.1242. [23] M. R. Amin-Naseri, B. R. Tabar, and Ieee, "Neural network approach to lumpy demand forecasting for spare parts in process industries," in International Conference on Computer and Communication Engineering, Kuala Lumpur, MALAYSIA, May 13-15 2008, 2008, pp. 1378-1382, doi: 10.1109/iccce.2008.4580831. [Online]. Available: ://WOS:000259601400276 [24] M. Rosienkiewicz, "Artificial Intelligence Methods in Spare Parts Demand Forecasting," Logistics and Transport, vol. 2013, 01/01 2013. [25] M. Şahin, R. Kizilaslan, and Ö. Demirel, "Forecasting Aviation Spare Parts Demand Using Croston Based Methods and Artificial Neural Networks," vol. 15, pp. 1-21, 01/01 2013. [26] K. N. Amirkolaii, A. Baboli, M. K. Shahzad, and R. Tonadre, "Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI)," Ifac Papersonline, vol. 50, no. 1, pp. 15221-15226, 2017, doi: 10.1016/j.ifacol.2017.08.2371. [27] M. Rosienkiewicz, "Accuracy Assessment of Artificial Intelligence-Based Hybrid Models for Spare Parts Demand Forecasting in Mining Industry," Information Systems Architecture and Technology, Isat 2019, Pt Iii, vol. 1052, pp. 176-187, 2020, doi: 10.1007/978-3-030-30443-0_16. [28] H. Abbasimehr, M. Shabani, and M. Yousefi, "An optimized model using LSTM network for demand forecasting," Computers & Industrial Engineering, vol. 143, May 2020, Art no. 106435, doi: 10.1016/j.cie.2020.106435. [29] A. Muhaimin, D. D. Prastyo, H. H. S. Lu, and Ieee, "Forecasting with Recurrent Neural Network in Intermittent Demand Data," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence 2021), pp. 802-809, 2021, doi: 10.1109/Confluence51648.2021.9376880. [30] M. Pacella and G. Papadia, "Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management," Procedia CIRP, vol. 99, pp. 604-609, 2021/01/01/ 2021, doi: 10.1016/j.procir.2021.03.081. [31] D. T. Wiyanti, I. Kharisudin, A. B. Setiawan, and A. K. Nugroho, "Machine-learning algorithm for demand forecasting problem," Journal of Physics: Conference Series, vol. 1918, no. 4, p. 042012, 2021/06/01 2021, doi: 10.1088/1742-6596/1918/4/042012. [32] K. K. Chandriah and R. V. Naraganahalli, "RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting," Multimedia Tools and Applications, vol. 80, no. 17, pp. 26145-26159, Jul 2021, doi: 10.1007/s11042-021-10913-0. [33] R. Carbonneau, R. Vahidov, and K. Laframboise, "Machine Learning-Based Demand Forecasting in Supply Chains," International Journal of Intelligent Information Technologies, vol. 3, no. 4, pp. 40-57, Oct-Dec 2007, doi: 10.4018/jiit.2007100103. [34] M. Hoffmann, R. Lasch, and J. Meinig, "Forecasting Irregular Demand Using Single Hidden Layer Neural Networks," Logistics Research, vol. 15, 07/15 2022, doi: 10.23773/2022_6. [35] S. Punia and S. Shankar, "Predictive analytics for demand forecasting: A deep learning-based decision support system," Knowledge-Based Systems, vol. 258, p. 109956, 2022/12/22/ 2022, doi: 10.1016/j.knosys.2022.109956. [36] R. V. Joseph, A. Mohanty, S. Tyagi, S. Mishra, S. K. Satapathy, and S. N. Mohanty, "A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting," Computers & Electrical Engineering, vol. 103, Oct 2022, Art no. 108358, doi: 10.1016/j.compeleceng.2022.108358. [37] I. Shafi et al., "Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety," Applied Sciences-Basel, vol. 13, no. 9, Apr 2023, Art no. 5475, doi: 10.3390/app13095475. [38] J. D. Kim, T. H. Kim, and S. W. Han, "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks," Mathematics, vol. 11, no. 3, Feb 2023, Art no. 501, doi: 10.3390/math11030501. [39] L. L. Fan, X. Liu, W. T. Mao, K. Yang, and Z. Y. Song, "Spare Parts Demand Forecasting Method Based on Intermittent Feature Adaptation," Entropy, vol. 25, no. 5, May 2023, Art no. 764, doi: 10.3390/e25050764. [40] M. Ifraz, A. Aktepe, S. Ersöz, and T. Çetinyokus, "Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet," Journal of Engineering Research, vol. 11, no. 2, Jun 2023, Art no. 100057, doi: 10.1016/j.jer.2023.100057. [41] G. Zhang, Y. Xia, and M. Xie, "Intermittent demand forecasting with transformer neural networks," ANNALS OF OPERATIONS RESEARCH, vol. 339, no. 1-2, pp. 1051-1072, 2023-06-23 2024, doi: 10.1007/s10479-023-05447-7. [42] B. Choi and J. Suh, "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea," Sustainability, vol. 12, p. 6045, 07/28 2020, doi: 10.3390/su12156045. [43] M. Ifraz, S. Ersöz, A. Aktepe, and T. Çetinyokus, "Sequential predictive maintenance and spare parts management with data mining methods: a case study in bus fleet," JOURNAL OF SUPERCOMPUTING, vol. 80, no. 15, pp. 22099-22123, 2024-06-20 2024, doi: 10.1007/s11227-024-06297-1. [44] N. J. van Eck and L. Waltman, "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, vol. 84, no. 2, pp. 523-538, 2010/08/01 2010, doi: 10.1007/s11192-009-0146-3. [45] F. Lolli, R. Gamberini, A. Regattieri, E. Balugani, T. Gatos, and S. Gucci, "Single-hidden layer neural networks for forecasting intermittent demand," Int. J. of Prod. Econ., vol. 183, pp. 116-128, Jan 2017, doi: 10.1016/j.ijpe.2016.10.021. [46] R. S. Gutierrez, A. O. Solis, and S. Mukhopadhyay, "Lumpy demand forecasting using neural networks," Int. J. of Prod. Econ., vol. 111, no. 2, pp. 409-420, Feb 2008, doi: 10.1016/j.ijpe.2007.01.007. [47] R. J. Hyndman and A. B. Koehler, "Another look at measures of forecast accuracy," International Journal of Forecasting, vol. 22, no. 4, pp. 679-688, 2006, doi: 10.1016/j.ijforecast.2006.03.001. [48] A. A. Syntetos and J. E. Boylan, "The accuracy of intermittent demand estimates," International Journal of Forecasting, vol. 21, no. 2, pp. 303-314, Apr-Jun 2005, doi: 10.1016/j.ijforecast.2004.10.001. [49] S. Kim and H. Kim, "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, vol. 32, no. 3, pp. 669-679, 2016/07/01/ 2016, doi: 10.1016/j.ijforecast.2015.12.003. [50] M. A. El-Sharief, O. Salah, and M. Heshmat, "ANFIS and regression-based ANOVA for attribute and variable prediction: a case of quality characteristics in the cement bags industry," in International Journal of Industrial and Systems Engineering, 2023, vol. 44, 3 ed., pp. 336-350, doi: 10.1504/IJISE.2023.132283.
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