Nabil, O., Afia, N., Ismail, T. (2025). Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review. JES. Journal of Engineering Sciences, 53(6), 187-210. doi: 10.21608/jesaun.2025.386170.1518
Omnia M Nabil; Nahid H Afia; T Ismail. "Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review". JES. Journal of Engineering Sciences, 53, 6, 2025, 187-210. doi: 10.21608/jesaun.2025.386170.1518
Nabil, O., Afia, N., Ismail, T. (2025). 'Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review', JES. Journal of Engineering Sciences, 53(6), pp. 187-210. doi: 10.21608/jesaun.2025.386170.1518
Nabil, O., Afia, N., Ismail, T. Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review. JES. Journal of Engineering Sciences, 2025; 53(6): 187-210. doi: 10.21608/jesaun.2025.386170.1518
Intermittent Demand Forecasting for Spare Parts Using Artificial Neural Networks and Deep Learning: Literature Review
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.
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