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Omnia M Nabil, Nahid H. Afia, Tengku Hanidza Tengku Ismail
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Intermittent demand forecasting for spare parts: a critical review
2021
Spare parts inventory management: a literature review
10.3390/su13052460 · 2021
Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand
10.3390/su14159295 · 2022
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We need to talk about intermittent demand forecasting
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10.1016/j.ijforecast.2004.10.001 · 2005
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Enhancing intermittent spare part demand forecasting: a novel ensemble approach with focal loss and SMOTE
10.3390/logistics9010025 · 2025
Robust interval prediction of intermittent demand for spare parts based on tensor optimization
10.3390/s23167182 · 2023
Machine learning algorithms in intermittent demand forecasting: a review
10.1080/00207543.2025.2578701 · 2025
Optimization models for critical spare parts inventories-a reliability approach
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Spare parts demand forecasting in maintenance, repair & overhaul
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“Intermittent demand forecasting for spare parts using artificial neural networks and deep learning: literature review,” (in en)
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RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting
10.1007/s11042-021-10913-0 · 2021
Spare parts forecasting and lumpiness classification using neural network model and its impact on aviation safety
10.3390/app13095475 · 2023
Machine-learning algorithm for demand forecasting problem
10.1088/1742-6596/1918/4/042012 · 2021
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A sporadic classification and regression-based approach to intermittent demand forecasting in smart supply chain
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Another look at measures of forecast accuracy
10.1016/j.ijforecast.2006.03.001 · 2006
Analysis of spare parts inventory control using the min-max stock method and continuous review
10.37899/journallamultiapp.v6i3.2011 · 2025
Analysis of spare parts inventory control using the min-max stock method and continuous review
10.37899/journallamultiapp.v6i3.2011 · doi-reference
Another look at measures of forecast accuracy
10.1016/j.ijforecast.2006.03.001 · doi-reference
10.1109/tgrs.2025.3553751
10.1109/tgrs.2025.3553751 · doi-reference
A brief review on multi-task learning
10.1007/s11042-018-6463-x · doi-reference
Multitask learning
10.1023/a:1007379606734 · doi-reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · doi-reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203450 · doi-reference
Random forests
10.1023/a:1010933404324 · doi-reference
Machine-learning algorithm for demand forecasting problem
10.1088/1742-6596/1918/4/042012 · doi-reference
Spare parts forecasting and lumpiness classification using neural network model and its impact on aviation safety
10.3390/app13095475 · doi-reference
RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting
10.1007/s11042-021-10913-0 · doi-reference
10.1109/confluence51648.2021.9376880
10.1109/confluence51648.2021.9376880 · doi-reference
Demand forecasting intermittent and lumpy time series: comparing statistical, machine learning and deep learning methods
10.24251/hicss.2021.172 · doi-reference
Optimization models for critical spare parts inventories-a reliability approach
10.1057/jors.2010.49 · doi-reference
Machine learning algorithms in intermittent demand forecasting: a review
10.1080/00207543.2025.2578701 · doi-reference
Robust interval prediction of intermittent demand for spare parts based on tensor optimization
10.3390/s23167182 · doi-reference
Enhancing intermittent spare part demand forecasting: a novel ensemble approach with focal loss and SMOTE
10.3390/logistics9010025 · doi-reference
Intermittent demand: linking forecasting to inventory obsolescence
10.1016/j.ejor.2011.05.018 · doi-reference
The accuracy of intermittent demand estimates
10.1016/j.ijforecast.2004.10.001 · doi-reference
Forecasting and stock control for intermittent demands
10.1057/jors.1972.50 · doi-reference
Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand
10.3390/su14159295 · doi-reference
Spare parts inventory management: a literature review
10.3390/su13052460 · doi-reference
We need to talk about intermittent demand forecasting
10.1016/j.ejor.2019.12.046 · doi-reference