Open Access
| Issue |
MATEC Web Conf.
Volume 415, 2025
International Colloquium on Mechanical and Civil Engineering (ICMCE’2025)
|
|
|---|---|---|
| Article Number | 03002 | |
| Number of page(s) | 10 | |
| Section | Artificial Intelligence and Optimization | |
| DOI | https://doi.org/10.1051/matecconf/202541503002 | |
| Published online | 27 October 2025 | |
- Abbasimehr, H., Shabani, M., Yousefi, M.: An optimized model using lstm network for demand forecasting. Computers & industrial engineering 143, 106435 (2020) [Google Scholar]
- Abbate, R., Manco, P., Caterino, M., Fera, M., Macchiaroli, R.: Demand forecasting for delivery platforms by using neural network. IFAC-PapersOnLine 55 (10), 607–612 (2022) [Google Scholar]
- Ahamed, S.F., Vijayasankar, A., Thenmozhi, M., Rajendar, S., Bindu, P., Rao, T.S.M.: Machine learning models for forecasting and estimation of business operations. The Journal of High Technology Management Research 34 (1), 100455 (2023) [Google Scholar]
- Alon, I., Qi, M., Sadowski, R.J.: Forecasting aggregate retail sales:: a comparison of artificial neural networks and traditional methods. Journal of retailing and consumer services 8 (3), 147–156 (2001) [Google Scholar]
- Chandriah, K.K., Naraganahalli, R.V.: Rnn/lstm with modified adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools and Applications 80 (17), 26145–26159 (2021) [CrossRef] [Google Scholar]
- Elalem, Y.K., Maier, S., Seifert, R.W.: A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks. International Journal of Forecasting 39 (4), 1874–1894 (2023) [Google Scholar]
- Falatouri, T., Darbanian, F., Brandtner, P., Udokwu, C.: Predictive analytics for demand forecasting–a comparison of sarima and lstm in retail scm. Procedia Computer Science 200, 993–1003 (2022) [Google Scholar]
- Gonçalves, J.N., Cortez, P., Carvalho, M.S., Frazão, N.M.: A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decision Support Systems 142, 113452 (2021) [Google Scholar]
- Güven, İ., Şimşir, F.: Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ann) and support vector machines (svm) methods. Computers & Industrial Engineering 147, 106678 (2020) [Google Scholar]
- Haoudi, Y., Yazdani, M.A., Roy, D., Hennequin, S.: Demand prediction based on machine learning algorithms for optimal distribution of insulin. IFAC-PapersOnLine 56 (2), 10174–10179 (2023) [Google Scholar]
- Hasan, N., Ahmed, N., Ali, S.M.: Improving sporadic demand forecasting using a modified k-nearest neighbor framework. Engineering Applications of Artificial Intelligence 129, 107633 (2024) [Google Scholar]
- Hatcher, W.G., Yu, W.: A survey of deep learning: Platforms, applications and emerging research trends. IEEE access 6, 24411–24432 (2018) [Google Scholar]
- He, Q.Q., Wu, C., Si, Y.W.: Lstm with particle swam optimization for sales fore-casting. Electronic Commerce Research and Applications 51, 101118 (2022) [Google Scholar]
- Joseph, R.V., Mohanty, A., Tyagi, S., Mishra, S., Satapathy, S.K., Mohanty, S.N.: A hybrid deep learning framework with cnn and bi-directional lstm for store item demand forecasting. Computers and Electrical Engineering 103, 108358 (2022) [Google Scholar]
- Kantasa-Ard, A., Nouiri, M., Bekrar, A., Ait el Cadi, A., Sallez, Y.: Machine learning for demand forecasting in the physical internet: a case study of agricultural products in thailand. International Journal of Production Research 59 (24), 7491–7515 (2021) [Google Scholar]
- Lazzeri, F.: Machine learning for time series forecasting with Python. John Wiley & Sons (2020) [Google Scholar]
- Mbonyinshuti, F., Nkurunziza, J., Niyobuhungiro, J., Kayitare, E.: The prediction of essential medicines demand: a machine learning approach using consumption data in rwanda. Processes 10 (1), 26 (2021) [Google Scholar]
- Moon, M.A., Mentzer, J.T., Smith, C.D.: Conducting a sales forecasting audit. International Journal of Forecasting 19 (1), 5–25 (2003) [Google Scholar]
- Nasseri, M., Falatouri, T., Brandtner, P., Darbanian, F.: Applying machine learning in retail demand prediction—a comparison of tree-based ensembles and long short-term memory-based deep learning. Applied Sciences 13 (19), 11112 (2023) [Google Scholar]
- Oliva, R., Watson, N.: Cross-functional alignment in supply chain planning: A case study of sales and operations planning. Journal of operations management 29 (5), 434–448 (2011) [Google Scholar]
- Pacella, M., Papadia, G.: Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management. Procedia CIRP 99, 604–609 (2021) [Google Scholar]
- Punia, S., Shankar, S.: Predictive analytics for demand forecasting: A deep learning-based decision support system. Knowledge-Based Systems 258, 109956 (2022) [CrossRef] [Google Scholar]
- Sarker, I.H.: Machine learning: Algorithms, real-world applications and research directions. SN computer science 2 (3), 160 (2021) [CrossRef] [PubMed] [Google Scholar]
- Taghiyeh, S., Lengacher, D.C., Sadeghi, A.H., Sahebi-Fakhrabad, A., Handfield, R.B.: A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management. Supply Chain Analytics 3, 100032 (2023) [Google Scholar]
- Tian, X., Wang, H., et al.: Forecasting intermittent demand for inventory management by retailers: A new approach. Journal of Retailing and Consumer Services 62, 102662 (2021) [Google Scholar]
- Türkmen, A.C., Januschowski, T., Wang, Y., Cemgil, A.T.: Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes. Plos one 16 (11), e0259764 (2021) [Google Scholar]
- Vallés-Pérez, I., Soria-Olivas, E., Martínez-Sober, M., Serrano-López, A.J., Gómez-Sanchís, J., Mateo, F.: Approaching sales forecasting using recurrent neural networks and transformers. Expert Systems with Applications 201, 116993 (2022) [Google Scholar]
- Weng, T., Liu, W., Xiao, J.: Supply chain sales forecasting based on lightgbm and lstm combination model. Industrial Management & Data Systems 120 (2), 265–279 (2020) [Google Scholar]
- Zhuang, X., Yu, Y., Chen, A.: A combined forecasting method for intermittent demand using the automotive aftermarket data. Data Science and Management 5 (2), 43–56 (2022) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

