Abstract
Research in operations has demonstrated the significant role that supply chain management plays in the sustainability of many firms, particularly in the disruptive period we live in today. The accuracy of demand forecasting is a crucial component of supply chain management effectiveness. Therefore, it is necessary to create trustworthy demand forecasting models in order to provide predictions that are better and more accurate. One disruptive tool that has promise for improving demand forecasting models over those now employed in supply chain management is machine learning. This paper’s major goal is to provide a better understanding of how demand volatility affects forecasting models’ accuracy and how it might be modeled in the supply chain. A model using machine learning is deployed to predict customers demand using past orders data. Results shows that the best model is the one elaborated using linear regression with a MAE score of 0.00057 and a RMSE score of 0.0014.
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References
Huber, J., Stuckenschmidt, H.: Daily retail demand forecasting using machine learning with emphasis on calendric special days. Int. J. Forecast. 36(4), 1420–1438 (2020)
Tanizaki, T., Hoshino, T., Shimmura, T., Takenaka, T.: Demand forecasting in restaurants using machine learning and statistical analysis. Procedia CIRP 79, 679–683 (2019)
Ahmad, T., Chen, H.: Utility companies strategy for short-term energy demand forecasting using machine learning based models. Sustain. Cities Soc. 39, 401–417 (2018)
Aamer, A., Eka Yani, L., Alan Priyatna, I.: Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Oper. Supply Chain. Manag. Int. J. 14(1), 1–13 (2020)
Spiliotis, E., Makridakis, S., Semenoglou, A.A., Assimakopoulos, V.: Comparison of statistical and machine learning methods for daily SKU demand forecasting. Oper. Res. 1–25 (2020)
Khan, M.A., Saqib, S., Alyas, T., Rehman, A.U., Saeed, Y., Zeb, A., Mohamed, E.M.: Effective demand forecasting model using business intelligence empowered with machine learning. IEEE Access 8, 116013–116023 (2020)
Moroff, N.U., Kurt, E., Kamphues, J.: Machine Learning and statistics: a Study for assessing innovative demand forecasting models. Procedia Comput. Sci. 180, 40–49 (2021)
Maulud, D., Abdulazeez, A.M.: A review on linear regression comprehensive in machine learning. J. Appl. Sci. Technol. Trends 1(4), 140–147 (2020)
Xing, R., Fu, J., Shao, Y., You, J.: Rigid Regression for facial image interpolation with local structure prior. In: 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 67–70. Hangzhou, China (2014). https://doi.org/10.1109/IHMSC.2014.119
Shariff, N.S.M., Duzan, H.: An Application of Proposed Ridge Regression Methods to Real Data Problem. Int. J. Eng. Technol. 7, 106 (2018). https://doi.org/10.14419/ijet.v7i4.30.22061
Ranstam, J., Cook, J.A.: LASSO regression. J. Br. Surg. 105(10), 1348–1348 (2018)
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Abouloifa, H., Bahaj, M. (2024). Artificial Intelligence in Supply Chain 4.0: Using Machine Learning in Demand Forecasting. In: Gherabi, N., Awad, A.I., Nayyar, A., Bahaj, M. (eds) Advances in Intelligent System and Smart Technologies. I2ST 2023. Lecture Notes in Networks and Systems, vol 826. Springer, Cham. https://doi.org/10.1007/978-3-031-47672-3_14
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DOI: https://doi.org/10.1007/978-3-031-47672-3_14
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