Abstract
Demand forecasting is affecting the success of Supply Chain Management (SCM), and the organizations which support them and are in the early stage of a digital transformation. In a near future it could represent the most significant change in the integrated SCM era in today’s complex, dynamic, and uncertain environment. The ability to adequately predict demand by the customers in an SCM is vital to the survival of any business. In this work, we have tried to solve this problem using various demand forecasting models to predict product demand for grocery items with machine learning techniques. A representative set ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As measurement metrics we have used R2 score, Mean Squared Error score and Mean Absolute Error score. Based on ranking, Random Forest classifier gives better performance result on this specific demand forecasting problem compared with the Artificial Neural Network falling behind in the tested category.
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Vairagade, N., Logofatu, D., Leon, F., Muharemi, F. (2019). Demand Forecasting Using Random Forest and Artificial Neural Network for Supply Chain Management. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_27
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DOI: https://doi.org/10.1007/978-3-030-28377-3_27
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