2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 12 , Issue 1 , PP: 24-37, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Esraa Kamal 1 * , Amal F. Abdel-Gawad 2 , Basem Ibraheem 3 , Shereen Zaki 4
Doi: https://doi.org/10.54216/FPA.120102
Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 75/25.
Demand Forecasting , Machine Learning , Multiple Linear Regression , Support Vector Machine , K-nearest Neighbors , Random Forest , Extreme Gradient Boosting.
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