Abstract
Effective and efficient supply chain management is one of the primary factors behind the success of modern-day organizations. The necessity to circumvent the impediments between supply and demand in any organization brings in the need for machine learning techniques. The performance of several machine learning methods, namely, random forest, gradient boosting, and XGBoost have been compared for demand forecasting. Weekly sales data of a multinational retail chain used consists of various attributes affecting the sales, for example, consumer price index and store size in the region. The data represents the sales made in 45 stores over 3 years across the United States of America. The comparison between the methods mentioned to find the most optimal forecasting method among them has been done through various performance metrics, namely, MAE, MSE, and R2 scores. The XGBoost model outperforms random forest and gradient boosting models producing the most accurate predictions.
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Mitra, A., Jain, A., Kishore, A., Kumar, P. (2023). A Comparative Study for Machine Learning Models in Retail Demand Forecasting. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_23
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DOI: https://doi.org/10.1007/978-981-19-5403-0_23
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