APPLICATION OF DATA SCIENCE METHODS FOR DEMAND FORECASTING IN RETAIL

Abstract

This scientific article examines the problem of forecasting demand in retail using data science methods. It is explained that traditional methods of demand forecasting do not give an excellent result, as machine learning, statistical models and data analysis become powerful tools, they need improvement, therefore this research is necessary and appropriate. The importance of accurate demand forecasting for effective inventory management, cost reduction, and customer service improvement is analyzed. The main methods of data science are considered, such as: machine learning, statistical models and data analysis. Real examples of the use of these methods in retail companies and their impact on increasing the accuracy of demand forecasting are also presented and clearly characterized for each company. Key steps in the forecasting process are described, including data collection and preparation, model selection, training, and performance evaluation. Examples of the use of machine learning algorithms, such as linear regression, decision trees, and neural networks, for demand forecasting in the retail sector are provided, and a comparison of these approaches is highlighted. The proposed price optimization procedure. This article shows that forecasting and analytics are integral to the effectiveness and competitiveness and flexibility of retailers in the market, and that the results of this study can be widely applied to further study the application of these methods, as well as to identify new methods. According to the scientific article, a conclusion was made that this research should be continued, and it will contribute to the effective functioning of retail companies and improve their competitiveness on the market. Recent achievements and prospects of using data science in demand forecasting are discussed.