Big Data Collection and Analysis in Warehouse Management
Abstract
This article explores the use of big data technologies to enhance warehouse management efficiency. The primary focus is on analyzing data collection, processing, and visualization methods and their role in process automation and decision-making. Methods of big data collection, machine learning algorithms for demand forecasting and inventory optimization are presented. The research findings indicate that the application of analytical technologies contributes to cost reduction, improved inventory accuracy, and faster order processing. Special attention is given to demand modeling based on historical data and external factors, such as weather conditions and market trends. The article discusses the advantages and limitations of various approaches, including ARIMA, SARIMA, XGBoost, and neural networks, for inventory management. Thus, the study highlights the importance of integrating big data into warehouse logistics to achieve high performance and adaptability.