Аннотация: (English) The study focuses on improving inventory management efficiency through accurate demand forecasting. It provides an overview of key statistical methods (ARIMA, exponential smoothing) and modern machine learning techniques (decision trees, neural networks), along with their hybrid versions. The impact of forecast accuracy on critical logistics aspects—inventory levels, safety stock, turnover rates, and shortage risks—is examined. Successful implementation practices in leading companies are analyzed, highlighting the economic benefits achieved. The author's contribution includes recommendations on selecting and adapting models to the specifics of logistics processes, as well as proposals for integrating demand forecasting with other elements of supply chain management.