Development and implementation of a predictive analytical model for optimizing inventory management in the B2C sector in highly competitive online markets

In the context of rapid growth in sales volumes and intensifying competition on global B2C marketplaces, effective inventory management plays a decisive role in ensuring profitability and long-term business sustainability. This study proposes a conceptual predictive analytical model aimed at optimizing stock management in companies operating on highly competitive online platforms such as Amazon. The objective is to develop a hybrid demand-forecasting system that combines classical time-series methods (SARIMA) with modern gradient boosting algorithms in order to ensure adaptability to rapidly changing market conditions, account for seasonal fluctuations, long-term trends, and the influence of external factors. The methodological basis comprises a critical review and synthesis of key publications from recent years, as well as the use of proprietary data on the company Skysales Ltd. to demonstrate the effectiveness of the approach. The results obtained indicate an increase in the accuracy of consumer demand forecasts, which leads to a reduction in excess inventory, a decrease in lost sales, and accelerated capital turnover. The scientific novelty of the work lies in the formation of a hybrid model architecture specifically adapted to the needs of small and medium-sized enterprises in the B2C e-commerce sector with a broad and dynamically updated assortment. This article will be useful both to academic researchers in the field of supply chain management and data analysts, and to practitioners—executives and e-commerce managers—seeking to improve the operational efficiency of their enterprises.