ARTIFICIAL INTELLIGENCE-BASED INVENTORY MANAGEMENT FOR TECHNOLOGY COMPANIES USING VIDEO GAME SALES DATA.

Arnawtee Mohammed MahdeMahmood, Zaiter Murooj FadhilZaiter

Effective inventory management within organizational systems requires reliable support for decision-making processes, especially under conditions of unstable demand and rapidly changing market environments. This paper proposes a methodological framework for a decision support system (DSS) for inventory management based on the integration of artificial intelligence models. The study focuses on the application of modern machine learning and deep learning methods for demand forecasting and inventory optimization in technology companies. Particular attention is paid to the formalization of feature selection procedures, development of predictive models, and their integration into a real-time operational DSS. The results of empirical tests using LightGBM, CatBoost, and XGBoost models demonstrate high accuracy (R² up to 0.98) and computational efficiency in solving the problem of intelligent inventory management.

Keywords:decision support system, inventory management, organizational systems, machine learning, demand forecasting, artificial intelligence.