Inventory management contributes to improve supply chain operations, directly impacting on costs, customer satisfaction and, overall, competitiveness, playing a key role to achieve the Sustainable Development Goals (SDGs), by promoting efficient resource use, reducing waste and supporting sustainable economic growth. This study explores the practical application of economic inventory management models, including the Continuous Review (Q-model) and Periodic Review (P-model), in dynamic environments characterized by demand variability and uncertainty. Using real-world data from a high-demand consumable item, the research validates and optimizes these models by integrating predictive analytics to improve decision-making. Results show that the Q-model achieved the lowest total cost of €354 519.50, reducing annual inventory expenses by over €39 000 (7.15%) and decreasing total inventory value by 68.55%, from €118 543 to €37 283. Removing obsolete stock, an additional saving of €750 was achieved. The P-model, while slightly less cost-efficient (€355 132.60, a 6.98% reduction), offered greater simplicity in implementation and alignment with periodic review schedules. Both models ensured a 99.9% service level, balancing cost efficiency and inventory availability. The findings provide actionable insights into selecting inventory policies based on operational context, product characteristics and organizational priorities.