Small and medium-sized enterprises (SMEs) face significant challenges in digitalizing decision-making processes, particularly in demand forecasting, replenishment, and production planning. This study investigates the application of integrated methods in replenishment planning. A set of key hybrid approaches employed in replenishment planning, including deep reinforcement learning, hybrid machine learning forecasting models, and optimization model have been identified. The findings indicate that integrating data-driven models with knowledge-based systems enhances decision-making, optimizes inventory levels, and mitigates procurement risks. Despite the benefits of hybrid approaches, further research is needed to expand integrated replenishment models, particularly for industries with complex supply chains. Future developments should focus on combining rule-based models with AI-driven predictive analytics to improve replenishment efficiency and adaptability in dynamic markets.