Application of Artificial Intelligence in Inventory Decision Optimization for Small and Medium Enterprises: An Inventory Management Strategy Based on Predictive Analytics

Small and medium enterprises (SMEs) face significant challenges in inventory management due to limited resources and dynamic market conditions. This research investigates the application of artificial intelligence technologies to optimize inventory decisions for SMEs using predictive analytics. The study develops a comprehensive AI-driven inventory management system that integrates machine learning algorithms with traditional inventory control theories. Through experimental validation using real-world SME data, the proposed framework demonstrates substantial improvements in inventory turnover rates and cost reduction. The research proposes a novel predictive analytics architecture specifically designed for resource-constrained environments, addressing key limitations of existing inventory management systems. Results indicate that AI-enabled inventory strategies can enhance operational efficiency by 34% while reducing inventory holding costs by 28%. The findings provide practical insights for SME decision-makers seeking to implement AI technologies in their inventory management processes. This research advances the understanding of AI applications in supply chain optimization for small business environments.