Optimization of SME Inventory Systems through the Application of AI-Based Predictive Technology

This study explores the potential of AI-based predictive technologies for optimizing inventory management in Small and Medium Enterprises (SMEs). By employing a qualitative research method with a literature review approach, this study synthesizes existing academic and industry research to examine how AI can improve demand forecasting accuracy, optimize inventory levels, and enable dynamic, real-time inventory adjustments. The findings indicate that AI-powered predictive models, such as machine learning algorithms, can significantly enhance the efficiency and profitability of SMEs by reducing overstocking and stockouts, improving cash flow, and enhancing customer satisfaction. Despite these advantages, the adoption of AI in SMEs is hindered by challenges such as financial constraints, a lack of technical expertise, and limited access to high-quality data. However, the increasing availability of cost-effective AI solutions, particularly software-as-a-service (SaaS) platforms, presents a promising solution for SMEs to overcome these barriers. Additionally, the integration of AI with other emerging technologies like the Internet of Things (IoT) and blockchain further strengthens the capabilities of inventory management systems. The study concludes that while AI has transformative potential, addressing the barriers to its adoption is essential for SMEs to fully capitalize on these technologies. Future research should focus on developing more accessible AI tools for SMEs, exploring the sustainability impacts of AI, and examining the integration of AI with other technologies to create resilient supply chains.