AI-Driven Inventory Management for SMEs: Integrating Demand Dynamics, Sustainability, and Operational Efficiency

Effective inventory management is crucial for small and medium-sized enterprises (SMEs) to enhance efficiency, reduce costs, and adopt sustainable practices. This study introduces an inventory model that integrates demand dynamics, preservation technology, and sustainability measures, addressing price, time, and advertisement frequency to optimize strategies. Carbon emission regulations are incorporated to promote eco-friendly operations. AI-driven demand forecasting enables precise predictions by analyzing historical sales data, seasonal trends, and external market factors, while ML-based optimization dynamically adjusts inventory policies to minimize costs and improve supply chain performance. Automated inventory tracking using barcode and RFID technologies ensures real-time visibility and accuracy, reducing errors and streamlining warehouse operations. RFID, in particular, provides non-line-of-sight tracking, enhancing inventory monitoring capabilities. These technologies not only improve operational efficiency but also generate actionable insights for strategic decision-making. The theoretical properties of the model are validated through numerical examples and sensitivity analyses. The findings underscore the importance of integrating modern technologies and sustainability measures in inventory management, demonstrating that SMEs can reduce operational costs, enhance efficiency, and build resilient, eco-friendly supply chains. This research showcases how leveraging advanced inventory models enables SMEs to remain competitive, financially viable, and environmentally responsible.