Abstract
Efficient inventory management is pivotal for manufacturing companies striving to maintain operational excellence, minimize costs, and meet dynamic market demands. Traditional inventory management systems, while functional, often fall short in addressing complex supply chain challenges such as real-time visibility, demand variability, and integration with modern technologies. This study explores the development of an integrated inventory management model designed to optimize inventory control, enhance decision-making, and reduce operational inefficiencies. By leveraging advanced technologies, including IoT, AI, and ERP systems, the proposed model integrates demand forecasting, inventory optimization, and supplier collaboration into a cohesive framework. Data-driven insights and predictive analytics form the cornerstone of this approach, enabling manufacturing companies to adapt to shifting market conditions seamlessly. The research evaluates the model's performance through simulations and case studies, highlighting significant improvements in inventory turnover, cost reduction, and operational agility. This study provides a pathway for manufacturing companies to transition to scalable, efficient, and technologically advanced inventory management systems.
Published Version
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