A Framework for an IoT-enabled Intelligent Inventory Management System

The rapid advancement of Internet of Things (IoT) technology has opened new frontiers in inventory management by enabling real-time, data-driven decision-making. This paper presents a comprehensive framework for an IoT-enabled intelligent inventory management system (IIMS) that integrates weight sensors, Radio Frequency Identification (RFID), computer vision, and neurofuzzy logic for optimizing inventory control. The proposed system aims to enhance inventory monitoring accuracy, automate item identification and placement verification, and optimize reorder levels. Weight sensors track inventory depletion, while RFID tags enable fast and precise identification of items. Computer vision is used to detect correct and incorrect placement of items, ensuring accuracy in warehouse operations. A neuro-fuzzy model is implemented to dynamically adjust reorder points based on inventory levels and demand fluctuations. This framework facilitates seamless, real-time inventory tracking and decision-making, improving operational efficiency, reducing human errors, and supporting timely replenishment decisions. The study underscores the system's potential to transform inventory management practices by integrating IoT technologies, making it highly adaptable to various industries.