A Review of Technological Advancements and Operational Improvements in IoT for Retail Inventory Management

Siti Rosmaniza Ab Rashid, Noor Shahida Mohd Kasim and Muhammad Nurakmal Zaidi
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Siti Rosmaniza Ab Rashid: Wireless Broadband & Networking Research Group (WiBNet), Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka.
Noor Shahida Mohd Kasim: Wireless Broadband & Networking Research Group (WiBNet), Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka.
Muhammad Nurakmal Zaidi: Wireless Broadband & Networking Research Group (WiBNet), Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka.

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 8, 69-82

Abstract: This paper provides an in-depth review of recent advancements in IoT-based inventory management systems. The integration of IoT with technologies such as artificial intelligence (AI), machine learning (ML), and blockchain have significantly enhanced the accuracy, security, and efficiency of inventory management across various industries, including retail, healthcare, food processing, and manufacturing. Key findings demonstrate that IoT-enabled real-time monitoring and predictive analytics help businesses optimize stock levels, reduce waste, and improve supply chain responsiveness. Blockchain integration offers improved transparency and data security, ensuring the integrity of inventory records. Building on these insights, this research proposes the development of an IoT-enabled retail inventory management system using LoRa technology combined with predictive analytics. The system aims to predict stock levels, identifying items likely to be understocked or overstocked within a month-long period. By utilizing LoRa’s low-power, long-range communication capabilities, the proposed system offers a scalable, cost-effective solution for both large and small retail operations. The predictive analytics component will enable retailers to proactively adjust inventory, reducing operational inefficiencies and improving customer satisfaction. Future work includes evaluating the system’s predictive accuracy in real-world retail environments and exploring further integrations with blockchain for enhanced data security.

Date: 2025
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