IoT-driven Smart Warehouses with Computer Vision for Enhancing Inventory Accuracy and Reducing Discrepancies in Automated Systems
Abstract
The increasing adoption of Internet of Things (IoT) technologies and advanced computer vision systems is transforming inventory management in automated warehouses. This review explores how the integration of IoT and computer vision can enhance inventory accuracy and reduce discrepancies, which are critical challenges in modern supply chain operations. IoT-driven smart warehouses utilize interconnected devices such as sensors, RFID tags, and IoT gateways to provide real-time monitoring, tracking, and data transmission of inventory items. When paired with computer vision, which enables automated product recognition, counting, and anomaly detection, these systems can significantly improve operational efficiency and decision-making processes. This paper reviews key IoT components and their applications in smart warehouses, highlighting the role of computer vision in minimizing discrepancies and ensuring accurate inventory counts. We also examine case studies of IoT and computer vision integration in various industries, showcasing their combined potential to address challenges such as miscounts, misplaced items, and human error. Despite the benefits, this review also addresses challenges related to system integration, data management, and scalability. By analyzing current research and technological trends, this paper presents recommendations for enhancing the effectiveness of IoT and computer vision in inventory management and identifies opportunities for future innovations. Overall, this review emphasizes the transformative impact of IoT and computer vision on the warehousing sector, outlining how these technologies can lead to more accurate, efficient, and reliable inventory systems in the era of automation.
Keywords
IoT, smart warehouses, computer vision, inventory accuracy, automated systems, RFID, real-time monitoring, inventory discrepancies, warehouse automation, supply chain management, artificial intelligence, sensor technologies, anomaly detection, inventory tracking.
Citations
IRE Journals:
Victoria Bukky Ayoola , George Osam-Nunoo , Chima Umeaku , Babatunde Olusola Awotiwon
"IoT-driven Smart Warehouses with Computer Vision for Enhancing Inventory Accuracy and Reducing Discrepancies in Automated Systems" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 176-210
IEEE:
Victoria Bukky Ayoola , George Osam-Nunoo , Chima Umeaku , Babatunde Olusola Awotiwon
"IoT-driven Smart Warehouses with Computer Vision for Enhancing Inventory Accuracy and Reducing Discrepancies in Automated Systems" Iconic Research And Engineering Journals, 8(5)
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