Leveraging Edge Computing for Real-Time Warehouse Operations in Retail

Authors

Vivek Prasanna Prabu

Abstract

Edge computing presents a transformative approach to warehouse operations within the retail industry by significantly enhancing real-time data processing capabilities. The shift from traditional centralized cloud models to distributed edge architectures provides substantial improvements in latency reduction, operational efficiency, and decision-making speed. This white paper explores edge computing's pivotal role in enabling real-time inventory management, automated picking processes, predictive maintenance, and enhancing security and compliance standards within retail warehouse settings. By integrating edge computing, retailers can drastically reduce response times, minimize downtime, and optimize resource utilization. The deployment of edge computing technologies directly at the warehouse floor facilitates immediate processing and analysis of data, improving operational responsiveness and customer satisfaction. This paper examines best practices for implementing robust edge computing infrastructures, including device selection, data governance, and integration strategies. Furthermore, it discusses challenges associated with edge computing, such as security vulnerabilities and scalability issues, providing strategies to mitigate these risks. Real-world case studies are presented to highlight successful implementations of edge computing solutions, showcasing measurable improvements in inventory accuracy, order fulfillment rates, and operational agility. Finally, this document presents emerging trends and future outlooks for edge computing in retail warehouse environments, emphasizing the importance of continual innovation and investment. Adopting edge computing not only positions retailers ahead of competitors but also ensures sustained operational excellence and resilience against evolving market demands.

Keywords

Leveraging Edge Computing for Real-Time Warehouse Operations in Retail. Vivek Prasanna Prabu. 2021. IJIRCT, Volume 7, Issue 6. Pages 1-11. https://www.ijirct.org/viewPaper.php?paperId=2504025