AI-Driven Hybrid Edge-Cloud Architecture for Real-Time Big Data Analytics and Scalable Communication in Retail Supply Chains

There are various challenges that the retail sector faces these days, and these include the fluctuating customer trends, poor inventory management especially when dealing with multiple channels, and language barriers. This paper proposes an AI-enabled Hybrid Edge-to-Cloud Infrastructure as the right solution to the challenges in the retail supply chain. This solution will streamline the data visualization and communication and increase the efficiency of the communication while at the same time improving the inventory demand forecasting. The combination of AI algorithms and a mix of models into one platform will transform the supply chain by offering direct solutions to current inefficiencies. The fabric has self-learning AI algorithms on the edge to react to changing demand patterns to enable localized decision-making for replenishment inventory items and accurate demand forecasting for last-mile delivery planning. In cloud computing technology solutions, generative AI creates scenarios-based models for longer-term planning and multi-channel communication for cross-functional collaboration. An advanced hierarchy-based blending feature makes it easy to sync edge and cloud operations without losing time transmitting data or making real-time decisions. The platform also consists of AI-advanced communication protocols focusing on data flow for reliable information flow across supply chain networks. It uses shortened technical language and prioritizes outcome-driven practical gains and action. Using real-world cases and actual implementations, this model exhibits fewer supply chain failures, more consistent demand and supply alignment, and significant savings. By confronting them with novel solutions, this research is an innovation-driven case study for retail supply chains to see how the AI fusion of edge and cloud can improve efficiency, agility, and scale to address dynamic challenges.