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DOI: https://doi.org/10.63345/ijrmeet.org.v9.i11.1
Ishu Anand Jaiswal
Independent Researcher
Civil Lines , Kanpur, UP, India-208001
Abstract— This is because the increasing complexity of global retail networks has only amplified these limitations, which have traditionally been rooted in fragmented decision-making and manually coordinated roll-outs. While literature provides strong foundations in AI-driven store operations, machine learning-based site selection, IoT-enabled smart store environments, and centralized algorithmic merchandising, it remains disconnected in scope and architecture. Though existing studies optimize singular tasks such as demand forecasting, assortment planning, spatial site evaluation, and RFID-based inventory detection, none of these works integrate these capabilities into an end-to-end orchestration framework capable of managing coordinated deployment programs across thousands of stores. Critical gaps persist in cross-store synchronization, multi-objective rollout scheduling, causal impact evaluation of deployed configurations, and human-in-the-loop governance for large-scale operational changes. The current research fills these gaps by proposing an AI-orchestrated store deployment architecture that unifies site selection intelligence, smart-store sensing, centralized optimization engines, and adaptive configuration management into a cohesive system. The proposed framework deploys reinforcement learning, spatiotemporal modeling, and closed-loop feedback to automate the planning, execution, and evaluation of store deployments across heterogeneous global markets. In sum, the contribution of this work creates a foundational blueprint for next-generation autonomous retail networks, where scalable, data-driven, and continuously improving deployment processes bridge the long-standing divide between operational AI and strategic retail expansion.
Keywords— AI-orchestrated retail deployment, smart-store analytics, multi-store optimization, automated site selection, adaptive configuration management
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