AI-Driven Inventory Intelligence for Large-Scale Retail Operations: A Framework for Real-Time Store-Level Stock Accuracy

Abstract

In the case of the large retail institutions, good inventory visibility is vital in the provision of products, reduction of operational waste and improving customer experiences. Conventional inventory operations, however, are anchored typically on manual audit and hand-slow exception management and store level fragmented store reporting, which results in stock inaccuracies, lost sale opportunities and an inefficient utilization of labor. The AI-driven Intelligence-driven inventory solution used in this paper is designed to improve inventory precision in real-time, in distributed retail environments. The system accepts item-level signals of transactions, associate workflow data, inventory adjust pattern, exception queue, and operational rules and inputs the same into one decision-support system. The system will be trained on predictive analytics and automatic prioritization and identify inventory variance, provide remedial decisions, and help in responding faster to the high-impact stocks. The provided proposal shows how artificial intelligence could be used to transform retail inventory management into responding to actively intelligent. Implementations in in consideration, including the data quality, system scalability, user adoption, auditability and integration with other existing enterprise platforms are also touched in the article. The study contributes to a plausible template in the implementation of AI to store-level inventory optimization not only, but also, the role of the broader intelligent automation in the existing retail supply chains.