###### Abstract  
Today, the intelligent management of market stores in the large distribution field represents one of the most difficult tasks to address, considering the various problems to be managed. Specifically, from the classic issues of managing out-of-stock to the reconstruction of customer sentiment and the optimal management of shelves, scientific research has placed considerable effort on producing robust and efficient solutions to the aforementioned problems. In this context, modern deep learning techniques have allowed for the development of intelligent and adaptive systems capable of automating and significantly improving the management of a large-scale distribution market. Specifically, the authors have designed and implemented an innovative full pipeline that integrates modern deep learning technologies. More in detail, an innovative pipeline embedding a visual AI-based engine for customer sentiment assessment merged with a deep framework for stock management and market store cashflow monitoring is proposed. The innovative proposed system has been tested and validated in a large-scale distribution supermarket, confirming the effectiveness of the proposed solution. Specifically, in the performed testing sessions, the designed pipeline was able to show ad hoc visual customer sentiment assessment with an accuracy of 95% as well as intelligent stock monitoring with an accuracy of 93% in cross validation.  
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Therefore, the problem that will be addressed by the authors in this scientific contribution specifically concerns the management of stock in warehouses and the correct monitoring of out-of-stock phenomena or inventory surplus. These aspects do not depend exclusively on the dynamics of purchase orders but rather on the study of the dynamics of customers in the market store (purchasing habits, purchasing methods, seasonality, in general what the authors defined as \"sentiment of the customer\" in this work). In addition to this, the financial flow of daily purchases and the visual monitoring of the various shelves of the market store actively contribute to characterizing the dynamics of the stock in the warehouse. Furthermore, by cross-referencing the data of the financial flows of product purchases with the sentiment reconstructed by the system herein proposed, the authors will be able to reconstruct--for each category of products monitored--the actual \"interest or sentiment\" of customers for those specific products.  
In order to address the aforementioned issues, the authors, in the context of the R&D activity dedicated to this market field, have designed and implemented a complete novel system named GAIA (Great-distribution Artificial Intelligence-based Algorithms) which provides an intelligent solution to the inefficiencies described above. Specifically, the GAIA system is a composite pipeline capable of monitoring warehouse stock by means of a visual assessment of the customer's purchase sentiment in the store combined with an intelligent assessment of purchase financial flows.  
Basically, GAIA enables a system that always guarantees the presence of products in the market warehouse according to the level of customer demand characterized by the proposed \"sentiment analysis\" crossed with financial flows and order dynamics. At the same time, we also want to avoid warehouse \"over-ordering\" phenomena in order to minimize waste. Finally, through the out-of-stock risk classification performed by the downstream control panel embedded in GAIA, we can determine the main issues of the market store, and then we can enable such recovery actions such as promotions, product price change, and so on.  
The paper herein proposed is structured as follows: In Section 2, a survey of the state-of-the-art will be disclosed, while in Section 3, the proposed pipeline will be presented with a description of the single subsystems. Finally, in Section 4, the experimental results will be reported, while in Section 5, a conclusive discussion and future works analysis will be outlined.