## 6 Discussions and Conclusions  
The primary objective of the paper in question is to evaluate the effectiveness of the text recognition algorithms application in improving the operational flow at the logistical level within the company. To conduct an accurate analysis, it was necessary to examine the process of manually loading shipping notes and compare it with the process following the implementation of text recognition algorithms. As can be observed from the graphs presented in the previous chapters of the document, operations become simpler and more streamlined. However, the introduction of artificial intelligence has caused some problems during the transitional phase, particularly at the moment when the switch to this system was made. Firstly, the system itself encountered some difficulties in reading the data, generating errors in the loading of shipping notes. Secondly, the lack of precision of the suppliers caused numerous problems in the phases of goods receipt. A recurring error phenomenon is related to the interpretation of orders that are erroneously linked to a specific item in the document. An example of this situation is illustrated in the following Figure 7: in the sixth row, the identification code 146999 was erroneously linked to order number 460663 instead of order number 459842. Figure 6: New structure of shipping notes. This inconvenience occurs occasionally, especially when the lines are closely adjacent. When such circumstances occur, significant difficulties emerge in terms of supply. Therefore, the company has promptly taken action to develop new geometric models, in order to prevent the recurrence of such an event. To solve these problems, several modifications were made to the system, in order to improve precision through the interaction of the \"human-in-the-middle\". The first modification was to define a rule of over-delivery: if the quantity delivered exceeds 5% of the ordered, the transport document in question will be moved to \"Need Attention\", before passing to \"History\". The second modification, equally important, was to prevent the passage to \"History\" of documents that have not corrected yet. In fact, if these present one or more lines with errors or over-delivery, the system will give an error message, minimizing human error. Before these modifications, it happened that some documents were mistakenly sent to \"History\" and entire documents were not loaded. Following these modifications, both small and large, the system immediately proved to be more effective and precise, particularly thanks to the greater reading capacity of artificial intelligence. The text recognition algorithm, in the working environment of the company, has proved to be an excellent tool to facilitate work in the logistics field, but above all it has allowed to minimize errors and make the loading of shipping notes more punctual. This tool allows staff to transcend the most banal tasks, focusing on activities that really add value. The application is very simple, allowing you to quickly become familiar with the tool. However, it is necessary to pay attention in the early periods, to avoid that artificial intelligence makes reading errors and loads the documents erroneously. In the two-month period, specifically February to April, a 50 per cent reduction in shipping notes requiring a \"human in the middle\" was observed, from 8 per cent to 4 per cent. This improvement is attributable to the implementation of specific standards for the structure of the shipping note, forwarded to all the company suppliers and subcontractors. These standards were defined with the aim of providing the text recognition algorithm with documents containing all the necessary information for correct loading. The numerical values corresponding to these percentages are defined in Scheme 2. Figure 7: Example of shipping notes where the text recognition algorithm performed a read error. The study fully met expectations, demonstrating a high capacity for responsiveness and adaptability.