## 1 Introduction
* We first analyze, in a more comprehensive way, the identified OSRs by formalizing a set of targeted measures for all the OSRs and not only for the most interconnected ones, as previously performed in [3]. These measures are reported in the first table in Section 4.1 and referenced with the existing literature. This makes our approach flexible and less sensitive to variations in the input data that human experts elicit. Measures are specifically conceived to reduce the occupational stress of workers while optimizing sustainability aspects and could also serve as guidelines to be adopted in any airport scenario. * We herein program a flexible recommendation system developed in Python language that is capable of printing indications in an automated way about the most suitable measures to be implemented with priority. We emphasize once again that the goal here is not of introducing a groundbreaking methodological approach. Instead, we strive to construct a holistic framework firmly grounded in a well-established method drawn from the academic literature and herein upgraded in terms of recommendation and visualization. This framework is designed to be easily understood and readily applicable within corporate environments. This may represent the pioneering implementation of this method in the specialized field of airport infrastructure, aimed at effectively suggesting management measures for reducing the complexities of occupational risk. * We obtain the network of relationships by abandoning the MentalModeler software ([[https://www.mentalmodeler.com/](https://www.mentalmodeler.com/)]([https://www.mentalmodeler.com/](https://www.mentalmodeler.com/)), accessed on 1 October 2023) which we used in [3]. A significant limitation of the MentalModeler software for building FCMs is indeed its inability to provide a clear visual distinction for the most critical factors, which can impede the prioritization of key elements in the model. In this research, differently from the previous approach, we are now capable of automatically displaying the prioritization of OSRs in the network through different layers according to their greater impact. This solution was previously achieved by manually adjusting the network obtained via MentalModeler software. * We validate the code by running it with different input data and, in particular, by using matrices elaborated in previous studies referring to different high-risk operational contexts [4; 5]. For each element of these new input matrices, we hypothesized a set of measures. We observe that, independently of the number of measures defined for each element, the system always recommends all the measures related to the most critical OSR(s). We enrich the validation section by using different matrices elaborated in the literature as the input for FCM in other sectors of activity and confirm results. The recommendation system is ready to be used and applied in several scenarios and industrial contexts for optimizing the risk management process. The present paper is organized as follows. Section 2 covers the literature review, setting the theoretical foundation for the study. In Section 3, we define the methodological steps for implementing the system. Section 4 presents the application of our approach, introducing a case study at an Italian airport, discussing the results and validating the model with different input data. Lastly, in Section 5, we conclude the work by discussing both the advantages and limitations of the proposed approach and suggesting potential directions for future research.