Abordagem para seleção e EPI a partir do método de análise multicritério SAPEVO-M-NC: um estudo de caso na construção civil.

Abstract

The construction industry is one of the sectors with the highest rates of workplace accidents in Brazil, characterized by a variety of risk factors. Research indicates that the dynamic and unpredictable nature of construction sites significantly contributes to the frequency of these accidents. These high rates not only jeopardize the physical integrity of workers but also result in considerable economic repercussions for companies. In this context, occupational safety emerges as a multidisciplinary field focused on the prevention of accidents and occupational diseases, aiming to ensure the health and physical integrity of employees. The present study aims to select the best Personal Protective Equipment (PPE) in the categories of split leather gloves, helmets, and safety boots to be used in a construction company through the application of the multicriteria method SAPEVO-M-NC. To gain a deeper understanding of the relevant studies in this area, a series of bibliographic and exploratory research was conducted. By gathering and analyzing this information, it was possible to establish a solid foundation that underpins comprehensive and systematic data collection. Additionally, the use and application of the chosen method were carried out based on the guidelines obtained during the research, ensuring that subsequent analyses were informed by a contextualized and well-grounded understanding of the topics in question. In implementing this application, the ease of entering information into the system was taken into consideration. The well-organized structure of the system allows data to be entered intuitively and efficiently. Moreover, the generated results are presented clearly and statistically significantly, which not only facilitates the interpretation of the information but also streamlines and simplifies the decision-making process for the decision-makers. This approach ensures that decisions are based on concrete and easily understandable data, contributing to more effective management.