## 3 Results  
The analysis of the OHS system according to the presented methodology was conducted in collaboration with experts who had assessed risks or worked as OHS officers in small window- and door-fitting companies. The idea for the research arose from interactions with individuals engaged in window- and door-fitting activities, and their personal insights into potential OHS difficulties. The study was focused on southern Serbia, more specifically on the Nisava region. In 2023, a total of 1348 apartments with more than 89,000 m\({}^{2}\) were built in the city of Nis, the centre of this region. This represents a 42.4% increase compared with 2022 (776 completed apartments), and a 28.5% increase compared with the five-year average (963 apartments per year). In addition, 2714 apartments remain under construction. The increased construction activity was influenced by the renovations of existing objects, partly caused by Serbia's national program of energy rehabilitation of residential homes and apartments, implemented by the city of Nis. As part of this program, the replacement of external doors, windows, and other transparent elements of the thermal envelope is a key measure. According to the program demands, the external elements must meet specific thermal performance properties: \(U\leq 1.3\) W/m\({}^{2}\)K for glazed windows, balcony doors, and their profiles, and \(U\leq 1.6\) W/m\({}^{2}\)K for doors to unheated spaces, where \(U\) represents the heat transfer coefficient. This measure covers part of the work regularly performed by employees of window- and door-fitting companies, leading to an increased workload. For this analysis, ten OHS experts whose work was related to small window- and door-fitting companies were contacted in September 2024. One did not meet the minimum requirements, and another did not want to participate. Eight experts took part--five male (62.5%) and three female (37.5%). All experts had completed at least a four-year undergraduate degree, while 50% held master's degrees. Their experience in risk assessment was represented by the vector \(R=\{\)2,3,2,2,3,2,3,3\(\}\), experience in risk analysis in construction by \(C=\{\)2,2,2,2,1,2,1,2\(\}\), and total (work) experience by \(T=\{\)3,3,3,3,3,2,3,3\(\}\). Based on these values, according to Equation (1), the \(\varphi_{\mathrm{i}}\) coefficients of experience were determined for the eight experts (\(I\)-\(VIII\)), forming the weight vector \(\Phi=\{\)0.12,0.18,0.12,0.12,0.09,0.08,0.09,0.18\(\}\). The experts did not suggest the inclusion of additional indicators in the initial list (Table 1), so it was used to select the key indicators. Consensus among experts, based on Equation (4), was achieved for the group's degree of satisfaction at \(\rho\geq 0.75\). Key indicators (KIs) were selected for four groups of factors: environment (Table 4), technology (Table 5), organisation (Table 6), and workers (Table 7). \(E_{i}\), \(T_{i}\), \(O_{i}\), and \(H_{i}\) refer to the indicators presented in Table 1. Columns \(I\) to \(VIII\) present how the experts ranked the indicators, while column \(B\) shows the values determined using Equation (2). The comparison of aspects is shown in Table 8. An aggregate matrix was formed based on individual comparison matrices. Equation (9) and the elements of the weight vector \(\Phi\) were applied to obtain the aggregate matrix, followed by Equation (12) to determine the fuzzy weights. Crisp weights of aspects were calculated using Equations (17) and (18).