## 5 Results  
When examining which of the selected suppliers shows the best results, it is first necessary to examine the importance of the selected criteria. The first step of this analysis is the DM's assessment of the importance of the criteria using linguistic terms (Table 4). After that, these linguistic terms are transformed into the corresponding intuitionistic numbers according to the defined membership function (Table 2). For example, the value very very good is transformed into an intuitionistic number [0.90, 0.10], the term very good into an intuitionistic number [0.80, 0.10] and the value good into [0.70, 0.20]. Then, the uncertainty value is calculated, which is calculated using Equation (4). For example, for the value very good, the uncertainty value is calculated as follows:  
\[1-0.80-0.10=0.10\]  
This is calculated for all values and an intuitive decision matrix is formed (Table 5). The next step in calculating the weights using the SWARA method is to transform the intuitive decision matrix into a crisp decision matrix. This is performed by calculating distance measures. In order to calculate these distance measures, positive and negative ideal solutions must be determined. These solutions are determined so that all elements belong to the set and do not belong to the set. In this way, the positive ideal solution is [1, 0], and the negative ideal solution is [0, 1]. Once this is determined, the distance measures are calculated. In the example of criteria C1 and DM1 (E1), this is calculated as follows: (Equations (10)-(12)) \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**DMs** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** \\ \hline DM1 & VVG & VVG & VEG & MEG & MEG & GOD & MED & MED & MEG & VVG \\ DM2 & VVG & VVG & VEG & GOD & GOD & VEG & MED & MED & MEG & VEG \\ DM3 & VEG & VVG & VVG & GOD & MEG & GOD & MED & MED & MEG & VVG \\ DM4 & VEG & VEG & GOD & MEG & MEG & GOD & MED & MED & MEG & VVG \\ DM5 & VVG & VVG & VEG & GOD & GOD & GOD & MEG & MEG & MEG & VEG \\ \hline \end{tabular} Source: author composition.  
\end{table} 
Table 4: Assessment of the importance of criteria by DMs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **C1** & **C2** & **C3** & **C4** &... & **C10** \\ \hline DM1 & (0.90, 0.10, 0.00) & (0.90, 0.10, 0.00) & (0.80, 0.10, 0.10) & (0.60, 0.30, 0.10) &... & (0.90, 0.10, 0.00) \\ DM2 & (0.90, 0.10, 0.00) & (0.90, 0.10, 0.00) & (0.80, 0.10, 0.10) & (0.70, 0.20, 0.10) &... & (0.80, 0.10, 0.10) \\ DM3 & (0.80, 0.10, 0.10) & (0.90, 0.10, 0.00) & (0.90, 0.10, 0.00) & (0.70, 0.20, 0.10) &... & (0.90, 0.10, 0.00) \\ DM4 & (0.80, 0.10, 0.10) & (0.80, 0.10, 0.10) & (0.70, 0.20, 0.10) & (0.60, 0.30, 0.10) &... & (0.90, 0.10, 0.00) \\ DM5 & (0.90, 0.10, 0.00) & (0.90, 0.10, 0.00) & (0.80, 0.10, 0.10) & (0.70, 0.20, 0.10) &... & (0.80, 0.10, 0.10) \\ \hline \end{tabular} Source: author composition.  
\end{table} 
Table 5: Intuitionistic decision matrix. \[\delta_{11}^{+}=\sqrt{\left(0.9-1\right)^{2}+\left(0.1-0\right)^{2}+\left(0-0 \right)^{2}}=0.14, \begin{array}{c}\delta_{11}^{-}=\sqrt{\left(0.9-0\right)^{2 }+\left(0.1-1\right)^{2}+\left(0-0\right)^{2}}=1.27,\\ CC_{11}=\frac{1.27}{0.14+1.27}=0.90\end{array} \]  
Using the same procedure, a crisp decision matrix is formed (Table 6). After that, the average score is calculated using the Bonferroni mean operator. These scores then form the basis for calculating weights using the SWARA method. Using the example of criterion C1, the Bonferroni mean operator is calculated as follows (izraz 13):  
\[r_{1}=\sqrt{\left(\frac{1}{5(5-1)}\times\left(0.9\times 0.9+0.9\times 0.831+0.9 \times 0.831+0.9\times 0.9+\cdots+0.9\times 0.831\right)}\right)=0.872\]  
When calculating the Bonferroni mean operator, first, determine how many DMs there were; in this case, there were five DMs. Then, each DM value is multiplied by the other DM. This is performed in pairs and these values are added. For example, the values of the first DM are multiplied by the values of the other DMs.