## 5 Results
### Results Validation  
In order to examine the results obtained by the CORASO method, validation of the results using comparative analysis was applied [(60; 61)]. This analysis was performed by using the same initial crisp decision matrix and weights obtained by the SWARA method, but the supplier rankings were performed using the steps of the other methods used. In this study, six MCDM methods were used to determine the ranking. The SAW (simple additive weighting) method is the simplest MCDM method that performs ranking on weighted normalized data. As such, this method is very easy to use and, therefore, it was adopted in this analysis. The MABAC method ranks alternatives based on deviations from the geometric mean [(62)]. In addition, this method uses more complex normalization, and when weighting normalized data, in addition to multiplication with the weights of the criteria of the normalized data, addition with the weights of the criteria was also performed. The ARAS (additive ratio assessment) method is a simple method that uses a utility function \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** \\ \hline AS\({}_{\text{max}}\) & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline SUP1 & 0.891 & 1.000 & 0.976 & 0.907 & 0.931 & 0.975 & 0.973 & 0.911 & 0.849 & 1.000 \\ SUP2 & 1.000 & 1.000 & 0.999 & 1.000 & 1.000 & 0.975 & 1.000 & 0.935 & 1.000 & 0.948 \\ SUP3 & 0.796 & 0.796 & 0.805 & 0.907 & 0.844 & 0.819 & 0.810 & 0.667 & 0.798 & 0.871 \\ SUP4 & 0.653 & 0.574 & 0.573 & 0.615 & 0.677 & 0.767 & 0.615 & 0.766 & 0.581 & 0.714 \\ SUP5 & 0.891 & 0.868 & 1.000 & 0.811 & 0.844 & 0.975 & 0.811 & 0.935 & 0.824 & 0.975 \\ SUP6 & 0.677 & 0.677 & 0.880 & 0.786 & 0.772 & 0.897 & 0.811 & 0.667 & 0.689 & 0.740 \\ SUP7 & 0.891 & 0.978 & 0.904 & 0.954 & 0.834 & 1.000 & 0.884 & 1.000 & 0.975 & 0.819 \\ SUP8 & 0.677 & 0.749 & 0.779 & 0.762 & 0.749 & 0.819 & 0.762 & 0.692 & 0.715 & 0.845 \\ \hline AS\({}_{\text{min}}\) & 0.653 & 0.574 & 0.573 & 0.615 & 0.677 & 0.767 & 0.615 & 0.667 & 0.581 & 0.714 \\ \hline \end{tabular} Source: author composition.  
\end{table} 
Table 10: Normalized decision matrix and alternative solutions. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \(S_{j}\) & \(R_{j}\) & \(R^{\prime}_{j}\) & \(Q_{i}\) & **Rank** \\ \hline AS\({}_{\text{max}}\) & 1.000 & & & & & \\ SUP1 & 0.944 & 0.944 & 0.682 & 0.161 & 2 \\ SUP2 & 0.986 & 0.986 & 0.653 & 0.203 & 1 \\ SUP3 & 0.814 & 0.814 & 0.791 & 0.014 & 5 \\ SUP4 & 0.652 & 0.652 & 0.987 & \(-\)0.204 & 8 \\ SUP5 & 0.897 & 0.897 & 0.718 & 0.111 & 4 \\ SUP6 & 0.759 & 0.759 & 0.848 & \(-\)0.055 & 6 \\ SUP7 & 0.922 & 0.922 & 0.698 & 0.138 & 3 \\ SUP8 & 0.757 & 0.757 & 0.851 & \(-\)0.059 & 7 \\ AS\({}_{\text{min}}\) & 0.644 & & & & & \\ \hline \end{tabular} Source: author composition.  
\end{table} 
Table 11: Calculation of the CORASO method. to determine its value. In addition, it uses a percentage normalization where individual alternatives are divided by the sum of all alternative values. Using the MABAC and ARAS methods, the impact of normalization on the ranking of suppliers was examined. The MARCOS (Measurement of Alternatives and Ranking according to the Compromise Solution) method determines the ranking in relation to ideal and anti-ideal solutions and in relation to the membership function [63]. This method is one of the most widely used newer MCDM methods and is, therefore, included in this analysis. The WASPAS (Weighted Aggregated Sum Product Assessment) method ranks alternatives as a compromise between the two methods WSM (weighted sum model) and WPM (weighted product model). The WAS method is actually a SAW method. The WPM method uses scaling of normalized values with weights. Based on the compromise of these two methods, the ranking was performed.