## 4 Case Study
### MCDM Method Steps
The CORASO method is a new MCDM method that has not been widely used in practice. However, in a study conducted by [PERSON] et al. [58], they showed that this method gives consistent results in contrast to some other much better-known methods. [PERSON] et al. [59] showed that the TOPSIS method does not show consistent ranking when the effects of dynamic decision matrices are calculated. Due to this characteristic and the simplicity of operation, this method was chosen for this study in comparison to some other methods such as VIKOR (Multicriteria Competitive Ranking), MABAC or PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation). This method ranks alternatives based on deviations from alternative solutions [58]. Alternative solutions are the highest-value alternatives for individual criteria and the lowest-value alternatives. This method was adapted to this study with the following steps:  
Step 1. Supplier evaluation with linguistic terms. Step 2. Transformation of linguistic terms and application of the definitions of the intuitionistic fuzzy approach. In this step, the formation of the aggregate intuitionistic fuzzy decision matrix is further performed using the Bonferroni mean operator. \[x_{ij}=\left(\frac{1}{k(k-1)}\sum_{ \begin{subarray}{c}i,j=1\\ i\
eq j\end{subarray} }^{k}\overline{n_{i}^{(UL)}p}\overline{n_{i}^{(UL)}q} \right)^{\frac{1}{p+q}} \tag{17}\]  
Step 3. Establishing the decision matrix. In this step, the sum of intuitionistic fuzzy numbers is transformed into crisp numbers. Step 4. Normalizing the decision matrix. \[n_{ij}=\frac{x_{ij}}{x_{j\ max}}\text{ for benefit criteria} \tag{18}\]  
\[n_{ij}=\frac{x_{j\ min}}{x_{ij}}\text{ for cost criteria} \tag{19}\]  
where \(x_{j\ min}\) and \(x_{j\ max}\) are, respectively, the minimum and maximum values for criterion j. Step 5. Determining alternative solutions. Step 6. Aggregating normalized alternatives. \[v_{j}=w_{j}\cdot n_{ij} \tag{20}\]  
Step 7. Calculation of aggregate values. \[S_{j}=\sum_{i=1}^{n}\widetilde{v}_{j} \tag{21}\]  
Step 8. Calculation of deviations from alternative solutions. \[R_{j}\ =\frac{S_{j}}{S_{j\ max\ AS}} \tag{22}\]  
\[R^{\prime}_{j}\ =\frac{S_{\ min\ AS}}{S_{j}} \tag{23}\]  
Step 9. Determining the value of the CORASO method. \[Q_{i}=\frac{R_{j}-R^{\prime}_{j}}{R_{j}+R^{\prime}_{j}} \tag{24}\]  
The improvement of these methods by applying the Bonferroni mean operator is reflected in the fact that the use of classical analyses for the environments is based on the assumption that all elements are independent. However, decision-making in a company is not a completely independent process because DMs still work together and there is a certain mutual influence. Therefore, it is necessary to include their ratings through interactions when calculating the average rating using the Bonferroni mean operator. In addition, the Bonferroni mean operator is a much more flexible tool for determining the mean value because it supports different forms of aggregating ratings in relation to DM preferences. In addition, it allows for the provision of a balanced aggregation of DM ratings. In this way, the steps of these methods are improved and their use in group decision-making is enabled, taking into account and harmonizing DM ratings.