# Optimization of PFMEA Team Composition in the Automotive Industry Using the IPF-RADAR Approach
## 4 Practical Implementation of the Proposed IPF-RADAR  
This chapter presents the practical implementation of the proposed model. Out of a total of 13 candidates, 6 members of the core PFMEA team needed to be selected. Formal positions within the company's hierarchy, as well as the candidates' job titles, were not considered and, therefore, are not mentioned in the paper. As previously stated, the case study was conducted in a company that is a Tier-1 supplier in the automotive supply chain. According to Step 1 of the proposed algorithm, the decision-makers expressed their assessments of the importance of the considered criteria:  
* Shift manager: \(\widetilde{\mathrm{w}}_{1}^{1}=\mathrm{V4}\); \(\widetilde{\mathrm{w}}_{2}^{1}=\mathrm{V5}\); \(\widetilde{\mathrm{w}}_{3}^{1}=\mathrm{V3}\); \(\widetilde{\mathrm{w}}_{4}^{1}=\mathrm{V2}\);
* HR specialist: \(\widetilde{\mathrm{w}}_{1}^{2}=\mathrm{V5}\); \(\widetilde{\mathrm{w}}_{2}^{2}=\mathrm{V4}\); \(\widetilde{\mathrm{w}}_{3}^{2}=\mathrm{V3}\); \(\widetilde{\mathrm{w}}_{4}^{2}=\mathrm{V4}\);
* Production supervisor: \(\widetilde{\mathrm{w}}_{1}^{3}=\mathrm{V5}\); \(\widetilde{\mathrm{w}}_{2}^{3}=\mathrm{V3}\); \(\widetilde{\mathrm{w}}_{3}^{3}=\mathrm{V2}\); \(\widetilde{\mathrm{w}}_{4}^{3}=\mathrm{V3}\). The aggregated (summed) values of the relative importance weights of criteria were determined by applying the IVPFN algebra rules (Step 2):  
\[\widetilde{\mathrm{w}}_{1}=\{[0.92,\;1.00],[0.00,\;0.03]\}\]  
\[\widetilde{\mathrm{w}}_{2}=\{[0.90,\;1.00],[0.00,\;0.05]\}\]  
\[\widetilde{\mathrm{w}}_{3}=\{[0.57,\;0.81],[0.10,\;0.29]\}\]  
\[\widetilde{\mathrm{w}}_{4}=\{[0.70,\;0.90],[0.05,\;0.19]\}\]  
The defuzzified and normalized criterion weights were as follows:  
\[\omega_{1}=0.29\]  
\[\omega_{2}=0.28\]  
\[\omega_{3}=0.19\]  
\[\omega_{4}=0.23\]  
According to Step 3 of the proposed algorithm, the decision-makers evaluated the considered candidates by consensus, which is presented through the fuzzy decision matrix (Table 1). Based on Steps 4 and 5, determination of the maximum and minimum proportion matrices was performed, respectively. Both of these matrices are presented in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline
**Candidate, \(i\)** & \(k\) = 1 & \(k\) = 2 & \(k\) = 3 & \(k\) = 4 \\ \hline \(i=1\) & S6 & S2 & S2 & S4 \\ \(i=2\) & S2 & S5 & S5 & S2 \\ \(i=3\) & S7 & S3 & S5 & S7 \\ \(i=4\) & S2 & S5 & S5 & S2 \\ \(i=5\) & S4 & S7 & S5 & S4 \\ \(i=6\) & S5 & S5 & S7 & S7 \\ \(i=7\) & S6 & S7 & S3 & S5 \\ \(i=8\) & S2 & S6 & S2 & S4 \\ \(i=9\) & S7 & S3 & S5 & S7 \\ \(i=10\) & S7 & S6 & S4 & S3 \\ \(i=11\) & S6 & S1 & S6 & S4 \\ \(i=12\) & S3 & S4 & S7 & S7 \\ \(i=13\) & S4 & S6 & S3 & S6 \\ \hline \end{tabular}
\end{table} 
Table 1: The fuzzy decision matrix. According to Step 6 of the proposed algorithm, the empty range matrix was formed, while applying Step 7 resulted in the formation of the relative relationship matrix, which is presented in Table 3.  
By applying Step 8 of the proposed algorithm, the weighted relative relationship matrix was determined, while Step 9 resulted in the aggregated ranking index, which was used for candidate ranking (Step 10). These values are presented in Table 4.  
From the presented results, it can be seen that the candidates who would be included in the PFMEA team were \(i=3\), \(i=9\), \(i=6\), \(i=12\), \(i=10\), and \(i=7\). In this way, a PFMEA team of six members would be formed. Candidates \(i=3\) and \(i=9\) received equal evaluations across all considered criteria, making it logical that they shared first and second place. These candidates had the highest scores for the first and fourth criteria. They were slightly below average for the second criterion, while they performed fairly well for the third criterion. Candidate \(i=6\) had the highest scores for the third and fourth criteria and was also rated well for the first and