# Fuzzy-FMEA Theory Approach for Prioritizing Supply Chain Nervousness Factors
## 4 Illustrative Application of the SCN Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Expert** & \(\rho_{e}\) & **EU** & **VU** & **UL** & **NE** & **LK** & **VL** & **EL** \\ \hline
1 & 0.15 & (0, 1.1, 2.7) & (0.9, 2.5, 3.6) & (2.9, 4.6, 6.4) & (3.1, 5.6, 7.4) & (5.8, 7.5, 8.7) & (7.1, 8.9, 9.8) & (9.6, 10, 10) \\ \hline
2 & 0.14 & (0, 1.3, 2.8) & (0.7, 2.4, 3.9) & (2.8, 4.5, 6.3) & (3.4, 5.8, 7.8) & (6.1, 7.1, 8.3) & (7.7, 8.8, 9.9) & (9.4, 10, 10) \\ \hline
3 & 0.11 & (0, 1.2, 2.8) & (0.6, 2.3, 3.2) & (3.1, 4.5, 6.3) & (3.6, 5.9, 7.9) & (5.7, 7.8, 8.7) & (8.3, 8.9, 9.7) & (9.3, 10, 10) \\ \hline
4 & 0.097 & (0, 1.4, 2.4) & (0.8, 2.6, 3.5) & (2.7, 4.7, 6.6) & (3.2, 6.2, 7.6) & (5.4, 7.9, 8.2) & (8.2, 8.7, 9.9) & (9.4, 10, 10) \\ \hline
5 & 0.089 & (0, 1.5, 2.3) & (0.7, 2.7, 3.7) & (2.9, 4.2, 6.2) & (3.9, 6.3, 8.1) & (5.8, 8.1, 9.1) & (7.9, 9.1, 9.8) & (9.5, 10, 10) \\ \hline
6 & 0.086 & (0, 1.7, 3.1) & (0.7, 2.8, 3.6) & (2.5, 5.2, 6.7) & (3.1, 5.7, 8.3) & (6.3, 6.9, 7.8) & (7.7, 9.3, 9.9) & (9.6, 10, 10) \\ \hline
7 & 0.078 & (0, 2.1, 2.2) & (0.9, 2.5, 3.8) & (2.4, 4.1, 6.6) & (3.2, 5.4, 7.5) & (4.8, 7.6, 8.6) & (7.4, 8.6, 9.7) & (9.7, 10, 10) \\ \hline
8 & 0.077 & (0, 1.3, 2.7) & (1.1, 3.1, 4.2) & (2.6, 4.5, 7.1) & (3.3, 5.3, 7.8) & (6.5, 7.8, 8.7) & (8.4, 9.1, 9.8) & (9.7, 10, 10) \\ \hline
9 & 0.066 & (0, 1.2, 2.8) & (0.8, 2.4, 3.3) & (2.5, 4.7, 6.1) & (4.1, 5.6, 7.9) & (6.6, 7.7, 8.5) & (7.5, 8.7, 9.9) & (9.8, 10, 10) \\ \hline
10 & 0.059 & (0, 1.1, 2.6) & (0.8, 2.3, 3.4) & (2.5, 4.1, 6.2) & (3.4, 5.8, 7.6) & (6.4, 7.9, 8.8) & (7.6, 8.8, 9.9) & (9.9, 10, 10) \\ \hline
11 & 0.048 & (0, 1.4, 2.5) & (1.2, 2.1, 3.5) & (2.6, 4.6, 6.3) & (3.5, 5.7, 7.7) & (6.8, 7.6, 8.6) & (7.8, 8.9, 9.9) & (9.8, 10, 10) \\ \hline Total/ & \multirow{2}{*}{1.00 Average} & \multirow{2}{*}{(0, 1.4, 2.6)} & \multirow{2}{*}{(0, 2.5, 3.6)} & \multirow{2}{*}{(2.7, 4.5, 6.4)} & \multirow{2}{*}{(3.4, 5.8, 7.8)} & \multirow{2}{*}{(5.9, 7.6, 8.5)} & \multirow{2}{*}{(7.8, 8.9, 9.8)} & \multirow{2}{*}{(9.6, 10, 10)} \\ Average & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: TFN expert scores for semantic evaluation. Using Equation (1) and the values given in Table 4, the weighted crisp values can be computed from fuzzy semantics, as shown in Table 5.  
Next, the FEMA and the weighting tables are prepared. Furthermore, the FEMA classifications and weights for all SCN levels are listed. A sample of calculations of Se evaluations for interruption failure modes of factor P is illustrated in Table 6, whereas Table 7 displays the weights of the SCN model's elements. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factor/Expert**} & \multicolumn{8}{c}{**Se**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-11}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline \multirow{3}{*}{PII1} & EL & NE & VL & LK & EL & VU & LK & VL & EL & NE & VL \\ \cline{2-11}  & 9.9 & 5.7 & 8.8 & 7.4 & 9.9 & 2.4 & 7.4 & 8.8 & 9.9 & 5.7 & 8.8 \\ \hline \multirow{3}{*}{PII2} & VL & NE & LK & VL & VL & NE & UL & VL & UL & LK & NE \\ \cline{2-11}  & 8.8 & 5.7 & 7.4 & 8.8 & 8.8 & 5.7 & 4.6 & 8.8 & 4.6 & 7.4 & 5.7 \\ \hline \multirow{3}{*}{P2I1} & VL & EL & VL & LK & EL & LV & VL & VL & LK & EL & NE \\ \cline{2-11}  & 8.8 & 9.9 & 8.8 & 7.4 & 9.9 & 7.4 & 8.8 & 8.8 & 7.4 & 9.9 & 5.7 \\ \hline \multirow{3}{*}{P2I2} & EL & VL & LK & UL & VU & NE & VL & LK & VL & EL & VL \\ \cline{2-11}  & 9.9 & 8.8 & 7.4 & 4.6 & 2.4 & 5.7 & 8.8 & 7.4 & 8.8 & 9.9 & 7.4 \\ \hline \multirow{3}{*}{P3I1} & VL & EL & VL & UL & NE & NE & UL & LK & NE & UL & VL \\ \cline{2-11}  & 8.8 & 9.9 & 8.8 & 4.6 & 5.7 & 5.7 & 4.6 & 7.4 & 5.7 & 4.6 & 7.4 \\ \hline \multirow{3}{*}{P3I2} & NE & LK & LK & NE & EU & VU & UL & VU & VL & NE & UL \\ \cline{2-11}  & 5.7 & 7.4 & 7.4 & 5.7 & 1.3 & 2.4 & 4.6 & 2.4 & 8.8 & 5.7 & 4.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experts’ Se scoring for failure modes of factor P.