## 6 Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Production Status**} & \multirow{2}{*}{**Item Type**} & \multicolumn{3}{c}{**Case 1**} & \multicolumn{3}{c}{**Case 2**} & \multicolumn{3}{c}{**Case 3 (This Paper)**} & \multicolumn{3}{c}{**Case 4**} \\  & & **Solar** & **Electricity** & **Solar** & **Electricity** & **Solar** & **Electricity** & **Solar** & **Electricity** \\  & & \(h=0\%\) & \((1-h)\%\) & \(h=10\%\) & \((1-h)\%\) & \(h=20\%\) & \((1-h)\%\) & \(h=30\%\) & \((1-h)\%\) \\ \hline In-house & \(A\) & 0 & 0.318 & 0.0318 & 0.2862 & 0.0636 & 0.2544 & 0.0954 & 0.2226 \\ In-house & \(B\) & 0 & 0.293 & 0.0293 & 0.2637 & 0.0586 & 0.2344 & 0.0879 & 0.2051 \\ In-house & C & 0 & 0.455 & 0.0455 & 0.4095 & 0.091 & 0.364 & 0.1365 & 0.3185 \\ \hline Rework & \(A\) & 0 & 0.675 & 0.0675 & 0.6075 & 0.135 & 0.54 & 0.2025 & 0.4725 \\ Rework & \(B\) & 0 & 0.702 & 0.0702 & 0.6318 & 0.1404 & 0.5616 & 0.2106 & 0.4914 \\ Rework & \(C\) & 0 & 0.757 & 0.0757 & 0.6813 & 0.1514 & 0.6056 & 0.2271 & 0.5299 \\ \hline Outsourced & \(A\) & 0 & 0.654 & 0.0654 & 0.5886 & 0.1308 & 0.5232 & 0.1962 & 0.4578 \\ to supplier \(U\) & \(B\) & 0 & 0.637 & 0.0637 & 0.5733 & 0.1274 & 0.5096 & 0.1911 & 0.4459 \\  & \(C\) & 0 & 0.72 & 0.072 & 0.648 & 0.144 & 0.576 & 0.216 & 0.504 \\ \hline Outsourced & \(A\) & 0 & 0.723 & 0.0723 & 0.6507 & 0.1446 & 0.5784 & 0.2169 & 0.5061 \\ to supplier \(V\) & \(B\) & 0 & 0.678 & 0.0678 & 0.6162 & 0.1356 & 0.5424 & 0.2034 & 0.4746 \\  & \(C\) & 0 & 0.741 & 0.0741 & 0.6669 & 0.1482 & 0.5928 & 0.2223 & 0.5187 \\ \hline Outsourced & \(A\) & 0 & 0.7 & 0.07 & 0.63 & 0.14 & 0.56 & 0.21 & 0.49 \\ to supplier \(W\) & \(B\) & 0 & 0.655 & 0.0655 & 0.5895 & 0.131 & 0.524 & 0.1965 & 0.4585 \\  & \(C\) & 0 & 0.683 & 0.0683 & 0.6147 & 0.1366 & 0.5464 & 0.2049 & 0.4781 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Data showing different cases of energy utilizations in the automobile part industry. In each case, the percentage of solar energy consumption is different in the form of \(h\%\) to vary the requirement of electricity. Case 1 illustrates the scenario that is taken by the research work done by [PERSON] et al. [49], where the only main source of energy is electricity and no energy is used from solar PV for manufacturing, while Case 3 shows the initial values as considered in this proposed model having \(h~{}=~{}20\%\). Cases 2 and 4 represent that solar energy fulfills \(h=10\%\) and \(h=30\%\) demand of electricity in the manufacturing system, respectively. In this way, the consumption of electricity also reduces in terms of \(1-h\) in each case. By using linear programming, the results are found and given in Table 12, which shows an intense change in carbon emission (goal \(B\)) with reference to case 3 (\(h~{}=~{}20\%\)). By comparing Case 1 against Case 3 (this paper), it is clear that there is very less negative effect observed in total cost of production i.e., \(-0.1\%\) but a significant change of \(-16.7\%\) and \(+10.6\%\) occur in cost of energy and carbon emissions, when there is no solar energy utilization in the system. The result is very meaningful to understand the impact of positive solar energy utilization on the environment but this increases the cost of production, which can be a trade-off. On the other hand, when solar energy utilization is escalated by 30% as in Case 4, energy cost increases and ultimately affects the total cost of production by almost 0.04% rise. From these results, it is concluded that the utilization of renewable energy from the solar system in the production system is very essential to reducing the carbon footprint of the automobile part industry, but, on the other hand, it slightly increases the total cost of production.