## 6 Results and Discussion  
### Analysis of Experimental Data Based on Taguchi Approach  
KPIs are selected to evaluate the performance of a case study and find any bottlenecks in the production line under investigation [74]. The selected KPIs for the validation of the digital model are presented in Table 9.  
The results of the digital model are described in Table 10.  
Table 10 verifies that the bottleneck is placed in the production section (mixing-boiling-homogenization process).  
After conducting the experiments based on the proposed values of Table 8, the mean value of response '\(y_{i}\)' for each experiment is presented in Table 11.  
Using the digital model developed in the previous section, the mean response value for each experiment is presented in Table 11. Based on Equation (2) and the responses in Table 11, the calculation of the SNR is described in Table 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Simulation Time = 480 (min)—Bottle Capacity = 490 (g)** \\ \hline \multirow{3}{*}{**Product**} & **KPI_1: Quantity** & **KPI_2: Productivity** \\  & **(ton/h)** & **(Bottles/h)** \\  & **Theoretical** & **Experimental** & **Theoretical** & **Experimental** \\  & **Value** & **Value** & **Value** \\ \hline Product A & 2 & 2 & 300 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of Digital Model—Before the Implementation of the Proposed Infrastructure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & **Conveyos Velocity** & **Machines’ Productivity** & **Operator Number** \\  & **(A)** & **(B)** & **(C)** \\ \hline
3 & 2 & 1 & 2 \\
4 & 2 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Orthogonal Array (OA) L4 (\(2^{3}\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Key Performance Indicator**} & **Description** \\ \hline KPI\_1 & Production Rate (quantity) of products \\ KPI\_2 & Filling Machines’ Productivity \\ \hline \hline \end{tabular}
\end{table}
Table 9: Key Performance Indicators’ Selection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & **Conveyors** & **Machines’** & **Operator Number** \\  & **Velocity (A)** & **Productivity (B)** & **(C)** \\ \hline
1 & 100 & 68 & 1 & 90 \\
2 & 100 & 300 & 2 & 94 \\
3 & 300 & 68 & 2 & 90 \\
4 & 300 & 300 & 1 & 102 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Resulting L4 Array for the Use Case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & **Conveyors** & **Machines’** & **Operator Number** \\  & **Velocity (A)** & **Productivity (B)** & **(C)** \\ \hline
1 & 100 & 68 & 1 & 32,624 \\
2 & 100 & 272 & 2 & 47,719 \\
3 & 300 & 68 & 2 & 32,640 \\
4 & 300 & 272 & 1 & 130,442 \\ \hline \hline \end{tabular}
\end{table}
Table 11: L4 Orthogonal Array with Experimental Results.  
Figure 12: Main Effects Plot for SN ratios (**A**) Conveyor Velocity, (**B**) Machine’s Productivity, (**C**) Operator Number.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Larger Is Better**} & **Conveyors Velocity** & **Machines’ Productivity** & **Operator Number** \\  & **(A)** & **(B)** & **(C)** \\ \hline
1 & 91.924 & 90.270 & 96.290 \\
2 & 96.290 & 97.940 & 91.922 \\ Rank & 2 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average SNR for Each Factor Level.  
Prior to calculating the contribution level of each factor, the values should be predetermined using Equations (3)-(6).  
The factor that most affects the performance of the production line under investigation is the machine's productivity, with a percentage of 48.20%, as described in Table 14. Therefore, the what-if scenarios will be presented in the next subsections, focusing on the production section, in order to increase the quantity of products produced, as well as the production rate of the machines. Furthermore, the contribution percentages of each control factor are depicted in Figure 13.