## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of** & **Mean** & **F-Value** & **\(p\)-Value** & **Significance** \\
**Variation** & **Squares** & **df** & **Square** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 1012.62 & 12 & 84.38 & \(2.757\times 10^{5}\) & \textless{}0.0001 & significant \\ Factors: & & & & & & \\ A & 1.72 & 1 & 1.72 & 5606.23 & \textless{}0.0001 & significant \\ B & 972.16 & 1 & 972.16 & \(3.176\times 10^{6}\) & \textless{}0.0001 & significant \\ C & 21.87 & 1 & 21.87 & 71,453.50 & \textless{}0.0001 & significant \\ D & 0.0095 & 1 & 0.0095 & 30.99 & \textless{}0.0001 & significant \\ AB & 0.0241 & 1 & 0.0241 & 78.87 & \textless{}0.0001 & significant \\ AC & 0.0578 & 1 & 0.0578 & 188.66 & \textless{}0.0001 & significant \\ AD & 0.0100 & 1 & 0.0100 & 32.79 & \textless{}0.0001 & significant \\ BC & 16.74 & 1 & 16.74 & 54,685.03 & \textless{}0.0001 & significant \\ BD & 0.0021 & 1 & 0.0021 & 6.97 & 0.0972 & significant \\ CD & 0.0069 & 1 & 0.0069 & 22.56 & \textless{}0.0001 & significant \\ ABC & 0.0139 & 1 & 0.0139 & 45.34 & \textless{}0.0001 & significant \\ ACD & 0.0036 & 1 & 0.0036 & 11.62 & 0.0006 & significant \\ Residual & 0.0205 & 67 & 0.0005 & & & significant \\ Deviation & & & & & & \\ of the & 0.0014 & 3 & 0.0003 & 1.61 & 0.5633 & not \\ model & & & & & & significant \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA-Conwip. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of** & **Mean** & **F-Value** & **\(p\)-Value** & **Significance** \\
**Variation** & **Squares** & **df** & **Square** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 703.33 & 13 & 54.10 & \(2.949\times 10^{5}\) & \textless{}0.0001 & significant \\ Factors: & & & & & & \\ A & 675.94 & 1 & 675.94 & \(3.684\times 10^{6}\) & \textless{}0.0001 & significant \\ B & 13.66 & 1 & 13.66 & 74,455.62 & \textless{}0.0001 & significant \\ C & 0.0081 & 1 & 0.0081 & 44.01 & \textless{}0.0001 & significant \\ D & 0.0146 & 1 & 0.0146 & 28.68 & \textless{}0.0001 & significant \\ AB & 0.0053 & 1 & 0.0053 & 28.84 & \textless{}0.0001 & significant \\ AC & 0.0484 & 1 & 0.0484 & 263.63 & \textless{}0.0001 & significant \\ AD & 0.0044 & 1 & 0.0044 & 23.84 & \textless{}0.0001 & significant \\ BC & 9.77 & 1 & 9.77 & 53,259.45 & \textless{}0.0001 & significant \\ BD & 0.0020 & 1 & 0.0020 & 10.68 & 0.0017 & significant \\ CD & 0.0081 & 1 & 0.0081 & 44.10 & \textless{}0.0001 & significant \\ ABC & 0.0099 & 1 & 0.0099 & 54.16 & \textless{}0.0001 & significant \\ ABD & 0.0015 & 1 & 0.0015 & 8.22 & 0.0056 & significant \\ ACD & 0.0049 & 1 & 0.0049 & 26.76 & \textless{}0.0001 & significant \\ Residual & 0.0121 & 66 & 0.0002 & & & & significant \\ Deviation & & & & & & & \\ of the & 0.0006 & 2 & 0.0003 & 1.81 & 0.1726 & not \\ model & & & & & & significant \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA-Hybrid Kanban/Conwip. The deviations of the models, as seen in the tables were not significant, which was indicated by F-value, meaning that the models describe the phenomenon well enough. After the analysis of variance, regression analysis was carried out for all mechanisms. All the values gather by regression analysis indicated significance of generated model and that the regression model is different from a random phenomenon. [69].