## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Validation Set**} \\ \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline  & \multicolumn{4}{c}{Without Parameter Optimization} \\ \hline LR & 85.1 & 82.6 & 84.3 & 83.5 \\ DT & 82.4 & 83.7 & 82.4 & 82.1 \\ RF & 94.3 & 94.3 & 94.3 & 94.3 \\ SVM & 81.9 & 82.2 & 81.9 & 81.6 \\ K-NN & 87.2 & 88.4 & 85.8 & 87.1 \\ ANN & 95.6 & 95.1 & 96.6 & 95.8 \\ \hline  & \multicolumn{4}{c}{With Parameter Optimization} \\ \hline LR & 85.11 & 82.61 & 84.36 & 83.5 \\ DT & 82.97 & 82.36 & 81.97 & 82.14 \\ RF & 93.61 & 93.37 & 93.82 & 93.59 \\ SVM & 82.97 & 84.52 & 80.63 & 82.97 \\ K-NN & 89.36 & 90.33 & 88.79 & 89.56 \\ ANN & 97.82 & 98.61 & 98.07 & 98.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of ML models for the validation set. As demonstrated in Table 6 and Figure 4, the application of demand has a substantial positive influence on Kanban (Shapley value = 0.6479), highlighting Kanban's adaptability to varying demand levels and a negative effect on line storage possibly due to inefficiencies or space constraints when handling large quantities, kitting likely due to inefficiencies in preparing kits for high-volume production, and call-out indicating that is not well-suited for high-demand scenarios (most significant negative effect of \(-0.4117\) on call-out). The compatibility with KV has a minor effect on Kanban, with a value of 0.0029, indicating compatibility considerations may favor Kanban, and on call-out, with a value of 0.1192, suggesting compatibility considerations favor call-out. However, it demonstrates a negative impact on kitting, with a value of \(-0.122\), possibly due to the difficulty of integrating kits into existing systems and no significant effect on line storage. Use case has a positive effect on Kanban with a value of 0.0481, showing Kanban's versatility in different production contexts, and kitting with a value of 0.0644, showing kitting works well in specific specialized production scenarios. A negative effect on line storage, with a value of \(-0.1142\), indicates that the specific use-case scenarios do not favor line-storage feeding and have a minimal effect on call-out, with a value of 0.0017 showing minimal impact from specific use-case scenarios. The reference number exhibits a negative effect on Kanban with a value of \(-0.0871\), suggesting reference numbers are less compatible with Kanban strategies; a positive effect on call-out with a value of 0.0817, indicating \begin{table}
\begin{tabular}{c c c c c} \hline  & **Line Storage** & **Kanban** & **Kitting** & **Call-Out** \\ \hline Demand & \(-0.1142\) & 0.6479 & \(-0.122\) & \(-0.4117\) \\ Compatibility & 0 & 0.0029 & \(-0.122\) & \(0.1192\) \\ with KV & \(-0.1142\) & 0.0481 & 0.0644 & \(0.0017\) \\ Use case & \(-0.0025\) & \(-0.0871\) & 0.008 & \(0.0817\) \\ Supply mode & 0.0008 & \(-0.0017\) & 0.0017 & \(-0.0008\) \\ \hline \end{tabular}
\end{table} 
Table 6: Shapley values for ANN model. Figure 4: Graphical summary of Shapley values for ANN model. call-out benefits from specific reference number configurations; a lesser degree of effect on kitting with a value of 0.008, suggesting a minor role in determining kitting suitability; and a minimal effect on line storage with a negative value of \(-0.0025\), indicating that the reference number plays a negligible role. The supply mode has a minimal contribution across all input parameters and has a positive influence on line storage with a value of 0.0008, suggesting that the supply mode plays a minor role in the material feeding method selection process and on kitting, with a value of 0.0017, indicating that it favors kitting to a mild degree. However, it has minor negative impacts on Kanban and call-out systems. It is imperative to acknowledge that the demand value has the most significant impact on the ANN model, both positively and negatively. Consequently, fluctuations in demand may result in a change to the material feeding method.