## 4 Results and Analysis
### Summary of Handling Activities: Total and Average Metrics  
The findings presented in Table 2 offer valuable insights into the impact of optimization on warehouse handling activities. The key takeaway is the significant reduction in direct picking in our proposed Simulation PD compared to both the (s,S) policy and Business as Usual. This illustrates the efficiency gains achieved through predictive and demand-driven approaches, particularly Simulation KD, which leverages known demand to minimize unnecessary direct picking, one of the most labor-intensive and costly activities in warehouse operations. The results also highlight the role of replenishment in improving operational efficiency. In Simulation KD, higher replenishment rates indicate that the system is replenishing stock at more optimal times and quantities, which in turn reduces the reliance on direct picking. The minimal increase in replenishment activities demonstrates that this process is not overly burdensome and instead adds to the overall efficiency by ensuring the forward area is adequately stocked. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Part Group** & **Scenario** & **Direct Picking (\%)** & **Replenishment (\%)** & \
\begin{tabular}{c} **Buffer Area Usage** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Forward-to-Buffer** \\ **Area Transfer (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{50 Parts} & Simulation PD & 13.83 \(\pm\) 0.7 & 33.75 \(\pm\) 1.1 & 18.91 \(\pm\) 1.5 & 15.75 \(\pm\) 1.2 \\ \cline{2-6}  & (s,S) Policy & 19.5 \(\pm\) 1.1 & 29.1 \(\pm\) 1.8 & 25.4 \(\pm\) 2.1 & 18.3 \(\pm\) 1.9 \\ \cline{2-6}  & Business as Usual & 28.06 \(\pm\) 1.3 & 32.47 \(\pm\) 1.4 & 56.78 \(\pm\) 2.5 & 23.98 \(\pm\) 2.0 \\ \cline{2-6}  & Simulation KD & 2.66 & 36.16 & 15.41 & 22.5 \\ \hline \multirow{4}{*}{200 Parts} & Simulation PD & 6.83 \(\pm\) 0.4 & 17.85 \(\pm\) 0.9 & 7.708 \(\pm\) 0.8 & 6.5 \(\pm\) 0.6 \\ \cline{2-6}  & (s,S) Policy & 10.2 \(\pm\) 0.8 & 15.5 \(\pm\) 1.2 & 12.1 \(\pm\) 1.4 & 9.8 \(\pm\) 1.0 \\ \cline{2-6}  & Business as Usual & 22.78 \(\pm\) 1.2 & 17.41 \(\pm\) 1.0 & 46.05 \(\pm\) 2.8 & 12.01 \(\pm\) 1.1 \\ \cline{2-6}  & Simulation KD & 2 & 18.66 & 0.52 & 11.08 \\ \hline \multirow{4}{*}{300 Parts} & Simulation PD & 5.541 \(\pm\) 0.3 & 14.55 \(\pm\) 0.8 & 5.70 \(\pm\) 0.7 & 4.75 \(\pm\) 0.5 \\ \cline{2-6}  & (s,S) Policy & 8.9 \(\pm\) 0.6 & 13.1 \(\pm\) 1.0 & 9.5 \(\pm\) 1.1 & 7.2 \(\pm\) 0.8 \\ \cline{2-6}  & Business as Usual & 19.15 \(\pm\) 1.1 & 14.24 \(\pm\) 0.9 & 43.88 \(\pm\) 2.9 & 10.30 \(\pm\) 1.0 \\ \cline{2-6}  & Simulation KD & 3.11 & 14.33 & 1.02 & 7.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Handling activities for different part groups (50, 200, 300 parts). The buffer area plays a crucial role in operational efficiency, especially in smaller part groups. In the 50-part scenario, Simulation KD shows a higher reliance on buffer usage, as this model optimizes buffer space to balance demand fluctuations. As the part group size increases, buffer usage in Simulation KD decreases, suggesting that for larger part groups, the optimization model relies more on strategic replenishment and less on buffer storage. This reflects the flexibility of the optimization model, adjusting to the scale of the operation and using buffers only when necessary. If the buffer area were not used at all, we would expect a significant increase in direct picking activities and overall handling costs. Without the buffer, the system would rely more on direct fulfillment from the forward or reserve areas, leading to inefficiencies and increased labor costs. The results make it clear that buffer areas, when used strategically, help smooth out inventory management, reduce unnecessary work, and improve overall efficiency. In summary, the findings show that the use of optimization, particularly Simulation KD, enhances warehouse operations by reducing direct picking and strategically utilizing the buffer area.