## 5 Discussion  
### Cost and Efficiency Trends  
Total cost trends were observed over time in four scenarios--Simulation PD, (s,S) Policy, Simulation KD, and Business as Usual--for three different parts groups (50, 200, and 300 parts). This analysis provides valuable information about the performance of each scenario and the effectiveness of the optimization models. It highlights how each approach impacts costs, particularly in the context of an automotive parts warehouse. Figure 2 presents the total daily costs for the scenarios of the 50-part group in a line graph. The Simulation KD scenario, which has the perfect knowledge of demand, consistently outperforms all other approaches, establishing a theoretical lower bound on costs. This outcome underscores the significant advantage of accurate demand forecasting in inventory management. The Simulation KD scenario's lower total costs suggest that \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Planning Horizon**} & **Direct Picking (\%)** & **Replenishment (\%)** & **Buffer Area Usage (\%)** & **Forward-to-Buffer** \\  & **(Sum/Mean)** & **(Sum/Mean)** & **(Sum/Mean)** & **Transfer (Sum/Mean)** \\ \hline
1 Week & 18822/15.685 \(\pm\) 0.9 & 405/0.3375 \(\pm\) 0.03 & 3067/2.55 \(\pm\) 0.21 & 3231/2.692 \(\pm\) 0.24 \\ \hline
2 Weeks & 18822/15.685 \(\pm\) 0.9 & 405/0.3375 \(\pm\) 0.03 & 3067/2.55 \(\pm\) 0.21 & 3231/2.692 \(\pm\) 0.24 \\ \hline
3 Weeks & 18710/15.592 \(\pm\) 0.9 & 409/0.341 \(\pm\) 0.03 & 3095/2.575 \(\pm\) 0.21 & 3241/2.701 \(\pm\) 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sum and mean of handling activities for different planning horizons (50 parts). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Planning Horizon**} & **Direct Picking (\%)** & **Replenishment (\%)** & **Buffer Area Usage (\%)** & **Forward-to-Buffer** \\  & & & & **Area Transfer (\%)** \\ \hline
1 Week & 13.83 \(\pm\) 0.7 & 33.75 \(\pm\) 1.1 & 18.91 \(\pm\) 1.5 & 18.91 \(\pm\) 1.2 \\ \hline
2 Weeks & 13.83 \(\pm\) 0.7 & 33.75 \(\pm\) 1.1 & 18.91 \(\pm\) 1.5 & 18.91 \(\pm\) 1.2 \\ \hline
3 Weeks & 13.73 \(\pm\) 0.7 & 33.97 \(\pm\) 1.1 & 19.067 \(\pm\) 1.5 & 19.067 \(\pm\) 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Handling activities for different planning horizons with predicted demands (50 parts). when demand can be known with certainty, inventory systems can be optimized to a level of efficiency that minimizes the total cost, likely through more effective stock positioning and reduced necessity for last-minute adjustments that typically incur higher costs. In contrast, the Simulation PD scenario, which relies on predicted demand, yields a total cost profile that exceeds that of the Business as Usual and (s,S) Policy scenarios at various points. This observation suggests that while predictive models aim to improve standard practices, the inherent uncertainty and potential inaccuracies in demand forecasting may lead to suboptimal decision-making that can inadvertently increase costs. It is important to note, however, that the higher costs associated with the Simulation PD model do not necessarily denote a flawed model; instead, they may reflect the complex trade-offs between different operational objectives, such as service level targets, risk of stockouts, and the cost of holding inventory. Moreover, the peaks in the cost graph for Simulation PD could indicate moments where the predictive model may have over- or under-estimated demand, leading to increased costs due to excess stock or emergency restocking. These variations in the Simulation PD scenario emphasize the critical role of forecasting accuracy and the potential cost implications of deviations from actual demand. Notably, the (s,S) Policy scenario falls consistently between Simulation PD and Business as Usual, validating its role as a reasonable academic benchmark but highlighting its limitations in adapting to dynamic demand.