## 5 Discussion
### Cost and Efficiency Trends
Meanwhile, the Business as Usual approach remains the most costly throughout, with significant variability and frequent cost spikes, illustrating the inefficiencies of manual decision-making and lack of systematic forecasting. This graph does not dismiss predictive models' value but highlights the complexity and challenges associated with demand forecasting. It also illustrates the substantial benefits realized when demand is known precisely, presenting a best-case scenario for optimization. Thus, these findings serve as a catalyst for investing in the accuracy and robustness of demand forecasting methods within predictive models to bridge the gap between the idealized outcomes of known demand and the fluctuating results of predicted demand scenarios. For the 200-part group, it is shown in Figure 3 that the Simulation PD scenario fluctuates significantly and, like in the 50-part set, sometimes exceeds the Business as Usual scenario in terms of total cost. This could indicate that the predictive model may struggle with scale, where the complexity of managing a more extensive inventory set might amplify the cost of inaccuracies in demand forecasting. The Simulation KD scenario maintains a significant advantage over other approaches, reinforcing that accurate demand knowledge allows for superior inventory optimization. The costs are consistently lower across the observed time frame, suggesting scalable efficiency gains when demand is precisely known. Business as Usual exhibits the highest costs among the three scenarios, which may signal  
Figure 2: Total cost comparison over time for 50 parts. inefficiencies that are magnified as the inventory set increases. This scenario appears to lack the dynamic adjustments possible in the simulated scenarios, leading to less optimized cost outcomes. The (s,S) Policy provides a consistently moderate performance, superior to Business as Usual but generally inferior to Simulation PD and far from the efficiency of Simulation KD. Its performance reflects a rule-based approach that, while systematic, lacks responsiveness to real-time demand dynamics. As is shown in Figure 4, in the 300-part group, the Simulation PD scenario again shows variability in total costs, with peaks potentially reflecting moments of over- or underestimation of demand by the predictive model. This variability seems less pronounced compared to the 200-part inventory, suggesting some degree of scalability in the model's predictive accuracy or a possible indication of the model's adaptive mechanisms that become more effective with larger data sets. Simulation KD continues to outperform the other scenarios, suggesting that the benefits of known demand in cost optimization are evident and consistent across different inventory sizes. The model's cost efficiency is apparent, with the lowest costs throughout, indicating that the ability to forecast demand directly correlates with cost savings precisely. Business as Usual remains relatively stable but at a higher cost level compared to Simulation KD, yet it occasionally dips below the costs observed in Simulation PD. This might reflect the inherent inefficiencies in a system that does not utilize optimization models to respond to changing demand patterns. Forward reserve area replenishment optimization analysis highlights consistent trends and critical insights across all part groups, namely 50, 200, and 300. Simulation PD consistently demonstrates lower direct picking frequencies than Simulation KD and Business as Usual scenarios. This pattern suggests the efficacy of predictive demand optimization in reducing direct picking occurrences, thereby potentially enhancing operational efficiency and cost-effectiveness. Figure 4: Total cost comparison over time for 300 parts. Figure 3: Total cost comparison over time for 200 parts. Furthermore, Simulation KD consistently exhibits lower overall costs allocated to direct picking and higher costs allocated to pulling inventory from forward to buffer areas compared to Simulation PD and Business as Usual.