## 5 Discussion  
The results presented in this study offer valuable insights into optimizing quality control systems in complex production environments. The proposed optimization model, which integrates technology selection with variable sampling rate decisions, demonstrates  
Figure 10: Sensitivity Analysis related to the exclusion of variable sampling rate. (**a**) Relative importance of input parameters. (**b**) Interaction effects of input parameters on objective function degradation. Figure 9: Sensitivity Analysis related to the exclusion of machine vision technology. (**a**) Relative importance of input parameters. (**b**) Interaction effects of input parameters on objective function degradation. strong potential for supporting strategic inspection planning across various operational contexts. The complexity analysis reveals a critical consideration for model deployment: the scalability of the model is highly sensitive to the number of inspection characteristics and available stations. As these parameters increase, the number of variables and constraints grows exponentially, leading to significant increases in solution time. This suggests that while the model is both flexible and detailed, the careful design of problem instances is crucial to maintain tractability, especially in large-scale applications. Techniques such as dimensionality reduction or hierarchical decomposition may help balance model granularity with computational feasibility. These considerations are particularly important for industries that require scalability, like mass production, where the complexity of the problem grows rapidly. In examining technology allocation within the automotive case study, the results highlight a clear preference for machine vision systems, which were selected in 75% of installations. This strong preference indicates that automated visual inspection technologies offer substantial economic and operational advantages due to their efficiency and defect detection capabilities. Interestingly, only 33.3% of the stations were equipped with technology, reflecting the model's selective approach to technology deployment. This targeted strategy suggests that not all stations require technology adoption; instead, investments should be concentrated where they yield the greatest return in terms of inspection coverage, speed, and defect detection. From a managerial standpoint, these findings support the notion that a strategic, selective approach to technological deployment is more cost-effective than blanket technology adoption across the production floor. The model's ability to adjust sampling rates dynamically further enhances its value. Full inspections (sampling rate of 1.0) are recommended most often, especially for machine vision technologies, while partial inspections are suggested under conditions where cost and resource constraints are more significant. This flexibility in adapting inspection strategies not only optimizes the allocation of resources but also prevents over-inspection, which is often a problem in traditional fixed-rate inspection policies. The results highlight that this adaptive approach is particularly valuable when using human-based inspection technologies, which may require more resource-intensive processes. By allowing the model to vary sampling rates based on real-time data and production needs, companies can achieve a more agile, cost-efficient quality control strategy. The Ablation Analysis provides additional insights into the internal dynamics of the model. Removing machine vision from the model resulted in a significant 42.6% increase in the objective function, underscoring the critical role that machine vision plays in achieving optimal inspection strategies. Conversely, excluding human-based inspections led to only a marginal decrease in model performance, though it did reduce the computational effort. This suggests that while human inspections contribute to quality assurance, their impact on the overall cost-effectiveness of the system is relatively limited compared to automated systems.