## 5 Discussion
The material feeding method selection problem is structured based on a multi-class classification problem, with four output parameters and five input parameters being determined. Six different classification-based ML algorithms, LR, DT, RF, SVM, K-NN, and ANN, are used for the material feeding method selection problem. The training dataset, which consists of 2221 materials, is split into two sets (training and testing) before running ML algorithms. In this study, the 8-fold CV technique is applied to the dataset, and random sampling is used to determine the folds. The ANN model demonstrates superior performance across all metrics when identifying the correct material feeding method. The Shapley values of the input parameters are analyzed for the developed ANN model. While the ANN method is commonly referred to as a 'black box' method, the analysis of Shapley values provides actionable insights into the influence of input parameters on material feeding system selection. According to the Shapley value results, fluctuations in demand may result in a change to the material feeding method. The Kanban method is the most influenced by demand and use-case parameters. The compatibility with KV and supply mode parameters exhibits low contributions across the material feeding methods. The call-out method demonstrates sensitivity to demand and compatibility with the KV parameters. In traditional manufacturing companies, material feeding method selection frequently depends on human decision-making, leading to hidden costs associated with inefficiencies in operational activities, excess inventory, and disruptions in material feeding. In contrast, the proposed ANN-based approach enhances the accuracy of the material feeding method selection, reducing downtime and enhancing productivity. The analysis encompasses implementation costs, including hardware upgrades for data collection, software licenses for ML platforms, and personnel training. These are then weighed against the long-term savings from decreased waste, reduced stock levels, and faster response times to production changeovers. For example, a reduction in incorrect assembly line feeding has been estimated to reduce re-work costs by a significant margin. These comparisons underscore the economic feasibility and strategic value of transitioning to a data-driven, ANN-based material feeding system. It is acknowledged that the ANN model incurs higher initial costs than traditional human-based models; however, it has been demonstrated that the operational savings resulting from reduced labor, inventory, and storage requirements result in favorable long-term cost efficiency. The ANN-driven solution can accommodate additional product lines, different material types, and varying demand levels in contrast to traditional methods. The transition to an ANN-driven material feeding strategy has been shown to deliver significant productivity, efficiency, and cost savings benefits. Despite the substantial initial investment and training costs, the long-term return on investment (ROI) is significant due to the enhanced operational efficiencies, improved decision-making processes, and increased operator productivity. This study makes a significant contribution to the advancement of knowledge concerning material feeding systems in manufacturing environments, thereby facilitating an improved understanding and enhanced application of such systems. The present study addresses the unique challenges faced by manufacturers operating in high-variety, low-volume production environments. This study was conducted within the context of a manufacturing company specializing in the production of hydraulic and pneumatic motors, along with agricultural and industrial vehicles. The findings of this research endeavor to offer significant real-world relevance, providing practical insights that can be directly applied in industry.