###### Abstract  
In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the selection of material feeding methods, including Kanban, line-storage, call-out, and kitting systems, within a manufacturing company. The research employs six machine learning (ML) algorithms--logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)--to develop a multi-class classification model for material feeding system selection. Utilizing a dataset comprising 2221 materials and an 8-fold cross-validation technique, the ANN model exhibits superior performance across all evaluation metrics. Shapley values analysis is employed to elucidate the influence of pivotal input parameters within the selection process for material feeding systems. This research provides a comprehensive framework for material feeding system selection, integrating advanced ML models with practical manufacturing insights. This study makes a significant contribution to the field by enhancing decision-making processes, optimizing resource utilization, and establishing the foundation for future studies on adaptive and scalable material feeding strategies in dynamic industrial environments. to the assembly line. The quantity of materials stored in the container is determined by the specific product type and is replenished when the container is empty. The operator responsible for assembly or material preparation retrieves the requisite materials from the designated storage location and prepares them in accordance with the specifications outlined in the production plan. In the event of a material shortage on the assembly line, production can continue uninterrupted by procuring new products directly from the adjacent container, as a consistent supply of materials is maintained in close proximity to the line. In manufacturing companies with a high degree of product variety, the time spent searching for the requisite parts and the space required for their placement next to the assembly line are considerable [1]. Kanban is a tool that facilitates the communication of information in accordance with the principles of lean production pull. Kanban facilitates the transfer of materials by enabling the circulation of information from the final production stage to the initial production stage. Upon withdrawal of the product from the designated area, a signal is generated to indicate the transition to the preceding stage of the process. The Kanban method is a material feeding technique that is employed to prevent overproduction and to reduce the number and cost of components in the production process that are not yet complete. The subsequent process stage transmits a signal to the preceding process stage, indicating whether production should continue or cease, utilizing a Kanban card. As the production decision is based on information from the subsequent process, there is no requirement for external planning, and the process is straightforward to manage. The term \"kit\" is used generically to describe a box containing parts used in one or more assembly steps [2]. A kit is defined as a specialized collection of parts [3] and a collection of materials that are required to produce a product or complete a production step [4]. The term 'kitting' is defined as the process of supplying the requisite components by placing predetermined quantities of parts together in specially designed containers and the process of handling only those parts that have been defined as necessary for a specific period and dispatching them to designated stations [5].