## 5 Discussion
The framework of the proposed study is predicated on a range of production-related factors, including, but not limited to, demand fluctuations, product variety, and physical space limitations. The integration of ML models provides a robust and innovative approach to address the complexities of this decision-making process. The integration of six classification-based ML algorithms, including LR, DT, RF, SVM, K-NN, and ANN, by incorporating a multi-class classification system, represents a pioneering advancement in the field, demonstrating the application of advanced computational techniques within the material feeding system selection process. This methodological innovation has been demonstrated to enhance the accuracy, efficiency, and scalability of the decision-making process within dynamic manufacturing environments. The present study establishes a foundation for future investigations into the intersection of advanced analytics and engineering applications by combining ML models with manufacturing operations research. It opens avenues for refining and expanding material feeding systems to meet evolving industry needs. The study's comprehensive and interdisciplinary approach offers both theoretical advancements and practical benefits, thereby establishing itself as a valuable resource for researchers and practitioners in the fields of manufacturing and supply chain management. Overall, this study bridges the gap between theoretical research and practical application, providing a comprehensive and adaptable framework for material feeding system selection in manufacturing. The findings are not only relevant to the specific case study but also serve as a foundation for broader applications and future innovations in engineering and operations management. This study adopts a comprehensive approach; however, the practical challenges of integrating advanced ML models into existing manufacturing workflows, such as data preparation and system integration, require further exploration. This study utilizes a static dataset for the training and testing of ML models, which does not account for real-time changes in demand, production schedules, or supply chain disruptions. The incorporation of dynamic, real-time data could provide a more adaptive and responsive framework for material feeding system selection. Despite the study's mention of ergonomic conditions, a more detailed analysis is required to ascertain the impact of the proposed material feeding systems on workers' physical demands and environmental considerations, such as waste reduction and energy efficiency. By acknowledging these limitations, future research can build on the study's findings, addressing these gaps and broadening the scope to create more versatile and widely applicable solutions for material feeding in manufacturing systems. **Author Contributions:** Conceptualization, M.S.C. ; methodology, M.S.C. and A.A.; project administration, M.S.C. ; supervision, A.A.; writing--original draft preparation, M.S.C. and A.A.; writing--review and editing, M.S.C. and A.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data supporting the findings of this study are available upon request from the corresponding author. Public access is restricted to protect privacy. **Conflicts of Interest:** Author [PERSON] was employed by the company Jaguar Land Rover-Solihull. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.