Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing
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 o...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/980 |
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| author | Müge Sinem Çağlayan Aslı Aksoy |
| author_facet | Müge Sinem Çağlayan Aslı Aksoy |
| author_sort | Müge Sinem Çağlayan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a3ff821b71f04e358986b0603090fab4 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a3ff821b71f04e358986b0603090fab42025-01-24T13:21:33ZengMDPI AGApplied Sciences2076-34172025-01-0115298010.3390/app15020980Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in ManufacturingMüge Sinem Çağlayan0Aslı Aksoy1Jaguar Land Rover-Solihull, Lode Lane, Solihull B92 8NW, UKIndustrial Engineering Department, Bursa Uludag University, Gorukle Campus, 16059 Bursa, TurkeyIn 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.https://www.mdpi.com/2076-3417/15/2/980material feeding systemsmachine learningmulti-class classificationmanufacturing systems |
| spellingShingle | Müge Sinem Çağlayan Aslı Aksoy Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing Applied Sciences material feeding systems machine learning multi-class classification manufacturing systems |
| title | Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing |
| title_full | Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing |
| title_fullStr | Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing |
| title_full_unstemmed | Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing |
| title_short | Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing |
| title_sort | integrating machine learning and material feeding systems for competitive advantage in manufacturing |
| topic | material feeding systems machine learning multi-class classification manufacturing systems |
| url | https://www.mdpi.com/2076-3417/15/2/980 |
| work_keys_str_mv | AT mugesinemcaglayan integratingmachinelearningandmaterialfeedingsystemsforcompetitiveadvantageinmanufacturing AT aslıaksoy integratingmachinelearningandmaterialfeedingsystemsforcompetitiveadvantageinmanufacturing |