چکیده مقاله
Material backorder prediction is a pivotal problem in supply chain studies that affects an inventory system’s effectiveness and service level Identifying parts with the greatest chance of unavailability before they occur can be a significant opportunity to improve the performance of an organization Accurate backorder prediction is critical for producers with limited production capacity A more advanced prediction method can help predict future orders In this study, we used five different tree based models: Random Forest Classifier, Decision Tree Classifier, and Gradient Boosting Classifier, KNeighbors, Adaboosting models With the help of Spearman feature extraction method, and random over sampler, we found the optimal combination of their hyperparameters We applied two sampling methods to deal with imbalanced data to improve the accuracy of the output in any classification problem Accordingly, the result of the proposed method showed an accuracy of 99 29%, which indicates an almost perfect classification model
کلیدواژهها
نویسندگان
Hamrah Kor
Operation Research and Math Department University of Melbourne Melbourne, Australia
شیوه ارجاع
Kor, Hamrah,1403,Enhancing Material Backorder Prediction in Supply Chain Management: A Comparative Study of Tree-Based Models,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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