[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128356-en":3,"doc-seo-128356-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128356,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Leveraging Machine Learning Classifiers for Backorder Prediction - A Comprehensive Framework for Enhancing Supply Chain Efficiency and Inventory Management - Addressing Class Imbalance Issue in Backorder Prediction","The current research explores the application of advanced machine learning and ensemble learning techniques to address challenges of backorder prediction in supply chain management, especially under severe class imbalance. Because accurate forecasting directly impacts service levels and costs, the study evaluates five resampling strategies—Random Under Sampling, ADASYN, SMOTE-ENN, Borderline-SMOTE, and SMOTE-SVM—paired with hyperparameter tuning via Randomized Search CV and 5-fold and 10-fold crossvalidation. Across 98 trained model combinations, results use AUC-ROC as the primary metric, supported by balanced accuracy, F1, and AUC-PR. Findings show ensemble learning, notably XGBoost, improves robustness and accuracy, with SMOTE-ENN and Random Under Sampling delivering strong gains; SHAP-based feature selection helps consistently, while PCA offers limited benefit.","Leveraging Machine Learning Classifiers for Backorder Prediction: A Comprehensive  \nFramework for Enhancing Supply Chain Efficiency and Inventory Management Addressing Class Imbalance issue in Backorder Prediction  \nAmin Sepehrnia  \nA Thesis  \nIn  \nThe Department  \nOf  \nSupply Chain & Business Technology Management  \nPresented in Partial Fulfillment of the Requirements  \nfor the Degree of  \nMaster of Supply Chain Management at  \nConcordia University  \nMontreal, Quebec, Canada  \nJanuary 2025  \n© Amin Sepehrnia, 2022  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Amin Sepehrnia  \nEntitled: Leveraging Machine Learning Classifiers for Backorder Prediction: A Comprehensive Framework for Enhancing Supply Chain Efficiency and Inventory Management-Addressing Class Imbalance issue in Backorder Prediction  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Supply Chain Management  \ncomplies with the regulations of the University and meets the accepted standards with respect to  \noriginality and quality.  \nSigned by the final Examining Committee:  \nPr. Xiaodan Pan  \nPr. Anton Shevchenko  \nPr. Danielle Morin  \nPr. Salim Lahmiri  \nChair  \nExaminer  \nExaminer  \nSupervisor  \nApproved by: Dr. Satyaveer S. Chauhan, Graduate Program Director  \nDate: 31st January 2025  \nDean: Dr. Anne-Marie Croteau  \nABSTRACT  \nLeveraging Machine Learning Classifiers for Backorder Prediction: A Comprehensive Framework for Enhancing Supply Chain Efficiency and Inventory Management Addressing Class Imbalance issue in Backorder Prediction  \nAmin Sepehrnia  \nThe current research explores the application of advanced machine learning and ensemble learning techniques to address the challenges of backorder prediction in supply chain management, specifically when the dataset is severely imbalanced. Considering the critical importance of accurate forecasting in supply chains, this study evaluates the performance of five resampling techniques (Random Under Sampling, ADASYN, SMOTE-ENN, Borderline-SMOTE, and SMOTE-SVM), combined with hyperparameter tuning (Randomized Search CV) and two crossvalidation methods (5-fold and 10-fold) . The research methodology involved training 98 combinations of two machine learning and five ensemble learning models, incorporating featureselection with SHAP and dimensionality reduction using PCA, alongside sophisticated data preprocessing techniques such as MICE for handling missing values. The primary evaluation metric is AUC-ROC, complemented by secondary metrics including balanced accuracy, F1 Score, and AUC-PR, ensuring a holistic assessment of model performance. Key findings demonstrate that ensemble learning models, particularly XGBoost, outperforms classical machine learning models in terms of robustness and being accurate in backorder prediction. Resampling techniques such as SMOTE-ENN and Random Under Sampling significantly enhance model performance, with SMOTE-ENN proving especially effective due to its noise reduction capabilities. Interestingly, dimensionality reduction using PCA was found to have little benefit, whereas feature selection using SHAP consistently improved efficiency and accuracy. The insights derived from this study provide a comprehensive framework for improving predictive performance in supply chain management applications, specifically backorder prediction. By addressing class imbalance, optimizing preprocessing techniques, and rigorously evaluating resampling methods, this research establishes best practices for tackling forecasting challenges in imbalanced, high-dimensional data environments.  \nKeywords: Supply Chain Management, Backorder Prediction, Machine Learning, Demand Forecasting, Inventory Management, Imbalanced Class  \nAcknowledgements  \nI would like to express my deepest gratitude to my supervisor, Prof Salim Lahmiri, for his insightful guidance and encouragement throughout the course of my research. His exper","cbCaivoh1AQUKXtv","https://ap.wps.com/l/cbCaivoh1AQUKXtv","pdf",3120689,4,1,103,"English","en",105,"# 1. Introduction\n## 1.1. Supply Chain Management, Backorder Prediction and Machine Learning Classifiers\n## 1.2. Background\n## 1.2.1. Supply Chain and Demand Forecasting\n## 1.2.2. Inventory Management\n## 1.2.3. Backorder\n## 1.2.4. Imbalanced Dataset\n## 1.2.5. Bullwhip Effect\n## 1.2.6. Artificial Intelligence and Machine Learning\n## 1.3. The main objective and motivation\n## 1.4. The main research questions\n## 1.5. The contributions of this research\n## 1.6. Theoretical Framework and Relevance\n## 1.7. Thesis Structure\n# 2. Literature Review\n## 2.1. The Role of Machine Learning in Addressing Demand Uncertainty and Backorder Prediction\n## 2.2. Machine Learning Approaches to Backorder Prediction in Supply Chain Management\n## 2.3. Review of Related Research on Backorder Prediction and Supply Chain Management\n## 2.4. Imbalance Class\n# 3. Dataset\n# 4. Research Methodology\n## 4.1. Data Preprocessing and Handling Missing Values\n## 4.1.1. MICE: Multiple Imputation by Chained Equations\n## 4.2. Addressing Imbalanced Data\n## 4.2.1. SMOTE-ENN: Synthetic Minority Over-Sampling Technique-Edited Nearest Neighbor","[{\"question\":\"How does the thesis handle severe class imbalance in backorder prediction datasets?\",\"answer\":\"It compares multiple resampling techniques, including Random Under Sampling, ADASYN, SMOTE-ENN, Borderline-SMOTE, and SMOTE-SVM, and evaluates their effect on prediction quality under crossvalidation.\"},{\"question\":\"Which model family performs best for backorder prediction in the study?\",\"answer\":\"Ensemble learning models, particularly XGBoost, outperform classical machine learning models in robustness and accuracy for backorder prediction.\"},{\"question\":\"What evaluation metrics are used to assess model performance?\",\"answer\":\"AUC-ROC is used as the primary metric, complemented by balanced accuracy, F1 score, and AUC-PR to provide a more comprehensive assessment.\"}]","Leveraging Machine Learning Classifiers for Backorder Prediction - A Comprehensive Framework for Enhancing Supply Chain Efficiency and Inventory Management - 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