Sepehrnia, Amin 
ORCID: https://orcid.org/0009-0009-2279-2583
(2025)
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.
Masters thesis, Concordia University.
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Abstract
The 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 cross-validation methods (5-fold and 10-fold). The research methodology involved training 98 combinations of two machine learning and five ensemble learning models, incorporating feature selection 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.
| Divisions: | Concordia University > John Molson School of Business > Supply Chain and Business Technology Management |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Sepehrnia, Amin |
| Institution: | Concordia University |
| Degree Name: | M. Sc. |
| Program: | Supply Chain Management |
| Date: | 20 January 2025 |
| Thesis Supervisor(s): | Lahmiri, Salim |
| Keywords: | Keywords: Supply Chain Management, Backorder Prediction, Machine Learning, Demand Forecasting, Inventory Management, Imbalanced Class |
| ID Code: | 995154 |
| Deposited By: | Amin Sepehrnia |
| Deposited On: | 17 Jun 2025 17:47 |
| Last Modified: | 18 Jun 2025 00:00 |
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