Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

Abstract

This article introduces an advanced analytical approach for predicting backorders in inventory management. Backorder refers to an order that cannot fulfilled immediately due to stock depletion. Multiple classification techniques, including Balanced Bagging classifiers, Fuzzy Logic, Variational Autoencoder (VAE) - Generative Adversarial Networks, and Multilayer Perceptron classifiers, are assessed in this work using performance evaluation metrics such as ROC-AUC and PR-AUC. Moreover, this work incorporates a profit function and misclassification costs, considering the financial implications and costs associated with inventory management and backorder handling. The study suggests a hybrid modelling approach, which includes ensemble techniques and VAE, which effectively addresses imbalanced datasets in inventory management. This approach emphasizes interpretability and reduces false positives and false negatives. This research contributes to the advancement of predictive analytics and offers valuable insights for future investigations in backorder forecasting and inventory control optimization for decision-making.