Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/5539
Title: Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations
Authors: Maitra, Sarit
Kundu, Sukanya
Keywords: Artificial Intelligence
Decision Making
Inventory Management
Machine Learning
Misclassification Cost
Supply Chain Analytics
Issue Date: 2023
Publisher: 27th International Computer Science and Engineering Conference 2023, ICSEC 2023
Citation: pp. 18
Abstract: This article introduces an advanced analytical approach for predicting backorders in inventory management. Backorder refers to an order that cannot be immediately fulfilled due to stock depletion. Multiple classification techniques, including Balanced Bagging Classifiers, Fuzzy Logic, Variational Autoencoder Generative Adversarial Networks, and Multilayer Perceptron classifiers, are assessed in this work using performance evaluation metrics such as ROCAUC and PRAUC. Moreover, this work incorporates a profit function and misclassification costs, considering the financial implications and costs associated with inventory management and backorder handling. The results demonstrate the effectiveness of the predictive model in enhancing inventory system service levels, which leads to customer satisfaction and overall organizational performance. Considering interpretability is a significant aspect of using AI in commercial applications, permutation importance is applied to the selected model to determine the importance of features. This research contributes to the advancement of predictive analytics and offers valuable insights for future investigations in backorder forecasting and inventory control optimization for decisionmaking. © 2023 IEEE.
URI: https://doi.org/10.1109/ICSEC59635.2023.10329654
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/5539
ISBN: 9798350342109
Appears in Collections:Conference Papers

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