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 Multi-layer 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 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 decision-making.

翻译：本文提出了一种用于库存管理中缺货预测的高级分析方法。缺货是指因库存耗尽而无法立即履行的订单。本研究评估了多种分类技术，包括平衡装袋分类器、模糊逻辑、变分自编码器-生成对抗网络以及多层感知机分类器，并采用ROC-AUC和PR-AUC等性能评价指标进行衡量。此外，本研究引入了利润函数和误分类成本，考虑了与库存管理和缺货处理相关的财务影响及成本。结果表明，预测模型在提升库存系统服务水平方面具有有效性，进而促进客户满意度及整体组织绩效。鉴于可解释性是人工智能在商业应用中的重要方面，本研究对所选模型采用排列重要性方法以确定特征的重要程度。本项研究推动了预测分析的发展，并为未来在缺货预测与库存控制优化决策方面的研究提供了宝贵见解。