The increasing scale and complexity of global supply chains have led to new challenges spanning various fields, such as supply chain disruptions due to long waiting lines at the ports, material shortages, and inflation. Coupled with the size of supply chains and the availability of vast amounts of data, efforts towards tackling such challenges have led to an increasing interest in applying machine learning methods in many aspects of supply chains. Unlike other solutions, ML techniques, including Random Forest, XGBoost, LightGBM, and Neural Networks, make predictions and approximate optimal solutions faster. This paper presents an automated ML framework to enhance supply chain security by detecting fraudulent activities, predicting maintenance needs, and forecasting material backorders. Using datasets of varying sizes, results show that fraud detection achieves an 88% accuracy rate using sampling methods, machine failure prediction reaches 93.4% accuracy, and material backorder prediction achieves 89.3% accuracy. Hyperparameter tuning significantly improved the performance of these models, with certain supervised techniques like XGBoost and LightGBM reaching up to 100% precision. This research contributes to supply chain security by streamlining data preprocessing, feature selection, model optimization, and inference deployment, addressing critical challenges and boosting operational efficiency.

翻译：全球供应链规模与复杂性的持续增长带来了跨领域的新挑战，例如港口长时间排队导致的供应链中断、物料短缺以及通货膨胀。结合供应链的庞大规模与海量数据的可获取性，应对此类挑战的努力促使机器学习方法在供应链诸多环节中的应用日益受到关注。与其他解决方案不同，机器学习技术（包括随机森林、XGBoost、LightGBM和神经网络）能够以更快的速度进行预测并逼近最优解。本文提出一种自动化机器学习框架，通过检测欺诈活动、预测维护需求及预报物料缺货来增强供应链安全。使用不同规模数据集的实验结果表明：采用采样方法的欺诈检测准确率达到88%，设备故障预测准确率达到93.4%，物料缺货预测准确率达到89.3%。超参数调优显著提升了模型性能，其中XGBoost和LightGBM等监督学习方法的部分指标可达100%精确率。本研究通过精简数据预处理、特征选择、模型优化与推理部署流程，应对关键挑战并提升运营效率，从而为供应链安全领域作出贡献。