利用自动化机器学习增强供应链安全
Enhancing supply chain security with automated machine learning
AI总结：
本文提出自动化机器学习框架，用于欺诈检测、维护预测与材料延期交货预测，并通过超参数调优显著提升供应链安全与运营效率。
摘要：
全球供应链日益增长的规模和复杂性带来了横跨多个领域的新挑战，例如港口长时间等待排队造成的供应链中断、材料短缺和通货膨胀。再加上供应链的庞大规模以及海量数据的可获得性，应对这些挑战的努力使人们越来越关注在供应链的许多方面应用机器学习方法。与其他解决方案不同，包括 Random Forest、XGBoost、LightGBM 和 Neural Networks 在内的 ML 技术能够更快地进行预测并近似最优解。本文提出一个自动化 ML 框架，通过检测欺诈活动、预测维护需求和预测材料延期交货订单来增强供应链安全。使用不同规模的数据集，结果表明：欺诈检测借助采样方法达到 88% 的准确率，机器故障预测达到 93.4% 的准确率，材料延期交货订单预测达到 89.3% 的准确率。超参数调优显著提升了这些模型的性能，其中 XGBoost 和 LightGBM 等某些监督技术的精确率最高达到 100%。本研究通过简化数据预处理、特征选择、模型优化和推理部署，为供应链安全作出贡献，应对关键挑战并提升运营效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
