This study tackles the complexities of global supply chains, which are
increasingly vulnerable to disruptions caused by port congestion, material
shortages, and inflation. To address these challenges, we explore the
application of machine learning methods, which excel in predicting and
optimizing solutions based on large datasets. Our focus is on enhancing supply
chain security through fraud detection, maintenance prediction, and material
backorder forecasting. We introduce an automated machine learning framework
that streamlines data analysis, model construction, and hyperparameter
optimization for these tasks. By automating these processes, our framework
improves the efficiency and effectiveness of supply chain security measures.
Our research identifies key factors that influence machine learning
performance, including sampling methods, categorical encoding, feature
selection, and hyperparameter optimization. We demonstrate the importance of
considering these factors when applying machine learning to supply chain
challenges. Traditional mathematical programming models often struggle to cope
with the complexity of large-scale supply chain problems. Our study shows that
machine learning methods can provide a viable alternative, particularly when
dealing with extensive datasets and complex patterns. The automated machine
learning framework presented in this study offers a novel approach to supply
chain security, contributing to the existing body of knowledge in the field.
Its comprehensive automation of machine learning processes makes it a valuable
contribution to the domain of supply chain management.

该研究通过机器学习方法来应对全球供应链面临的复杂性，从而提高供应链安全性。研究集中于通过欺诈检测、维护预测和物料缺货预测来增强供应链安全。引入一种自动化机器学习框架，优化数据分析、模型构建和超参数优化。研究指出了影响机器学习性能的关键因素，包括抽样方法、分类变量编码、特征选择和超参数优化。机器学习方法在处理大规模数据集和复杂模式时显示出优势，为供应链管理领域提供了有价值的贡献。