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。

通过自动化机器学习增强供应链安全

Enhancing supply chain security with automated machine learning

Enterprise relation extraction aims to detect pairs of enterprise entities
and identify the business relations between them from unstructured or
semi-structured text data, and it is crucial for several real-world
applications such as risk analysis, rating research and supply chain security.
However, previous work mainly focuses on getting attribute information about
enterprises like personnel and corporate business, and pays little attention to
enterprise relation extraction. To encourage further progress in the research,
we introduce the CEntRE, a new dataset constructed from publicly available
business news data with careful human annotation and intelligent data
processing. Extensive experiments on CEntRE with six excellent models
demonstrate the challenges of our proposed dataset.

该研究旨在从非结构化或半结构化文本数据中检测企业实体对并识别它们之间的业务关系，并介绍了一个名为 CEntRE 的新数据集，它由公开的商业新闻数据构建而成，并通过人工标注和智能数据处理进行了精心处理。基于 CEntRE 数据集的六种模型的广泛实验表明了我们提出的数据集的挑战。