Enhancing Supply Chain Risk Management by Applying Machine Learning to Identify Risks

被引：4
|
作者：
Hassan, Ahmad Pajam [1 ]
机构：
[1] Oldenburg Univ, Dept Comp Sci, Oldenburg, Germany
来源：
BUSINESS INFORMATION SYSTEMS, BIS 2019, PT II | 2019年 / 354卷
关键词：
Supply chain risk management; NLP; Data analytics; Machine learning; Risk identification; BIG DATA ANALYTICS; TEXT CLASSIFICATION; PREDICTIVE ANALYTICS; DESIGN; INFORMATION; SCIENCE;
D O I：
10.1007/978-3-030-20482-2_16
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Supply chain risks negatively affect the success of an OEM in automotive industry. Finding relevant information for supply chain risk management (SCRM) is a critical task. This investigation utilizes machine learning to find risk within textual documents. It contributes to the supply chain management (SCM) by designing (i) a conceptual model for supply risk identification in textual data. This addresses the requirement to see the direct connection between data analytics and SCM. (ii) An experiment in which a prototype is evaluated contributes the requirement to have more empirical insight in the interdisciplinary field of data analytics in SCRM.
引用
收藏
页码：191 / 205
页数：15
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