End-to-end supply chain resilience management using deep learning, survival analysis, and explainable artificial intelligence-Reference-Cited by-同舟云学术

End-to-end supply chain resilience management using deep learning, survival analysis, and explainable artificial intelligence

Published:2024-06-28 Issue: Volume: Page:1-29
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Li Xingyu¹²,Krivtsov Vasiliy²³,Pan Chaoye²,Nassehi Aydin⁴,Gao Robert X.⁵,Ivanov Dmitry⁶

Affiliation:

1. School of Engineering Technology, Purdue University, West Lafayette, IN, United States

2. Ford Motor Company, Dearborn, United States

3. Department of Mechanical Engineering, University of Maryland, College Park, United States

4. School of Electrical, Electronic, and Mechanical Engineering, University of Bristol, Bristol, United Kingdom

5. Department of Mechanical & Aerospace Engineering, Case Western Reserve University, Cleveland, United States

6. Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2367685

Reference114 articles.

1. A reinforcement learning-based framework for disruption risk identification in supply chains

2. Predicting supply chain risks using machine learning: The trade-off between performance and interpretability

3. Artificial Intelligence-Driven Innovation for Enhancing Supply Chain Resilience and Performance Under the Effect of Supply Chain Dynamism: An Empirical Investigation;Belhadi Amine;Annals of Operations Research,2021

4. Predicting corporate credit risk: Network contagion via trade credit

5. An empirically derived agenda of critical research issues for managing supply-chain disruptions