Federated machine learning for privacy preserving, collective supply chain risk prediction
Author:
Affiliation:
1. Institute for Manufacturing, University of Cambridge, Cambridge, United Kingdom
Funder
Engineering and Physical Sciences Research Council
Publisher
Informa UK Limited
Subject
Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management
Link
https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2164628
Reference64 articles.
1. Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions
2. Barros J. J. N. Gonçalves P. Cortez and M. S. Carvalho. 2022. “A machine learning strategy for estimating supply lead times towards improved safety stock dimensioning.” Available at SSRN 4118108 .
3. Supply chain risk management and artificial intelligence: state of the art and future research directions
4. Briggs C. Z. Fan and P. Andras. 2021. “Federated learning for short-term residential energy demand forecasting.” arXiv preprint arXiv:2105.13325 .
5. Brintrup A. 2020. “Artificial intelligence in the supply chain.” The Oxford Handbook of Supply Chain Management .
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