The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Business and International Management,Management Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s40171-023-00357-w.pdf
Reference79 articles.
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2. Al Qundus, J., Gupta, S., Abusaimeh, H., Peikert, S., & Paschke, A. (2023). Prescriptive analytics-based SIRM model for predicting Covid-19 outbreak. Global Journal of Flexible Systems Management, 24(2), 235–246. https://doi.org/10.1007/s40171-023-00337-0
3. Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G. (2021). Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, 108193. https://doi.org/10.1016/j.ijpe.2021.108193
4. Aldrighetti, R., Zennaro, I., Finco, S., & Battini, D. (2019). Healthcare supply chain simulation with disruption considerations: A case study from Northern Italy. Global Journal of Flexible Systems Management, 20(1), 81–102. https://doi.org/10.1007/s40171-019-00223-8
5. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993–1004. https://doi.org/10.1016/j.future.2019.07.059
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