Building Supply Chain Resilience Using Artificial Intelligence in Risk Management Systems

Author:

Gaurav Mayank

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Aven, T., & Krohn, B. S. (2014). A new perspective on how to understand, assess and manage risk and the unforeseen. Reliability Engineering & System Safety, 121, 1–10. https://doi.org/10.1016/j.ress.2013.07.005

2. Bailey, T., Barriball, E., Dey, A., & Sankur, A. (2019, March 8). A practical approach to supply-chain risk management. McKinsey & Company. https://www.mckinsey.com/capabilities/operations/our-insights/a-practical-approach-to-supply-chain-risk-management

3. Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2018). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476

4. Davenport, T., & Alavi, A. (2023, July 6). How to train generative AI using your company’s data. Harvard Business Review. https://hbr.org/2023/07/how-to-train-generative-ai-using-your-companys-data

5. Dey, A., LaGuardia, P., & Srinivasan, M. (2011). Building sustainability in logistics operations: A research agenda. Management Research Review, 34(11), 1237–1259. https://doi.org/10.1108/01409171111178774

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