Predictive Analytics and Machine Learning for the Risk-Based Management of Agricultural Supply Chains
Safe, healthy and resilient food supply chains are essential to ensuring the livelihood and well-being of humans and societies, as well as local and global economies. However, the ability to provide and sustain access to nutritious and safe food continues to be a major concern and a challenge for ev...
Main Author: | Renegar, Nicholas |
---|---|
Other Authors: | Levi, Retsef |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2022
|
Online Access: | https://hdl.handle.net/1721.1/140138 |
Similar Items
-
Public health risks arising from food supply chains: Challenges and opportunities
by: Chen, Lu, et al.
Published: (2022) -
Leveraging machine learning to assess market-level food safety and zoonotic disease risks in China
by: Qihua Gao, et al.
Published: (2022-12-01) -
Leveraging machine learning to assess market-level food safety and zoonotic disease risks in China
by: Gao, Qihua, et al.
Published: (2024) -
Using predictive analytics to address risk in complex supply chains
by: Schmidt, Rachel Marie, S.M. Sloan School of Management
Published: (2018) -
Supply Chain 4.0: A Machine Learning-Based Bayesian-Optimized LightGBM Model for Predicting Supply Chain Risk
by: Shehu Sani, et al.
Published: (2023-09-01)