Food Supply Chain Analytics and Sensing Initiative

Food Supply Chains Health Risks

Credit: Betsy Skrip, CBI

Risk-based Sampling of Food Supply Chains in China

Food adulteration within the supply chain occurs due to negligence, economically motivated adulteration (EMA), or malicious motivations. In China, food safety regulatory organizations, such as the State Administration for Market Regulation (AMR), rely on random sampling for food safety tests (intended to broadly test for adulterants throughout the supply chain). AMR data is then published independently by each province and prefecture. 

Our database work developed a method for scraping AMR data from across these various websites and we: 1) constructed our own automated and centralized database of millions of food inspection results nation-wide, 2) generated interactive maps of high-risk farms/manufacturers, and 3) identified food supply chain sectors which are at high risk for food adulteration. 

Using our self-constructed database, we developed a framework for risk-based testing, which uses historical data to identify risky products, companies, and supply chain locations that can significantly improve the ability to locate risk. Analysis shows that provinces selling more animals through high-risk markets have more human cases of zoonotic flu. The results illustrate the effectiveness of this method in identifying risk, and it can facilitate a more granular sampling strategy to enhance food safety in China. The hope is that our approach may offer a new way of understanding zoonotic disease risks, as well as informing regulatory approaches to reduce them.

Principal Investigators

Retsef Levi

Retsef Levi

J. Spencer Standish (1945) Professor of Management

Retsef Levi is the J. Spencer Standish (1945) Professor of Operations Management at the MIT Sloan School of Management. He is a member of the Operations Management Group at MIT Sloan and affiliated with the MIT Operations Research Center. Levi also…

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Stacy Springs

Stacy Springs

Executive Director, Food Supply Chain Analytics and Sensing (FSAS) Initiative

"Leveraging Machine Learning to Assess Market-level Food Safety and Zoonotic Disease Risks in China."

Gao, Qihua, Retsef Levi, and Nicholas Renegar. Scientific Reports Vol. 12, (2022): 1650.

Faculty: Retsef Levi
"Testing at the Source: Analytics-Enabled Risk-Based Sampling of Food Supply Chains in China."

Jin, Cangyu, Retsef Levi, Qiao Liang, Nicholas Renegar, Stacy Springs, Jiehong Zhou, and Weihua Zhou. Management Science Vol. 67, No. 5 (2021): 2985-2996. Supplemental Material. Download Paper.


AMR Database Construction Workflow

Project Researchers

David Byun

David Byun

Associate Director, Data Science, FSAS