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Testing at the Source: Analytics-Enabled Risk-Based Sampling of Food Supply Chains in China

Author

Listed:
  • Cangyu Jin

    (China Academy for Rural Development, School of Public Affairs, Zhejiang University, Hangzhou 310058, China)

  • Retsef Levi

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Qiao Liang

    (China Academy for Rural Development, School of Public Affairs, Zhejiang University, Hangzhou 310058, China)

  • Nicholas Renegar

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Stacy Springs

    (Center for Biomedical Innovation, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Jiehong Zhou

    (China Academy for Rural Development, School of Public Affairs, Zhejiang University, Hangzhou 310058, China)

  • Weihua Zhou

    (School of Management, Zhejiang University, Hangzhou 310058, China)

Abstract

This paper illustrates how supply chain (SC) analytics could provide strategic and operational insights to evaluate the risk-based allocation of regulatory resources in food SCs, for management of food safety and adulteration risks. This paper leverages data on 89,970 tests of aquatic products extracted from a self-constructed data set of 2.6 million food safety tests conducted by the China Food and Drug Administration (CFDA) organizations. The integrated and structured data set is used to conduct innovative analysis that identifies the sources of adulteration risks in China’s food SCs and contrasts them with the current test resource allocations of the CFDA. The analysis highlights multiple strategic insights. Particularly, it suggests potential gaps in the current CFDA testing allocation by SC location, which is heavily focused on retail and supermarkets. Instead, the analysis indicates that high-risk parts of the SC, such as wholesale and wet markets, are undersampled. Additionally, the paper highlights the impact that SC analytics could have on policy-level operational decision making to regulate food SCs and manage food safety. The hope is that the paper will stimulate the interest of academics with expertise in these areas to conduct more work in this important application domain.

Suggested Citation

  • Cangyu Jin & Retsef Levi & Qiao Liang & Nicholas Renegar & Stacy Springs & Jiehong Zhou & Weihua Zhou, 2021. "Testing at the Source: Analytics-Enabled Risk-Based Sampling of Food Supply Chains in China," Management Science, INFORMS, vol. 67(5), pages 2985-2996, May.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:5:p:2985-2996
    DOI: 10.1287/mnsc.2020.3839
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    References listed on IDEAS

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    1. Lowder, Sarah K. & Skoet, Jakob & Raney, Terri, 2016. "The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide," World Development, Elsevier, vol. 87(C), pages 16-29.
    2. Michaela Fox & Mike Mitchell & Moira Dean & Christopher Elliott & Katrina Campbell, 2018. "The seafood supply chain from a fraudulent perspective," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(4), pages 939-963, August.
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    Cited by:

    1. Dan, Bin & Lei, Ting & Zhang, Xumei & Liu, Molin & Ma, Songxuan, 2023. "Modeling of the subsidy policy in fresh produce wholesale markets under yield uncertainty," Economic Modelling, Elsevier, vol. 126(C).
    2. Lin, Wen & Ma, Baojie & Liang, Jiangyuan & Jin, Shaosheng, 2024. "Price response to government disclosure of food safety information in developing markets," Food Policy, Elsevier, vol. 123(C).

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