Testing at the Source: Analytics-Enabled Risk-Based Sampling of Food Supply Chains in China
30 Pages Posted: 28 Aug 2019 Last revised: 11 Jun 2020
Date Written: August 25, 2019
This paper illustrates how supply chain (SC) analytics could provide strategic and operational insights to inform risk-based allocation of regulatory resources in food SCs, for management of food safety and adulteration risks. The paper leverages a massive, self-constructed dataset of food safety tests conducted by China Food and Drug Administration (CFDA) organizations. The integrated and structured dataset 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 areas for improvement in the current CFDA testing allocation by SC location, that 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.
Keywords: Food Safety, Supply Chain, Agriculture, Big Data, Analytics, China
Suggested Citation: Suggested Citation