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

• 成果类型: Article
• 署名作者: Jin, Cangyu; Levi, Retsef; Liang, Qiao; Renegar, Nicholas; Springs, Stacy; Zhou, Jiehong; Zhou, Weihua
• 署名单位: Zhejiang University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Zhejiang University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3839
• 发表日期: 2021
• 页码: 2985-2996
• 关键词: Food safety; supply chain; big data; analytics

摘要

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 under-sampled. 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.