Search

Supply chain traceability is key to reduce food safety risks, since it allows problems to be traced to their sources. Moreover, it allows regulatory agencies to understand where risk is introduced into the supply chain, and offers a major disincentive for upstream agricultural businesses engaging in economically motivated adulteration. This paper focuses on the aquatic supply chain in China, and seeks to understand the adoption of traceability both through an analytical model, and empirical analysis based on data collected through an extensive (largest ever) field survey of Chinese aquatic wholesale markets. The field survey includes 76 managers and 753 vendors, covering all aquatic wholesale markets in Zhejiang and Hunan provinces. The analytical and empirical results suggest that the adoption of traceability among wholesale market vendors is significantly associated with inspection intensity, their individual history of food safety problems, and their risk awareness. The effect of inspection intensity on traceability adoption is stronger in markets which are privately owned than in markets with state/collective ownership. The analysis offers insights into the current state of traceability in China. More importantly, it suggests several hypothesized factors that might affect the adoption of traceability and could be leveraged by regulatory organizations to improve it.

While many have advocated for widespread closure of Chinese wet and wholesale markets due to numerous zoonotic disease outbreaks (e.g., SARS) and food safety risks, this is impractical due to their central role in China’s food system. This first-of-its-kind work offers a data science enabled approach to identify market-level risks. Using a massive, self-constructed dataset of food safety tests, market-level adulteration risk scores are created through machine learning techniques. Analysis shows that provinces with more high-risk markets also have more human cases of zoonotic flu, and specific markets associated with zoonotic disease have higher risk scores. Furthermore, it is shown that high-risk markets have management deficiencies (e.g., illegal wild animal sales), potentially indicating that increased and integrated regulation targeting high-risk markets could mitigate these risks.

Numerous zoonotic outbreaks in China (e.g., COVID-19) have been associated with wholesale markets (WSMs) and wet markets (WMs). Furthermore, markets are associated with significant food-safety risks. Many have advocated for market closures. However, this is impractical due to markets’ central roles in China’s food supply chain. This first-of-its-kind work offers an alternative pragmatic approach, by showing correlation between these two types of risks. Leveraging a massive, self-constructed dataset of food safety tests, market-level food-safety risk scores are created through machine learning techniques. Analysis shows that provinces selling more animals through high-risk markets have more human cases of zoonotic flu. Additionally, specific markets associated with zoonotic disease are high-risk, and high-risk markets have more negative news stories related to management deficiencies (e.g., illegal wild animal sales). The hope is that this approach may offer a new way of understanding zoonotic disease risks, as well as informing regulatory approaches to reduce them.

Food adulteration poses a serious threat to public health. The U.S. Food and Drug Administration (FDA) has a major role in maintaining food safety in the U.S. through various activities including sampling of imported shipments and site inspections. However, resource constraints limit the number of these interventions, making risk-based allocation essential to ensure effectiveness. This paper aims to develops a data-driven, risk analytics approach to identify high-risk firms importing food (consignees). Leveraging supply chain analytics, based on shipment history and other data sources, network features are constructed to model risk, specifically predicting which consignees are likely to fail FDA site inspections. The approach is applied to consignees of shrimp, a product with frequent food safety problems. The main findings are that supply chain network complexity, and website network engagement, are predictive of risk. For instance, firms that import a more unusual portfolio of products, as measured through subgraph modularity on a product network graph, are more likely to fail site inspections. The results suggest that network-based risk analytics could significantly improve the effectiveness of regulatory activities related to food supply chains, and substantially increase the number of failed site inspections and imported shipment samples.

Food safety is a global problem with a large impact on human health and a high level of priority for governments. Supply chain traceability is key to enable food safety, since it allows problems to be traced to their source. Moreover, it allows regulatory agencies to understand where risk enters into the supply chain, and offers a major disincentive for upstream agricultural businesses engaging in economically motivated adulteration. Despite its importance, there has been little empirical research on what factors are associated with the adoption of traceability. This paper focuses on the aquatic supply chain in China, and seeks to understand the adoption of supply chain traceability empirically, through the most extensive field survey ever conducted at Chinese wholesale markets. The survey includes 76 managers and 753 vendors, covering all aquatic wholesale markets in Zhejiang and Hunan provinces. The results suggest that adoption of traceability among aquatic wholesale market vendors is significantly associated with inspection intensity, their individual history of food safety problems, and their risk awareness (specifically exposure to quality and safety issues as measured by their involvement in aquatic products associations and social media groups). In addition, a theoretical model of vendor behavior is proposed to explain these observations. The goal is that this paper will offer insights into the current state of traceability in China, as well as hypotheses for how the adoption of traceability can be improved.

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. This paper was accepted by Charles Corbett, operations management.