Chinese Puzzle: Reducing the Risk of Food Contamination

Posted on March 31, 2016 • 0 Comments

Some food products emanating from China are prone to adulteration.

China is the third-largest source of food imported into the U.S., but is responsible for the highest number of imported food safety alerts received by the U.S. Federal Drug Administration (FDA). Only a tiny fraction of the shipments moving through these highly fragmented supply chains are inspected, so what risk management methods can be developed to improve safety margins?

Retsef Levi, Professor of Operations Management, MIT Sloan School of Management, explained some possibilities at MIT CTL’s Crossroads 2016 conference on March 23, 2016.

A team of experts from MIT’s Sloan School of Management, the Center for Biomedical Innovation, and the Computer Science and Artificial Intelligence Lab, is doing ground-breaking research on global food supply chains. At the Crossroads conference, Levi focused on one of the questions that the team is addressing: How does the structure of food supply chains impact the risk of economically motivated food adulteration in China?

The level of risk is disturbingly high. Moreover, the Food Safety Modernization Act, which was signed into law by President Obama in 2011, shifts responsibility and accountability for mitigating these risks to industry. Levi described a few examples of cases were dangerous contaminants were found in food produced in China, including tainted baby milk powder and the use of harmful oil in baked food products.

The complexity of China’s food supply chains is one of the main risk drivers. For example, a common model is Dragon Head Farming, were a company contracts with many thousands of farmers to supply foods such as dairy products, poultry, seafood and vegetables. Product consolidators often form another link in the chain between the Dragon Head company and its vast supplier base.

These thousands of suppliers operate relatively unmonitored, and are subject to tight output targets set by the Dragon Head company, explained Levi. As a result, they have a strong incentive to adulterate product in order to maintain production levels. For example, the research team mapped 95 different antibiotics, chemotherapy drugs, and Chinese medicines that are used to ward off avian flu in chickens. As Levi pointed out, in addition to the economic pressures on these suppliers, “there is no support, no education, and no mentoring.”

After analyzing these operations, the team has identified the level of supply chain dispersion as a critical driver of risk. Sourcing from a highly distributed supplier network increases the risk of economic adulteration. Regions with weaker regulations are also prone to this type of risk. Other drivers include market exposure to major price differences, an excess of unused production capacity, and the availability of contaminants that are difficult to detect. Food products that use ingredients present in industrial applications – oils that come in food and industrial grades, for example – also exhibit a higher risk of adulteration.

Where the range of hard-to-detect contaminants is extremely broad, “trying to develop a robust testing approach is almost impossible,” Levi said. The problem is compounded by regulations driven by legal and scientific experts who assume that rigorous testing is effective for food imports. This may be the case in the pharmaceutical business, but not in the convoluted food supply chains that the team is studying, he said.

If testing is not the answer, how can the threat of food contamination be reduced? The team’s approach is to use supply chain information and predictive modeling to anticipate where the threats are likely to emerge. Efforts to address the underlying problems can then be targeted at these high-risk areas.

Using FDA information on alerts, the team has studied the practices of bad versus good suppliers. They have also analyzed shipment information and mapped the attributes and supply chain patterns associated with high-risk shippers and manufacturers.

For example, between 2006 and 2015 there were almost 63,000 shipments of honey from China to the U.S. About 6,200 shippers were involved, and where adulteration was detected, the main contaminant was antibiotics.

The team has developed a model that predicts where the risk of adulteration is high. The model also accounts for shippers and manufacturers that do not show up on the FDA’s alert system. Some 40,000 shipments represented a significant risk between 2007 and 2014, according to the model. Validation tests confirmed that the model performs well.

A key takeaway from the research is that testing food imports from China is not enough; companies and government agencies also need a deep understanding of the supply chain dynamics that drive risk as well as more effective monitoring systems.  Supply chain analytics can be deployed to help prioritize risk at the product, firm, and shipment levels, and to support a more systematic approach to risk management.

The next step is to launch a pilot to test these risk management methods developed by the team.