Advanced Search
DSpace@MIT

A data-driven approach to mitigate risk in global food supply chains

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Tauhid Zaman and Retsef Levi. en_US
dc.contributor.author Anoun, Amine en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US
dc.date.accessioned 2017-10-30T15:30:49Z
dc.date.available 2017-10-30T15:30:49Z
dc.date.copyright 2017 en_US
dc.date.issued 2017 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/112084
dc.description Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (pages 141-143). en_US
dc.description.abstract Economically motivated adulteration of imported food poses a serious threat to public health, and has contributed to several poisoning incidents in the past few years in the U.S. [1]. Prevention is achieved by sampling food shipments coming to the U.S. However, the sampling resources are limited: all shipments are electronically sampled [2], but only a small percentage of shipments are physically inspected. In an effort to mitigate risk in shipping supply chains, we develop a data-driven approach to identify risky shippers and manufacturers exporting food to the U.S., as well as U.S. based consignees and importers receiving imported products. We focus our analysis on honey and shrimp, two products that are routinely imported and frequently adulterated. We obtain over 62,000 bills of lading of honey entering the U.S. between 2006 and 2015 from public sources, and over a million shipment records of shrimp entering the U.S. between 2007 and 2015 from the Food and Drugs Administration (FDA). We analyze these data to identify common patterns between high risk shippers, manufacturers, U.S. consignees and importers, and use them to determine structural features of shipping supply chains that correlate with risk of adulteration. In our analysis of shrimp manufacturers, we distinguish two types of adulteration: intentional (driven by economic motivation) and unintentional (due to negligence or poor sanitary conditions). We use a Bayesian approach to model both the sampling or inspection procedure of the FDA, and the risk of adulteration. Our model is able to predict which companies are at risk of committing adulteration with high out-of-sample accuracy. We find that both geographical features (e.g., travel route, country of origin and transnational paths) and network features (e.g., number of partners, weight dispersion and diversity of the product portfolio) are significant and predictive of suspicious behavior. These outcomes can inform various decisions faced by the FDA in their sampling policy for honey and shrimp shipments, and their site inspection policy for consignees and importers. This work can also extend to other commodities with similar mechanisms, and provides a general framework to better detect food safety failures and mitigate risk in food supply chains. en_US
dc.description.statementofresponsibility by Amine Anoun. en_US
dc.format.extent 143 pages en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Operations Research Center. en_US
dc.title A data-driven approach to mitigate risk in global food supply chains en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 1006886026 en_US


Files in this item

Name Size Format Description
1006886026-MIT.pdf 17.22Mb PDF Full printable version

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage