The food industry has many points of vulnerability in its supply chain. It currently lacks integrated crisis management and response programs to understand the importance of decision-making during and in the aftermath of a bioterrorist attack on the food supply. Computer simulations have been used successfully in other industries as training and analysis tools. This paper describes an agent-based simulation for food defense training and analysis. Production information, consumption patterns, morbidity/mortality rates, recall costs and additional information were collected and provided to a data-driven simulation to anticipate the impact of decision-making on economic and public health during a terrorist attack. A case study is given with a representative exercise involving forty industry representatives who participated in a food defense simulation. Their decisions (recall and microbiological and toxicological testing) were derived from testing results, press releases, epidemiological data, and discussions with other industry and regulatory teams. Decisions made during the simulation resulted in over 76,000 illnesses, 45 deaths, and $132 million in recall costs. The no intervention, baseline scenario estimated to result in 91,000 illnesses and 54 deaths, indicating the improved public health outcomes resulting from players' decisions. Participants identified three key learning points: 1) communication between all groups is pertinent and challenging, 2) approaches to solve inherent food safety problems cannot be used to address food defense situations, and 3) human resource procedures regarding new hires and disgruntled employees should involve additional security measures. This computer simulation could be a valuable resource in food defense awareness and help educate companies and regulators about food defense risks and decision-making consequences. © 2014 IUPESM and Springer-Verlag.
CITATION STYLE
Chaturvedi, A., Armstrong, B., & Chaturvedi, R. (2014). Securing the food supply chain: Understanding complex interdependence through agent-based simulation. Health and Technology, 4(2), 159–169. https://doi.org/10.1007/s12553-014-0086-7
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