A Markov chain model for quantifying consumer risk in food supply chains

According to the Centers for Disease Control and Prevention, approximately 48 million people experience foodborne illnesses per year. The majority of the illnesses are attributed to the presence of bacteria in food products. However, there is some concern about the likelihood of food contamination resulting from intentional acts of sabotage. This research presents a stochastic model to quantify food supply chain vulnerability in terms of the number of people who become ill from consuming a contaminated food product. We specifically use a discrete time, discrete state Markov chain model with rewards and estimate consumer illness by product and distribution channel. The results of our computational study show the relationship between purchasing behavior, product shelf life, and consumer risk. We propose a classification scheme that can be used to categorize the level of vulnerability among different food distribution channels. We also show the impact of purchasing behavior on the speed with which the products are sold at each distribution channel. The proposed model has the potential to provide insight into timely interventions and influence how intervention policies would need to be tailored to each distribution channel in the event that a chemical contamination occurs.