Modeling Shock Propagation on Supply Chain Networks: A Stochastic Logistic-Type Approach

Abstract

Supply Chains have been more and more suffering from unexpected industrial, natural events, or epidemics that might disrupt the normal flow of materials, information, and money. The recent pandemic triggered by the outbreak of the new COVID-19 has pointed out the increasing vulnerability of supply chain networks, prompting companies (and governments) to implement specific policies and actions to control and reduce the spread of the disease across the network, and to cope with exogenous shocks. In this paper, we present a stochastic Susceptible-Infected-Susceptible (SIS) framework to model the spread of new epidemics across different distribution networks and determine social distancing/treatment policies in the case of local and global networks. We highlight the relevance of adaptability and flexibility of decisions in unstable and unpredictable scenarios.