Evaluating Supply Chain Resilience During Pandemic Using Agent-based Simulation

Abstract

Recent pandemics have highlighted vulnerabilities in our global economic systems, especially supply chains. Possible future pandemic raises a dilemma for businesses owners between short-term profitability and long-term supply chain resilience planning. In this study, we propose a novel agent-based simulation model integrating extended Susceptible-Infected-Recovered (SIR) epidemiological model and supply and demand economic model to evaluate supply chain resilience strategies during pandemics. Using this model, we explore a range of supply chain resilience strategies under pandemic scenarios using in silico experiments. We find that a balanced approach to supply chain resilience performs better in both pandemic and non-pandemic times compared to extreme strategies, highlighting the importance of preparedness in the form of a better supply chain resilience. However, our analysis shows that the exact supply chain resilience strategy is hard to obtain for each firm and is relatively sensitive to the exact profile of the pandemic and economic state at the beginning of the pandemic. As such, we used a machine learning model that uses the agent-based simulation to estimate a near-optimal supply chain resilience strategy for a firm. The proposed model offers insights for policymakers and businesses to enhance supply chain resilience in the face of future pandemics, contributing to understanding the trade-offs between short-term gains and long-term sustainability in supply chain management before and during pandemics.

Figures (8)

• Figure 1: A schematic view of the proposed epidemiological-economic model with supply chains.
• Figure 2: A simple example of three supply chain strategies obtained for different objectives of a firm from the perspective of a store (firm) for a case of only two locations and four firms where the consumer population is present only in the same location as the store. The solid-green arrows indicate the store firm established these supply chains while dashed-red arrows indicate the store firm do not established these supply chains.
• Figure 3: A schematic view of the epidemiological model which is divided into five states - susceptible, exposed, infected, recovered, and dead.
• Figure 4: Analysis of four supply chain resilience strategies as the course of a one-year-long pandemic. The results are shown as the mean $\pm$ standard deviation of $n=1000$ simulations.
• Figure 5: A sensitivity analysis of the model's main epidemiologically-related parameters. The weight of profit rather than supply chain resilience parameter ($\omega_1$) is obtained as an average $\pm$ standard deviation of $n=100$ simulations with the rest of the parameters are sampled according to Table \ref{['table:params']}.