Emergency regional food supply chain design and its labor demand forecasting model: application to COVID-19 pandemic disruption

Abstract

The COVID-19 pandemic has severely disrupted the global food supply chain through various interventions, such as city closures, traffic restrictions, and silent management. Limited research has been conducted on the design of emergency regional food supply chains (ERFSC) and its labor demand forecasting under government-mandated interventions. This paper applies emergency supply chain management theory to analyze the business processes of the ERFSC and proposes a multi-level ERFSC network tailored to different risk levels. Additionally, a food demand forecasting model and a mathematical model for stochastic labor demand planning are constructed based on the development trend of regional epidemics. An empirical analysis is presented using Huaguoyuan, Guiyang, China, as an example. The results demonstrate that the proposed ERFSC design and its labor demand forecasting model can achieve secure supply and accurate distribution of necessities in regions with different risk levels. These findings have important policy and research implications for the government and practitioners to take interventions and actions to ensure food supply for residents in the context of city closure or silent management. This study serves as a pilot study that will be further extended by the authors from geographical and policy perspectives.