Emergency regional food supply chain design and its labor demand forecasting model: application to COVID-19 pandemic disruption - Tian, Shuang; Mei, Yi (Frontiers in Sustainable Food Systems, 2023)
The COVID-19 pandemic has severely disrupted the global food supply chain through various interventions, such as city closures, traffic restrictions, and silent management, resulting in significant impacts on socio-economic and human life safety. Despite much discussion on how to restore the food supply chain, limited research has been conducted on the design of emergency regional food supply chains (ERFSC) and its labor demand forecasting, especially under government-mandated interventions such as city closures and silent management. The public is concerned about ensuring the supply of necessities for residents in affected areas. This paper applies emergency supply chain management theory to analyze the business processes of the ERFSC and proposes, for the first time, a multi-level ERFSC network tailored to different risk levels. Secondly, a food demand forecasting model and a mathematical model for stochastic labor demand planning are constructed based on the development trend of regional epidemics. Finally, 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 is a pilot study that will be further extended by the authors from geographical and policy perspectives.