Study on measurement and prediction of agricultural product supply chain resilience based on improved EW-TOPSIS and GM (1,1)-Markov models under public emergencies

The
COVID-19 pandemic, African Swine Fever, and other major public health
emergencies have affected the agricultural product supply chain in recent
years. It appeared in various chain breakdown and blocking issues, in which the
resilience was drastically reduced and food security and social stability were
greatly disrupted. This dissertation adopted an improved EW-TOPSIS method to
evaluate resilience and determined the significance of influence factors of the
agricultural product supply chain in China, showing that adjustment capability was
closely connected to resilience. Through the empirical research on top listed
enterprises（NHL, SQF, DBN, YILI, HTGF）,
it was found that the resilience of the industry was
generally lower in 2020-2021 than in 2015-2019, and recovered and peaked in
2022. An improved Markov-modified GM (1,1) forecasting method was adopted to
construct a resilience-predicting model. It was found that there would be a
decline of resilience in 2024-2025, while a general growth with fluctuations trend
was shown during the thirteen years before and after the breakout of the
COVID-19 pandemic. In addition, this dissertation uses independent samples T-test
and Solomon sensitivity analysis methods to verify the feasibility of the
empirical results. Accordant enhancement mechanisms were proposed based on the
empirical findings and results, which were expected to improve the
risk-resistant capability of the domestic agricultural product supply chain
under potential public emergency scenarios in the future. Our research findings
can serve as a valuable reference for scientific decision-making and policy
formulation to encourage the establishment of a robust agricultural product
supply chain resilience system.