DOI: 10.3886/e222681
title: 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

description: 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.
description: Weights distribution in the combined model of improved EW - TOPSIS and GM(1,1) - Markov.
description: Response Rates: Improved EW-TOPSIS
and GM (1,1)-Markov models.
description: Presence of Common Scales: Comprehensive scales for supply - chain resilience measurement and prediction.
description: Typical listed agricultural product companies in China（NHL, SQF, DBN, YILI, HTGF）.
description: This study focuses on the measurement and prediction of the resilience of the agricultural product supply chain under public emergencies. Considering the instability and complexity of the supply chain caused by public emergencies, an improved EW - TOPSIS model is proposed to measure the resilience of the agricultural product supply chain. The entropy weight method in the traditional TOPSIS model is improved to more accurately reflect the importance of various indicators in evaluating supply chain resilience. At the same time, the GM (1,1) - Markov model is introduced for prediction. The GM (1,1) model is used to make an initial prediction of the supply chain resilience trend, and then the Markov chain is used to correct the prediction results to improve the accuracy and reliability of the prediction. Through empirical research, this study provides a scientific basis for decision - makers to enhance the resilience of the agricultural product supply chain and effectively deal with public emergencies, which is of great significance for ensuring the stable operation of the agricultural product supply chain and the supply of agricultural products. Smallest Geographic Unit: Agricultural production areas.
description: other; web scraping; web-based survey; The study “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” addresses the challenges posed by public emergencies to the stable operation of the agricultural product supply chain. Its significance lies in ensuring food security and stabilizing market supply. It employs the improved EW - TOPSIS model, which modifies the traditional entropy weight method for determining index weights, and measures the supply chain resilience by collecting data on indicators such as supply stability. The GM (1,1) - Markov model is used, where the GM (1,1) model makes an initial prediction of the supply chain resilience trend, and then the Markov chain is used for correction to improve prediction accuracy. The research methods include data collection, index system construction, model application, and result analysis. The study contributes theoretically by enriching the supply chain risk management system and practically by helping enterprises and governments understand supply chain risks and formulate response measures in advance. However, it has limitations in data and the accuracy of index quantification. Future research can expand data sources and combine advanced technologies for in - depth exploration.