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

Principal Investigator(s): View help for Principal Investigator(s) Hongzhi Wang, Qingdao Agricultural University ; Li Lu, Qingdao Agricultural University ; Zhaoli Liu , Qingdao Agricultural University ; Yuxuan Sun, University College of Dublin

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Project Citation:

Wang, Hongzhi , Lu, Li, Liu , Zhaoli, and Sun, Yuxuan. 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. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-03-14. https://doi.org/10.3886/E222681V1

Project Description

Summary: 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.

Funding Sources: Humanities and Social Science Research Project of Ministry of Education of China (22YJA790057)

Scope of Project

Subject Terms: supply chain resilience; measurement ; prediction

Geographic Coverage: Typical agricultural product listed companies in China

Time Period(s): 2015 – 2027 (The 21st century)

Collection Date(s): 2023 – 2024 (The 21st century )

Universe: 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.

Data Type(s): survey data

Collection Notes: 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.

Methodology

Response Rate: Improved EW-TOPSIS and GM (1,1)-Markov models.

Sampling:

Typical listed agricultural product companies in China（NHL, SQF, DBN, YILI, HTGF）.

Data Source: CSMAR database and the annual reports of the agricultural product listed companies.

Collection Mode(s): other; web scraping; web-based survey

Scales: Comprehensive scales for supply - chain resilience measurement and prediction.

Weights: Weights distribution in the combined model of improved EW - TOPSIS and GM(1,1) - Markov.

Unit(s) of Observation: Agricultural supply chain enterprises as units of observation.

Geographic Unit: Agricultural production areas.

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