Risk analysis of the agri-food supply chain: a multi-method approach
Risk analysis of the agri-food supply chain: a multi-method approach
Agri-food supply chains (AFSCs) are becoming more complex in structure, and thus more susceptible to different vulnerabilities and risks. Therefore, to enhance performance, we need to manage the risks in AFSCs effectively and efficiently. This study analyses various AFSC risks using a multi-method approach, including thematic analysis, total interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis. Based on the empirical data collected from experienced AFSC practitioners and following thematic analysis, eight categories of risk and 16 risk factors were identified as important. Furthermore, the interrelationships among the identified risks were built using TISM. Finally, the identified risks were classified into various categories according to their dependence and driving power using fuzzy MICMAC analysis. The research results indicate that the weather-related and political risks have the highest driving power and are located at the lowest level in the TISM hierarchy. These risks have a high tendency to disturb the whole flow of AFSC and so should be managed effectively. This study advances existing literature on identifying risk factors, defining interrelations between different AFSC risks, and determining the key risks. The risk analysis results can help AFSC practitioners in AFSC to identify, categorise and analyse the risks.
agri-food supply chain, fuzzy MICMAC, risk identification, thematic analysis, total interpretive structural modelling
4851-4876
Zhao, Guoqing
6fa035d1-56b1-46ea-a8fd-f91d7010674e
Liu, Shaofeng
9e435733-c5c6-49f8-b2b8-d0b44c24fc96
Lopez, Carmen
f11f88d5-36c4-4beb-a4c5-ceb16a6df19c
Chen, Huilan
b5ead873-3e2d-4b53-8fed-8a4db18410f0
Lu, Haiyan
bca844c5-6521-4a1e-af78-ac374a5e34e4
Kumar Mangla, Sachin
53baf599-e8f0-4504-814c-7e51ebbdd5ce
Elgueta, Sebastian
b8d2d422-0c6d-47a0-a953-1a46873ed0d0
17 August 2020
Zhao, Guoqing
6fa035d1-56b1-46ea-a8fd-f91d7010674e
Liu, Shaofeng
9e435733-c5c6-49f8-b2b8-d0b44c24fc96
Lopez, Carmen
f11f88d5-36c4-4beb-a4c5-ceb16a6df19c
Chen, Huilan
b5ead873-3e2d-4b53-8fed-8a4db18410f0
Lu, Haiyan
bca844c5-6521-4a1e-af78-ac374a5e34e4
Kumar Mangla, Sachin
53baf599-e8f0-4504-814c-7e51ebbdd5ce
Elgueta, Sebastian
b8d2d422-0c6d-47a0-a953-1a46873ed0d0
Zhao, Guoqing, Liu, Shaofeng, Lopez, Carmen, Chen, Huilan, Lu, Haiyan, Kumar Mangla, Sachin and Elgueta, Sebastian
(2020)
Risk analysis of the agri-food supply chain: a multi-method approach.
International Journal of Production Research, 58 (16), .
(doi:10.1080/00207543.2020.1725684).
Abstract
Agri-food supply chains (AFSCs) are becoming more complex in structure, and thus more susceptible to different vulnerabilities and risks. Therefore, to enhance performance, we need to manage the risks in AFSCs effectively and efficiently. This study analyses various AFSC risks using a multi-method approach, including thematic analysis, total interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis. Based on the empirical data collected from experienced AFSC practitioners and following thematic analysis, eight categories of risk and 16 risk factors were identified as important. Furthermore, the interrelationships among the identified risks were built using TISM. Finally, the identified risks were classified into various categories according to their dependence and driving power using fuzzy MICMAC analysis. The research results indicate that the weather-related and political risks have the highest driving power and are located at the lowest level in the TISM hierarchy. These risks have a high tendency to disturb the whole flow of AFSC and so should be managed effectively. This study advances existing literature on identifying risk factors, defining interrelations between different AFSC risks, and determining the key risks. The risk analysis results can help AFSC practitioners in AFSC to identify, categorise and analyse the risks.
Text
IJPR. 2020. Guoqing et al._Open Access
- Accepted Manuscript
More information
Accepted/In Press date: 27 January 2020
e-pub ahead of print date: 16 February 2020
Published date: 17 August 2020
Additional Information:
Funding Information:
The work reported in this paper has benefited from the RUC-APS project funded by European Commission under the Horizon 2020 programme [H2020-MSCA-RISE Award No. 691249]; Fondo Nacional de Desarrollo Cientifico y Technologico [grant number Fondecyt Iniciacion 11190872].
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Keywords:
agri-food supply chain, fuzzy MICMAC, risk identification, thematic analysis, total interpretive structural modelling
Identifiers
Local EPrints ID: 438139
URI: http://eprints.soton.ac.uk/id/eprint/438139
ISSN: 0020-7543
PURE UUID: ee357f9f-86c2-49ad-9e77-d9a18fc65db7
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Date deposited: 03 Mar 2020 17:30
Last modified: 17 Mar 2024 05:21
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Contributors
Author:
Guoqing Zhao
Author:
Shaofeng Liu
Author:
Carmen Lopez
Author:
Huilan Chen
Author:
Haiyan Lu
Author:
Sachin Kumar Mangla
Author:
Sebastian Elgueta
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