Key points are not available for this paper at this time.
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.
Zhao et al. (Sun,) studied this question.