Introduction
Recent global developments such as pandemics, wars, and climate change have caused widespread disruptions in supply chains. For example, with the outbreak of COVID-19 in 2020, many countries closed their borders due to fears of food-borne transmission, which led to significant disruptions in supply chains and severe food shortages, especially in needy countries. Many studies in recent years have focused on analyzing past disasters and identifying and assessing risk factors, and a quantitative study  focused on identifying critical risk factors and resilience in the field of supply chain logistics from the perspective of quantification, prioritization, and ranking, and providing an integrated strategic model with a fuzzy approach to prevent and reduce the effects of disasters. This study aims to systematically review these studies to identify, evaluate, and prioritize the risk factors that influence the resilience of supply chain logistics.

Methods
This is a mixed-method (qualitative/quantitative) study. First, by reviewing the literature and based on the expert feedback, 16 primary risk factors were identified: “minimum fuel consumption”, “proper personnel training”, “price stability”, “inventory management and sustainable storage”, “resilience to demand fluctuations”, “past performance and credit”, “safety in operations and goods movement”, “impact of viral pandemics”, “natural disasters”, “cyberattacks”, “distortion in information sharing”, “mismanagement in supplier selection”, “poor planning”, “shortage of skilled personnel”, “transportation strikes” and “infrastructure failure”. The Pareto analysis and an integrated fuzzy approach, including MICMAC analysis and the total interpretive structural modeling (TISM), were used for analyzing the factors.

Results 
The Pareto analysis showed that 20% of factors (n=12) were responsible for 80% of the effects. Therefore, less important factors, including resilience to Demand Fluctuations, safety in operations and goods movement, past performance and credit, and infrastructure failure, were removed. 
Based on the MICMAC analysis, key factors were divided into four categories: Autonomous, Dependent, Independent, and Linkage. Factors including price stability, proper personnel training, and the impact of viral pandemics were in the autonomous category. These factors have low influence and dependency. Factors including mismanagement in supplier selection and natural disasters were in the dependent category. These factors were highly dependent and easily influenced, but had little impact on the system. Factors including transport strikes and shortages of skilled personnel were in the independent category. These factors were influential and could cause significant changes in the system, but are not affected by other factors. None of the factors were included in the linkage category. 
Based on the TISM method, the five most important risk factors were identified as:
Natural disasters: A vital factor that requires forecasting and planning;
Mismanagement in supplier selection: It can cause serious disruptions in supply;
Transport strikes: it can affect the delivery of goods;
Shortages of skilled personnel: It can reduce productivity;
Minimum fuel consumption: An environmental and economic issue
The validity of the final TISM model of resilience in supply chain logistics was assessed based on the opinions of 14 logistics experts using a 5-point Likert scale. This process helped identify the strengths and weaknesses of the system, provide optimal strategies, and scientifically strengthen the model, providing an effective tool for improving supply chain logistics.
According to this model, recommendations can be made to reduce risk factors in the field of supply chain logistics in order to manage response in emergencies as follows:
Improving supplier management: Develop detailed criteria for selecting suppliers and strengthening relationships with suppliers to reduce risk in times of crisis;
Diversity in transportation: Using diverse routes and transportation methods to reduce the effects of strikes;
Personnel training: Conducting training courses to increase the skills of personnel and improve crisis response;
Using advanced technologies: Utilizing supply chain management information systems and the Internet of Things (IoT).

Conclusion
This research showed that resilience in supply chain logistics is not just about responding to crises, but also includes anticipation and preparedness. Key factors such as natural disasters, mismanagement in supplier selection, and transport strikes should be managed as a priority. Building strong infrastructure, investing in advanced technologies and strengthening collaboration across different parts of the supply chain are essential to achieving greater resilience. Mismanagement in supply chain logistics can lead to serious problems in the supply of goods and services, especially during disasters. For this reason, organizations should establish strict criteria for evaluating and selecting suppliers to ensure that they are capable of managing crisis situations. This research provides a good basis for future studies and the development of practical solutions in the field of logistics.

Ethical Considerations

Compliance with ethical guidelines
All ethical principles were considered in this article. Informed consent was obtained from all participants.

Funding
This article was extracted from the master’s thesis of Hamed Asghari at the Department of Civil Engineering and Disaster Management, Faculty of Engineering and Passive Defense, Malek Ashtar University of Technology. This research did not receive any grant from the funding agencies in the public, commercial, or nonprofit sectors.

Authors' contributions
Validation, supervision, project management: Mohammad Eskandari and Mahdi Modiri; Analysis, investigation, resources, initial draft preparation, visualization, editing & review: Mohammad Eskandari and Hamed Asghari; Conceptualization and methodology: All authors.

Conflicts of interest
The authors declare no conflict of interest.

Acknowledgments
The authors would like to thank all participants for their cooperation in this research.