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JNFS 2025, 10(3): 338-340 |
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Marzban A, Sadeghi-Nodoushan F. The Role of Artificial Intelligence in Food Supply Chain Management during Natural Disasters. JNFS 2025; 10 (3) :338-340
URL: http://jnfs.ssu.ac.ir/article-1-1252-en.html
URL: http://jnfs.ssu.ac.ir/article-1-1252-en.html
1Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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The Role of Artificial Intelligence in Food Supply Chain Management during Natural Disasters
Ameneh Marzban; PhD*1 & Fateme Sadeghi-Nodoshan; MSc2
1Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; 2 Department of Biochemistry, School of Medicine, Shahid Sadoughi University of Medical Sciences. Yazd, Iran.
Ameneh Marzban; PhD*1 & Fateme Sadeghi-Nodoshan; MSc2
1Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; 2 Department of Biochemistry, School of Medicine, Shahid Sadoughi University of Medical Sciences. Yazd, Iran.
| ARTICLE INFO | ||
| EDITORIAL ARTICLE | Corresponding author: amenemarzban@yahoo.com Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. Postal code: 1449614535 Tel: +98 9172458896 |
|
| Article history: Received: 8 Feb 2025 Revised: 15 Mar 2025 Accepted: 21 Mar 2025 |
Natural disasters such as hurricanes, earthquakes, floods, and wildfires pose significant challenges to food supply chains, often disrupting the production, transportation, and distribution of essential food items (Azizsafaei et al., 2021, Emami et al., 2024). Ensuring a resilient food supply chain during such crises is crucial for preventing food shortages and ensuring the well-being of affected populations. In recent years, artificial intelligence (AI) has emerged as a transformative tool in enhancing the efficiency and resilience of food supply chains during natural disasters (Effah et al., 2023).
AI-Driven predictive analytics
One of the most critical applications of AI in food supply chain management is predictive analytics. AI algorithms can analyze historical data, weather patterns, and real-time information to forecast the occurrence and impact of natural disasters (Albahri et al., 2024). For instance, previous studies have demonstrated how AI predicted disruptions caused by hurricanes in coastal regions, which allowed for better inventory pre-positioning. However, such approaches faced challenges in accuracy when data quality was insufficient (Albahri et al., 2024, Azizsafaei et al., 2021). By predicting potential disruptions, AI enables stakeholders to implement proactive measures, such as pre-positioning food supplies in strategic locations, optimizing inventory levels, and adjusting transportation routes to avoid affected areas. This predictive capability helps to mitigate the impact of disasters and ensure a more reliable food supply (Albahri et al., 2024).
Optimizing logistics and transportation
AI-powered systems can optimize logistics and transportation by analyzing real-time data on road conditions, traffic, and weather. During a natural disaster, AI can recommend alternative routes, prioritize the delivery of critical supplies, and allocate resources more efficiently. For example, the application of AI in flood-prone areas has proven effective in minimizing delays by suggesting real-time alternative routes. However, challenges were observed in coordinating between multiple systems when data integration was inconsistent (Trollman, 2024). Additionally, AI-driven automation can streamline warehouse operations, ensuring that food supplies are quickly and accurately dispatched to affected areas. These optimizations reduce delays, minimize waste, and ensure that food reaches those in need promptly (Abid et al., 2021, Trollman, 2024).
Enhancing inventory management
AI can also improve inventory management in food supply chain by accurately predicting demand and managing stock levels (Effah et al., 2023). Machine learning algorithms can analyze consumption patterns, seasonal variations, and disaster-specific factors to forecast demand for various food items. Past implementations in earthquake-prone regions revealed both successes in minimizing stockouts and challenges in balancing diverse product demands (Abid et al., 2021). This information allows suppliers to maintain optimal inventory levels, reducing the risk of shortages or overstocking. AI-driven inventory management ensures that food supplies are available and accessible during emergencies (Azizsafaei et al., 2021).
Supporting decision-making
AI can assist decision-makers in developing and implementing effective disaster response strategies. By integrating data from multiple sources, including social media, satellite imagery, and sensor networks, AI can provide a comprehensive overview of the disaster's impact on the food supply chain. For example, AI systems have proven successful in integrating satellite data during wildfires, helping to allocate resources more effectively. However, they struggled to capture real-time data due to connectivity issues in affected regions (Albahri et al., 2024). Decision-makers can use these insights to coordinate relief efforts, allocate resources, and monitor the effectiveness of response measures. AI-driven decision support systems enhance situational awareness and enable more informed, timely, and effective interventions (Albahri et al., 2024, Effah et al., 2023, Trollman, 2024).
Challenges and future directions
Despite the significant benefits of AI in food supply chain management during natural disasters, several challenges should be considered. Data privacy and security concerns, the need for high-quality and real-time data, and the integration of AI systems with existing infrastructure are critical issues that need to be addressed. Furthermore, the development of AI solutions tailored to the specific needs of different regions and types of disasters is essential for maximizing their effectiveness (Abid et al., 2021, Albahri et al., 2024, Azizsafaei et al., 2021). Additionally, a thorough review of existing literature is necessary to identify overlooked applications of AI in various disaster scenarios. This can provide a more holistic understanding of AI's potential and limitations (Trollman, 2024). Future research should focus on enhancing the robustness and adaptability of AI algorithms, improving data sharing and collaboration among stakeholders, and addressing ethical considerations related to AI deployment in disaster scenarios (Albahri et al., 2024). By addressing these challenges, AI can play an even more significant role in ensuring a resilient and efficient food supply chain during natural disasters (Abid et al., 2021, Albahri et al., 2024).
Conclusion
Artificial intelligence offers transformative potential for enhancing food supply chain management during natural disasters. Through predictive analytics, optimized logistics, improved inventory management, and decision support, AI can mitigate the impact of disasters and ensure the timely delivery of essential food supplies. As technology continues to advance, the integration of AI into food supply chain management will be critical in building more resilient and responsive systems capable of withstanding the challenges posed by natural disasters. By leveraging AI, individuals can enhance the resilience and efficiency of food supply chains, ensuring that communities affected by natural disasters have access to essential food supplies when they need it the most.
References
Abid SK, et al. 2021. Toward an integrated disaster management approach: how artificial intelligence can boost disaster management. Sustainability. 13 (22): 12560.
Albahri A, et al. 2024. A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and electrical engineering. 118: 109409.
Azizsafaei M, Sarwar D, Fassam L, Khandan R & Hosseinian-Far A 2021. A critical overview of food supply chain risk management. In Cybersecurity, Privacy and Freedom Protection in the Connected World: Proceedings of the 13th International Conference on Global Security, Safety and Sustainability, London, January 2021, pp. 413-429. Springer.
Effah D, Bai C, Asante WA & Quayson M 2023. The role of artificial intelligence in coping with extreme weather-induced cocoa supply chain risks. IEEE Transactions on engineering management. 71: 9854-9875.
Emami P, et al. 2024. Knowledge and attitude of Kurdistan University of Medical Sciences students towards food hygiene in disasters. Journal of nutrition,fasting and health. 12 (4 ): 228-234.
Trollman H 2024. Feature extraction for artificial intelligence enabled food supply chain failure mode prediction. Discover food. 4 (1): 22.
AI-Driven predictive analytics
One of the most critical applications of AI in food supply chain management is predictive analytics. AI algorithms can analyze historical data, weather patterns, and real-time information to forecast the occurrence and impact of natural disasters (Albahri et al., 2024). For instance, previous studies have demonstrated how AI predicted disruptions caused by hurricanes in coastal regions, which allowed for better inventory pre-positioning. However, such approaches faced challenges in accuracy when data quality was insufficient (Albahri et al., 2024, Azizsafaei et al., 2021). By predicting potential disruptions, AI enables stakeholders to implement proactive measures, such as pre-positioning food supplies in strategic locations, optimizing inventory levels, and adjusting transportation routes to avoid affected areas. This predictive capability helps to mitigate the impact of disasters and ensure a more reliable food supply (Albahri et al., 2024).
Optimizing logistics and transportation
AI-powered systems can optimize logistics and transportation by analyzing real-time data on road conditions, traffic, and weather. During a natural disaster, AI can recommend alternative routes, prioritize the delivery of critical supplies, and allocate resources more efficiently. For example, the application of AI in flood-prone areas has proven effective in minimizing delays by suggesting real-time alternative routes. However, challenges were observed in coordinating between multiple systems when data integration was inconsistent (Trollman, 2024). Additionally, AI-driven automation can streamline warehouse operations, ensuring that food supplies are quickly and accurately dispatched to affected areas. These optimizations reduce delays, minimize waste, and ensure that food reaches those in need promptly (Abid et al., 2021, Trollman, 2024).
Enhancing inventory management
AI can also improve inventory management in food supply chain by accurately predicting demand and managing stock levels (Effah et al., 2023). Machine learning algorithms can analyze consumption patterns, seasonal variations, and disaster-specific factors to forecast demand for various food items. Past implementations in earthquake-prone regions revealed both successes in minimizing stockouts and challenges in balancing diverse product demands (Abid et al., 2021). This information allows suppliers to maintain optimal inventory levels, reducing the risk of shortages or overstocking. AI-driven inventory management ensures that food supplies are available and accessible during emergencies (Azizsafaei et al., 2021).
Supporting decision-making
AI can assist decision-makers in developing and implementing effective disaster response strategies. By integrating data from multiple sources, including social media, satellite imagery, and sensor networks, AI can provide a comprehensive overview of the disaster's impact on the food supply chain. For example, AI systems have proven successful in integrating satellite data during wildfires, helping to allocate resources more effectively. However, they struggled to capture real-time data due to connectivity issues in affected regions (Albahri et al., 2024). Decision-makers can use these insights to coordinate relief efforts, allocate resources, and monitor the effectiveness of response measures. AI-driven decision support systems enhance situational awareness and enable more informed, timely, and effective interventions (Albahri et al., 2024, Effah et al., 2023, Trollman, 2024).
Challenges and future directions
Despite the significant benefits of AI in food supply chain management during natural disasters, several challenges should be considered. Data privacy and security concerns, the need for high-quality and real-time data, and the integration of AI systems with existing infrastructure are critical issues that need to be addressed. Furthermore, the development of AI solutions tailored to the specific needs of different regions and types of disasters is essential for maximizing their effectiveness (Abid et al., 2021, Albahri et al., 2024, Azizsafaei et al., 2021). Additionally, a thorough review of existing literature is necessary to identify overlooked applications of AI in various disaster scenarios. This can provide a more holistic understanding of AI's potential and limitations (Trollman, 2024). Future research should focus on enhancing the robustness and adaptability of AI algorithms, improving data sharing and collaboration among stakeholders, and addressing ethical considerations related to AI deployment in disaster scenarios (Albahri et al., 2024). By addressing these challenges, AI can play an even more significant role in ensuring a resilient and efficient food supply chain during natural disasters (Abid et al., 2021, Albahri et al., 2024).
Conclusion
Artificial intelligence offers transformative potential for enhancing food supply chain management during natural disasters. Through predictive analytics, optimized logistics, improved inventory management, and decision support, AI can mitigate the impact of disasters and ensure the timely delivery of essential food supplies. As technology continues to advance, the integration of AI into food supply chain management will be critical in building more resilient and responsive systems capable of withstanding the challenges posed by natural disasters. By leveraging AI, individuals can enhance the resilience and efficiency of food supply chains, ensuring that communities affected by natural disasters have access to essential food supplies when they need it the most.
References
Abid SK, et al. 2021. Toward an integrated disaster management approach: how artificial intelligence can boost disaster management. Sustainability. 13 (22): 12560.
Albahri A, et al. 2024. A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and electrical engineering. 118: 109409.
Azizsafaei M, Sarwar D, Fassam L, Khandan R & Hosseinian-Far A 2021. A critical overview of food supply chain risk management. In Cybersecurity, Privacy and Freedom Protection in the Connected World: Proceedings of the 13th International Conference on Global Security, Safety and Sustainability, London, January 2021, pp. 413-429. Springer.
Effah D, Bai C, Asante WA & Quayson M 2023. The role of artificial intelligence in coping with extreme weather-induced cocoa supply chain risks. IEEE Transactions on engineering management. 71: 9854-9875.
Emami P, et al. 2024. Knowledge and attitude of Kurdistan University of Medical Sciences students towards food hygiene in disasters. Journal of nutrition,fasting and health. 12 (4 ): 228-234.
Trollman H 2024. Feature extraction for artificial intelligence enabled food supply chain failure mode prediction. Discover food. 4 (1): 22.
Type of article: letter to the editor |
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Received: 2025/02/8 | Published: 2025/08/28 | ePublished: 2025/08/28
Received: 2025/02/8 | Published: 2025/08/28 | ePublished: 2025/08/28
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