This study aims to analyze the impact of severe weather on supply chains in Jazan region, Saudi Arabia, in order to develop a solution, by examining the weather patterns, road maintenance reports, vehicle traffic data, and customer survey results. Key findings reveal that the customers satisfaction rate was 67.7%, 38.7% of customers experienced delivery delays and 11% received damaged goods, with infrastructure issues, road closures and poor visibility being primary causes. Based on the findings, a conceptual framework is proposed for a real-time supply chain system aimed at mitigating these challenges. 

The system integrates dynamic routing algorithms, participatory hazard reporting, and predictive analytics to address logistical inefficiencies. Real-time weather and traffic data are processed to optimize delivery routes, while hazard reports submitted by drivers enhance situational awareness. Predictive analytics utilizes historical data to anticipate disruptions, enabling proactive decision-making. The system is designed to adapt to Jazan’s unique conditions but can be extended to other regions and user groups to improve driving efficiency during severe weather. 

While the system remains untested, its conceptual design addresses key logistical challenges highlighted by the data analysis. The study contributes to the field of supply chain resilience by presenting a scalable and adaptive framework that leverages real-time data, participatory inputs, and predictive tools to enhance operational efficiency. Future work will focus on prototype development and testing to evaluate its practical effectiveness and scalability.