Integrated AI-Based Decision Support Systems for Emergency Supply Chain Management in the United States During Natural Disasters

Abstract

The more natural disasters increase in frequency and severity in the United States (such as hurricanes, wildfires, floods, and pandemics), the more it illustrates the vulnerability of emergency supply chain systems. These incidents typically result in the general gridlock of transportation systems, prolonged delays in the deployment of resources, and a lack of coordination among federal, state, and local actors, as well as those in the non-governmental sector. The effects are not just logistical; they have a direct impact on the safety, health, and survival of the populations affected. Even at the current stages of development of logistics technologies, the majority of traditional emergency management systems are reactionary, using stale data pipelines, manually planned, and lock-step processes that have little capacity to adapt to the dynamic situation in disasters. To counter these repeated failures, this paper will suggest a connected Artificial Intelligence (AI) based Decision Support System (DSS) framework that will reset how emergency supply chains work in the case of a natural disaster. AI solutions. The possibilities of better disaster readiness, a faster decision-making process, and logistics coordination based on more intelligent, more flexible decisions lie in AI technologies, especially machine learning techniques, predictive analytics, and real-time optimization. The infrastructure introduced here is interoperable, data-composed, and provides real-time situational awareness as it addresses latency, decreases the imbalance between the supply and demand, and assures equal access to resources of the region affected by the disaster. In reviewing the current challenges, the paper proposes a modular AI-based DSS architecture and a broad roadmap of strategic implementation, also covering technical, institutional, and policy aspects. Using historical case examples and simulation-based lessons, the present study provides useful tips to the audience of emergency managers, policymakers, and technology developers. This ends with a future concern about the national resilience problem and the necessity of intelligent, integrated systems in the disaster response segment of infrastructure.