2026· International Journal of Multidisciplinary Research and Growth Evaluation· 0 citations
TL;DR
This paper develops an integrative conceptual framework that links AI-enabled compliance assurance with resilient coordination across national emergency supply networks and contributes to policy design, system architecture development, and future research on resilient, compliant, and adaptive emergency supply chain ecosystems.
Abstract
National emergency supply chains face increasing pressure from climate-induced disasters, pandemics, cyber-physical disruptions, and geopolitical instability. These shocks expose persistent coordination failures, regulatory fragmentation, and limited real-time visibility across public and private response networks. Recent advances in artificial intelligence offer a critical opportunity to redesign emergency supply chain preparedness through data-driven compliance monitoring and adaptive coordination mechanisms. This review synthesizes conceptual advances in AI-enabled compliance and coordination models that support national emergency supply chain readiness before, during, and after large-scale disruptions. The paper examines how machine learning, natural language processing, multi-agent systems, and digital twin architectures are being integrated into regulatory intelligence, inter-agency coordination, and risk-aware logistics planning frameworks. Particular attention is given to AI-driven compliance automation for emergency procurement, inventory governance, and cross-jurisdictional policy alignment, as well as coordination models that enable dynamic resource allocation and decentralized decision-making under uncertainty. The review also evaluates emerging governance challenges, including algorithmic transparency, accountability, data sovereignty, and interoperability across heterogeneous emergency management systems. By consolidating theoretical perspectives and recent implementation models, this paper develops an integrative conceptual framework that links AI-enabled compliance assurance with resilient coordination across national emergency supply networks. The findings contribute to policy design, system architecture development, and future research on resilient, compliant, and adaptive emergency supply chain ecosystems capable of supporting national preparedness objectives in an era of complex systemic risk.
Humanitarian and disaster response supply chains operate under extreme uncertainty, time pressure, and resource constraints, where delays or misallocations directly translate into human suffering and loss of life. In recent years, predictive intelligence models have emerged as critical enablers for enhancing supply chain resilience by improving anticipatory decision-making, situational awareness, and adaptive coordination across complex humanitarian networks. This review examines conceptual advances in predictive intelligence models applied to humanitarian and disaster response supply chains, with emphasis on their theoretical foundations, methodological evolution, and resilience-oriented capabilities. The paper synthesizes developments across data-driven forecasting, probabilistic risk modeling, machine learning, and hybrid human–AI decision frameworks, highlighting how these approaches support demand anticipation, disruption prediction, inventory pre-positioning, and logistics network reconfiguration. Particular attention is given to the integration of real-time data streams from remote sensing, social media, Internet of Things devices, and institutional reporting systems, as well as the role of explainability and trust in high-stakes humanitarian contexts. The review also discusses persistent challenges, including data sparsity, ethical constraints, model transferability across disaster types and regions, and governance issues related to inter-agency coordination. By organizing the literature around resilience dimensions—robustness, adaptability, and recoverability—the paper offers a unifying conceptual lens for evaluating predictive intelligence models beyond pure accuracy metrics. The study concludes by identifying research gaps and proposing future directions, including human-centered predictive systems, federated and privacy-preserving learning, and policy-aligned intelligence architectures. Overall, the review provides a structured foundation for researchers, practitioners, and policymakers seeking to leverage predictive intelligence to strengthen humanitarian supply chain resilience in increasingly volatile disaster environments.
Abiola Idowu, Abimbola Caleb Adesemoye, Esther Sydney et al.· International Journal of Mul...· 0 citations
Healthcare resource allocation during crises fails because decisions are made by separate systems operating in silos, limiting the ability to adapt quickly as demand changes. This problem disproportionately affects low- and middle-income countries (LMICs), where a lack of infrastructure leads to more logistical failures. This paper asks how healthcare distributors can improve resource allocation during crises, comparing existing solutions of government stockpiling, mutual aid agreements, logistics management information systems (LMIS), blockchain, localized 3D printing, and a new proposed solution of artificial intelligence (AI) using Ben Shneiderman’s reliability, safety, and trust (RST) framework, as well as considerations regarding LMIC feasibility. The paper argues that instead of replacing existing systems, the proposed AI solution should function as a human-monitored coordination layer built on LMIS data, using demand forecasting, shortage prediction, route optimization, and anomaly detection. The paper’s original contribution is a system architecture and pilot framework for a regional network of hospitals. This framework includes measurable outcomes such as shortage days, forecast error, response time, emergency shipments, waste, equity gaps, and human overrides. Because no live deployment has been tested, the paper’s conclusions are conceptual and not empirical. Yet, the comparisons suggest that a coordinated, transparent, and human-centered AI model may be more realistic and defensible than treating AI as a standalone replacement in an industry where trust is so vital.
Modern Disaster Management Systems (DMS) are currently shifting from reactive hazard response to proactive, intelligent resilience architectures. As global hazards escalate in both frequency and severity, classical mitigation lifecycles are increasingly burdened by severe latency in information processing and resource allocation.
To address these shortcomings, a comprehensive, Artificial Intelligence-driven framework for disaster management is proposed, which synthesizes state-of-the-art computational models with foundational hazard theories. By integrating Geographic Information Systems (GIS), Internet of Things (IoT) distributed sensor networks, and sophisticated multimodal data fusion techniques, this proposed system accurately processes disparate spatial and environmental data streams in real time. Crucially, the architecture utilizes the Gemini 2.5 multimodal model to orchestrate extreme-scale context management and to execute next-generation agentic capabilities across decentralized emergency response networks.
Furthermore, the paper critically examines the socio-technical challenges associated with crisis management AI and proposes explicit methodologies to mitigate spatial blind spots in vulnerability mapping as well as algorithmic bias. The ultimate result is a translational, highly scalable AI framework engineered for equitable, real-world deployment.
Risav Dey· International Journal For Mu...· 0 citations
The devastating California wildfires of January 2025 underscored the staggering human and economic tolls of escalating climate disasters. While retrospective analyses often attribute these losses to climate change, fuel accumulation, and wildland-urban interface expansion, a critical systemic blind spot remains: the inefficiencies in wildfire suppression driven by misaligned incentives, behavioral biases, and fragmented interagency coordination. In this perspective, we argue that Agentic Artificial Intelligence (Agentic AI) offers a transformative pathway to bridge these operational divides, moving beyond isolated, local resource optimization toward a synchronized, global response capability. To address these challenges, we propose a comprehensive governance framework designed to successfully employ multi-agent AI systems for disaster management. By aligning incentives, ensuring accountability, and fostering cross-agency collaboration, this framework provides a blueprint for leveraging Agentic AI to mitigate the coordination crisis in wildfire management and build systemic resilience against future climate shocks. Integrating agentic AI within a shared governance framework for wildfire management would help overcome cooperation problems and improve collaboration and coordination in decision-making across agencies, suggests a synthesis of inefficiencies and options for improvement.
Ramit Debnath, Aric P. Shafran, Israel Waichman· Communications Earth & Envir...· 0 citations
The U.S. trucking and freight logistics industry plays a critical role in supporting economic activity and maintaining the efficient movement of goods across national supply chains. Despite its significance, there is a persistent challenge in communication and coordination in the sector that leads to operational inefficiencies, delays in shipments, high operating costs and lack of supply chain visibility. Recent developments in artificial intelligence (AI) have created new opportunities to improve communication processes and enhance coordination between key logistics stakeholders, including shippers, carriers, brokers, dispatchers and drivers.
This paper provides an overview of how AI-powered communication tools can improve the efficiency of coordination in U.S. trucking and freight logistics operations. The study explores current literature using a narrative literature review approach for Natural Language Processing, Machine Learning, Internet of Things (IoT) telematics, predictive analytics, automated dispatch systems, and digital freight communication platforms. The review assesses the role of these technologies in enabling real-time information exchange, enhancing the visibility of the operations, minimizing delays in communication and strengthening decision-making across freight networks.
The results suggest that AI-powered communication systems can have a significant impact on logistics operations by minimizing administrative tasks, improving load matching accuracy, boosting logistics visibility, and providing proactive solutions to disruptions. However, challenges related to data interoperability, cybersecurity, regulatory uncertainty, workforce adaptation and implementation costs continue to influence adoption outcomes. Overall, the paper finds that the U.S. freight sector has significant potential to gain from the use of AI-enabled communication infrastructure to enhance the efficiency of coordination and increase supply chain resilience. These benefits will take time to be realised and will need ongoing investment in digital infrastructure, industry partnerships, good governance structures, and human-centered systems design.
Olayemi Olorunsola· Journal of Management Resear...· 0 citations
Air traffic management (ATM) modernization is being shaped by digitalization, automation, traffic growth, resilience requirements, and climate policy. This research develops a tri-dimensional framework that treats technological capability, organizational readiness, and environmental responsibility as interdependent features of system transition. It uses an integrative, concept-driven review of peer-reviewed studies and accountable institutional sources. The synthesis covers SESAR and NextGen, artificial intelligence, remote digital towers, unmanned-aircraft integration, human–AI teaming, high-reliability governance, trajectory optimization, and CO₂ and non-CO₂ mitigation. The framework is then applied to the ICAO Middle East Region and to Türkiye/DHMİ. The cases indicate that advanced national systems do not automatically produce network-wide performance. In the Middle East, major investment and demand growth coexist with uneven interoperability, coordination, and environmental measurement. In Türkiye, documented programmes in indigenous R&D, simulation, free-route airspace, and triple-runway operations show how technical deployment is linked to training, assurance, and operational learning. The research argues that sustainable modernization depends on continued alignment across the three dimensions, transparent benefit attribution, and staged implementation supported by operational evidence. The framework is intended to inform context-sensitive analysis by regulators, air navigation service providers, technology developers, and researchers.
S. Çeken, A. Tuncal· 0 citations
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