Real-Time Monitoring and Resource Allocation in Public Administration Using AI-Driven Demand Forecasting : A Comprehensive Review

Abstract

A dynamic data-driven framework to improve public administration's efficiency in certificate issuance is covered in this paper. Delays and growing backlogs result from traditional resource allocation models' frequent inability to modify their demands in response to shifting demand. The suggested framework supports improved resource utilization with the least amount of delay for delivery services by combining the advantages of cloud computing, real-time monitoring, and predictive analytics to react to variations in demand. These will enable more proactive resource management through AI-driven demand forecasting, regular adjustments, and real-time dashboards. The results show significant gains in speed, transparency, and citizen satisfaction when handling the practical difficulties of data standardization and system interoperability. The study provides a basis for further investigation into adaptable resource allocation in public administration. The findings indicate that this innovative approach significantly enhances speed, transparency, and citizen satisfaction while tackling practical challenges such as data standardization and system interoperability. The study underscores the importance of adaptable resource allocation in public administration, providing a solid foundation for future research in this area. Ultimately, the framework not only streamlines the certificate issuance process but also fosters a more proactive and efficient public service environment, benefiting both administrators and citizens alike.