Skill-Pulse:An Intelligent Decision Support Architecture for Dynamic Workforce Allocation

Skill-Pulse:An Intelligent Decision Support Architecture for Dynamic Workforce Allocation

DR. S. Gnanapriya1, Athira R2

1Associate professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.

ncmdrsgnanapriya@gmail.com

2Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.

athiponnu2020@gmail.com

Abstract

Rapid transformations in the labor market, driven by digitalization, automation, and artificial intelligence, have intensified skill mismatches and workforce instability across urban economies. Traditional labor analytics platforms rely on static reports and siloed indicators, limiting their ability to support strategic workforce planning and policy intervention. To address these challenges, this paper proposes Skill-Pulse AI, an integrated, intelligence-driven decision support system for real-time labor market analysis, forecasting, and reskilling optimization.

The proposed system employs a hybrid analytical architecture that combines geospatial intelligence, time-series demand forecasting, anomaly detection, graph-based career path optimization, and semantic resume analysis within a unified Streamlit-based interface. Market dynamics are modeled using synthetic-real hybrid data, enhanced with seasonality-aware neural forecasting and Isolation Forest–based risk detection. Network graph algorithms are applied to compute optimal reskilling pathways, while policy simulations and persona-based analytics enable both job-seeker and institutional decision support.

Experimental evaluation demonstrates that Skill-Pulse AI effectively identifies high-demand skill clusters, talent supply gaps, and labor market risk zones with improved interpretability and responsiveness compared to conventional systems. Forecasting modules capture short-term demand trends with scenario-based projections, while reskilling recommendations reduce transition cost by optimizing salary and skill distance metrics. The system further enables quantitative policy impact assessment through grant-to-employment ROI simulations.

The implications of this work extend to government agencies, educational institutions, enterprises, and individual professionals, providing actionable intelligence for workforce resilience, equitable labor mobility, and strategic investment planning. By integrating multiple analytical lenses into a single platform, Skill-Pulse AI supports evidence-based decision-making in complex labor ecosystems.

Despite its effectiveness, the current implementation relies partially on simulated market signals and city-level aggregation, which may limit fine-grained sectoral accuracy. Future enhancements will incorporate real-time job portal feeds, deep learning–based semantic embeddings, and cross-country labor mobility modeling to further strengthen predictive precision and global applicability.

Keywords: - Labor Market Intelligence; Workforce Analytics; Skill Gap Analysis; AI-Based Demand Forecasting; Reskilling Recommendation System; Geospatial Labor Analysis; Policy Impact Simulation; Anomaly Detection; Career Path Optimization