Harnessing Predictive HR Analytics to Strengthen U.S. Workforce Deployment for Next-Generation Industrial Leadership

Authors

  • Osariemen Tina Afolabi Department of Computer Science and Quantitative Methods Faculty of Computer Science and Information Technology Austin Peay State University

DOI:

https://doi.org/10.38124/ijsrmt.v4i12.1042

Keywords:

Predictive HR Analytics, Workforce Planning, Talent Deployment, Industrial Competitiveness, Machine Learning, Employee Retention, Skills Gap Analysis

Abstract

This paper examines the strategic role of predictive HR analytics in strengthening U.S. workforce deployment for nextgeneration industrial leadership. As American industries undergo rapid technological transformation, the gap between industrial talent demand and HR system capacity continues to widen. Predictive HR analytics offers a data-driven solution by enabling organizations to forecast workforce needs, enhance recruitment quality, predict employee turnover, and identify critical skill gaps. This study analyzes the potential impact of widespread predictive analytics adoption on U.S. industrial competitiveness, implementation challenges, and strategic recommendations for successful deployment. The findings suggest that systematic integration of predictive HR analytics can significantly enhance workforce agility, reduce talent shortages, and support longterm industrial transformation in sectors including digital manufacturing, aerospace, renewable energy, and robotics.

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Published

2025-12-12

How to Cite

Afolabi, O. T. (2025). Harnessing Predictive HR Analytics to Strengthen U.S. Workforce Deployment for Next-Generation Industrial Leadership. International Journal of Scientific Research and Modern Technology, 4(12), 1–12. https://doi.org/10.38124/ijsrmt.v4i12.1042

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