Zero Learning Human Resource Management (ZL-HRM): A Data-Driven Framework for Autonomous Workforce Optimization

This study introduces the concept of Zero Learning Human Resource Management (ZL-HRM), a data-driven framework designed to eliminate traditional training dependencies through intelligent workforce allocation. Leveraging machine learning (ML), deep reinforcement learning (DRL), and federated learning, the proposed framework predicts optimal employee-role alignment without the need for extended learning cycles. The research integrates historical HR datasets to construct adaptive allocation models that minimize onboarding time and maximize productivity. Empirical analysis demonstrates that ZL-HRM reduces training costs by up to ۴۸%, shortens productivity ramp-up by ۳۷%, and increases organizational performance metrics by ۳۲% compared to conventional HRM models. These findings establish ZL-HRM as a disruptive innovation bridging the gap between human resource analytics and autonomous decision intelligence. The framework provides both theoretical and practical implications for the evolution of future HRM systems in data-intensive organizations.

Keywords:

Zero Learning HRM , Machine Learning , Workforce Optimization , Reinforcement Learning , Federated Learning , Predictive HR Analytics , Automation , Organizational Performance