چکیده مقاله
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 48%, shortens productivity ramp up by 37%, and increases organizational performance metrics by 32% 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
کلیدواژهها
نویسندگان
Babak Ghafari
Department of Computer Engineering, Ma C , Islamic Azad University, Mashhad, Iran
Vahid Torkzadeh
Department of Computer Engineering, Ma C , Islamic Azad University, Mashhad, Iran
شیوه ارجاع
Ghafari, Babak and Torkzadeh, Vahid,1404,Zero Learning Human Resource Management (ZL-HRM): A Data-Driven Framework for Autonomous Workforce Optimization,1th national conference on challenges of human capital management in large scale organizations,Mashhad
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