The rapid evolution of supply chain systems worldwide has introduced new challenges in workforce management, such as employee engagement, retention, and recruitment. Traditional human resource management (HRM) methods cannot keep pace with the complexity and speed at which decisions are made to maintain optimal operational efficiency in the business environment. To overcome these issues, this paper focuses on a generative AI (GAI)-driven HRM framework that caters to strategic workforce management in supply chain systems. The proposed methodology utilises GAI models to enhance human resource (HR) decision-making through predictive analytics and dynamic talent management. The GAI framework predicts future workforce demand, designs customized strategies for employee engagement and optimizes the recruitment process in line with fluctuating supply chain needs. The technique uses historical data on workforce demand and employee performance to identify changes in market conditions while addressing talent shortages. In addition, a global employee utility model is developed based on a combination of compensation factors, job satisfaction, and career growth, using machine learning-based turnover rate predictions. Simulations carried out in real-world supply chain datasets show significant improvements in workforce optimization: a 25% increase in the prediction accuracy of workforce levels, a 20% reduction in recruitment costs and a 15% reduction in employee turnover rates.