Authors: Anushree, Dr. P. Lalitha Associate Professor, Dr. S. Suja Assistant Professor
Abstract: With the rapidly evolving business environment, the traditional forms of workforce planning can no longer be used to manage uncertainty, skill upheaval and rapidly shifting talent requirements. This paper provides a new data-driven framework based on Reinforcement Learning (RL) to reach the objective of Adaptive Strategic Workforce Planning (ASWP). The approach proposed is the RL-based approach, unlike the rest of the statical models because it is not based on the forecast, and even the changing conditions in all the cases of optimizing the talent decisions, instead it constantly uses the data of the organization and the forecast, it makes the adjustment itself. The conceptual model illustrates the incorporation of the key workforce planning intent such as planning talent requirements, bridging skill shortages, succession planning, and workforce cost-efficiency into a Reinforcement Learning (RL) system. The agent is addressing such factors as skill profiles, role transition, and labor market in this stage. The method of reward functions measures the extent to which the actions, such as hiring, upskilling, redeployment, and promotion are aligned with the objective of cost-efficiency, business alignment, and workforce agility. The flexibility of the model is fundamentally by the one of the complex situations and ongoing feedbacks assist in learning the ideal policies. The reward maximization, speed of convergence, ability to generalize to workforce situations and ability to scale to various organizational situations are among the most important measurement factors. Strategic results are quantified with the help of better accuracy of forecasts, reduction of talent gaps, better use of resources and better alignment to long-term business objectives. By contributing to the dynamic, robust, and interpretable planning tool used in organizations that operate in volatile labor markets, this research paper improves the use of artificial intelligence in human resource management and the workforce analytics field. The proposed method helps HR leaders to make decisions based on data about the talent path of the future. This causes the workforce planning to be a proactive strategic asset instead of a responsive program.
