Abstract

Effective management of human capital is crucial for organizations to thrive in today's competitive business landscape. Organizations recognize the significance of investing in their human resources, as they are the primary drivers of productivity, innovation, and overall organizational success. However, allocating resources to human capital investments requires careful planning and decision-making. This research proposes a novel approach to predicting organizational asset and investments based on the assessment of human capital using a resource management model. The model incorporates key factors such as employee skills, experience, performance, and potential for growth, along with market dynamics and organizational goals. By leveraging data analytics techniques and machine learning algorithms, the model aims to provide organizations with valuable insights and predictions regarding the impact of human capital investments on organizational outcomes. The proposed predictor offers several benefits, including enhanced resource allocation strategies, improved workforce planning, and increased return on investment in human capital. It enables organizations to make informed decisions regarding recruitment, training and development initiatives, talent retention, and succession planning. Moreover, the predictor facilitates proactive decision-making by identifying potential gaps and opportunities in the human capital landscape. The research draws upon existing theories and frameworks related to human capital management, resource allocation, and predictive analytics. The empirical validation of the proposed model involves the analysis of historical organizational data, employee performance metrics, and financial indicators. The outcomes of this research contribute to the field of strategic human resource management and provide organizations with a valuable tool for optimizing their investments in human capital.