Predicting Human Resource Trends in Technical Education Through ERP Data and Machine Learning Models

Abstract

Enterprise Resource Planning (ERP) systems have become integral in streamlining academic and administrative processes in technical education institutions. However, their impact on human resource (HR) trends, including faculty performance, student outcomes, and job placements, remains underexplored. This study leverages ERP-generated data to predict HR trends in Odisha’s technical education sector using machine learning models. The dataset comprises institutional records, faculty evaluations, student performance metrics, and employment statistics collected from ERP systems. We employ Random Forest and Gradient Boosting models to analyze key determinants influencing HR efficiency. Results indicate that faculty engagement and student ERP usage significantly correlate with improved job placement rates and academic performance. The study highlights the predictive power of machine learning in forecasting HR trends, aiding policy decisions for educational institutions. By integrating ERP analytics with AI-driven models, institutions can optimize HR strategies and enhance student career outcomes.