Authors: Camila Duarte, Romain Delacroix, Arjun Mehta, Tobias Lindgren, Ananya Kulkarni
Abstract: Workforce systems are increasingly expected to operate as adaptive intelligence infrastructures rather than static repositories of employee transactions. Conventional ERP based human capital platforms were engineered to ensure data accuracy, compliance integrity, and standardized administrative workflows; however, their batch oriented processing logic and retrospective analytics limit organizational capacity to detect emerging workforce risks and coordinate timely interventions. This study proposes an event-driven, machine learning enabled ERP architecture that transforms human capital systems into continuously responsive ecosystems capable of real time sensing, predictive modeling, and automated orchestration. The framework integrates streaming event pipelines, dynamic feature engineering, embedded predictive and prescriptive models, and governance aligned control mechanisms within a unified enterprise environment. A novel Continuous Workforce Optimization Index is introduced to quantify adaptive stability across engagement momentum, performance variability, capacity distribution, and compliance consistency. Through architectural modeling and scenario based simulation, the research demonstrates measurable reductions in decision latency, improvements in prediction accuracy, enhanced intervention precision, and strengthened systemic resilience when compared with traditional batch driven ERP configurations. The findings establish a scalable blueprint for designing next generation human capital architectures that enable sustained, continuous workforce optimization in complex and rapidly evolving enterprise contexts.
DOI: https://doi.org/10.5281/zenodo.19104880
