Employee performance optimization through Smart Workload Framework: Full-Time Equivalent based analysis

This study employs Full-Time Equivalent (FTE) to examine the workload of two departments of Bank BRI. The results show that, in general, there are still overloaded and underloaded employees, indicating that the workload is inefficient. Furthermore, this study examines the transformational potential of technological solutions in overcoming these difficulties by analyzing 279 papers obtained from Scopus metadata. This investigation reveals a three-dimensional framework of technological sophistication, data-driven accuracy, and efficiency-oriented management. Technological intelligence emphasizes integrating techniques such as artificial intelligence (AI) and robotic process automation (RPA) to facilitate real-time monitoring, predictive analytics, and process automation, efficiently addressing workload disparities. Furthermore, data-driven accuracy underscores the critical function of predictive models and analytics in improving resource allocation and workforce planning, supported by empirical data from industries such as healthcare and education. Meanwhile, efficiency-focused management prioritizes process optimization, leveraging automation, and collaborative efforts to increase productivity and achieve task balance.