The growing complexity of infrastructure projects, such as railway construction, necessitates precise tools to monitor progress and accurately predict completion times. Traditional methods often fail to capture real-time schedule dynamics, resulting in ineffective project management. This study employs earned value management (EVM) and earned schedule (ES) to predict the project duration for a double-track railway initiative. Performance metrics for scheduling, such as SPI and SPI(t), as well as forecasting tools, including EAC(t), IEAC, and IEAC(t), were analyzed over 28 weeks. The results demonstrate that SPI(t)-based methods offer more stable and realistic duration forecasts than conventional SPI-based approaches, particularly during periods of poor performance. IEAC values fluctuated sharply during the project's early and middle stages, exceeding 800 days, while IEAC(t) remained consistent, aligning closely with actual progress. After the implementation of contract addenda in Weeks 21 and 27, both indicators improved significantly. SPI and SPI(t) exceeded 1.0, and the forecasted completion date aligned closely with the original plan. The study concludes that the ES method improves schedule forecasting accuracy and provides better insight into project performance trends.
Widowati et al. (Tue,) studied this question.