The role of advanced earned value management (EVM) metrics in schedule performance analysis of building projects

Although venerable by the tide, the tools and metrics used still include CPI, SPI, and performance in the form of schedules; these hardly reflect true scheduling performance, especially in more complex scenarios of building projects subject to multiple variables. Thus, the advanced metrics of EVM introduced for measuring the Earned Schedule (ES), Schedule Variance (SV), and To-Complete Performance Index (TCPI) allow not only the argument of schedules on more rigorous grounds but also for extensive commentary with an eye both to history and future in respect of schedule performance. This research follows a mixed-method approach comprising quantitative data analysis and qualitative case studies. Quantitative analysis was applied to data from building projects to compare conventional with advanced metrics. By interviewing project managers, qualitative data were obtained to inform on the applicability and effectiveness of advanced EVM metrics. Advanced EVM metrics have proved to greatly assist in appraising the schedule performance. Advanced metrics such as ES and SV provide a greater depth of understanding concerning the project's progress and thus afford its managers some discretion for making proactive decisions. In other words, TCPI comes in concerning forecasts and, thereby, resource planning, showing even this level of performance that must be achieved to meet the deadlines. These metrics also synchronize cost and schedule management so that immediate detection of issues can occur with a concomitant reduction of the risk of delays and cost overruns, providing a sense of security and optimism. Transparency plus communication, combined with Evm Metrics, significantly affect stakeholders' trust and satisfaction. Training workshops organized for project teams and management tools emerging as organizational tools with these advanced metrics can enhance these benefits. Other options range from investigating possible applications for different project types to possible synergies in integrating AI technologies.