Eliminating Sub-Optimality in Earned Value Management Scheduling

Strategic and operational project successes rely on optimal implementation, with time-to-execution being a key value driver and competitive advantage. Earned Value Management (EVM) is the most pervasive approach to scheduling and cost monitoring in large-scale projects. Earned Schedule (ES) is a derivation of EVM that specifically addresses scheduling metrics, aiming to enhance the accuracy and relevance of time-based project assessments. However, prior research has shown that both EVM and ES may produce sub-optimal scheduling results. In addressing these challenges, our research aims to significantly reduce sub-optimality in EVM and ES schedules, meaning to methodically minimize inefficiencies within EVM processes, though recognizing that absolute optimality may not be achievable due to inherent project complexities. Specifically, this research demonstrates the common conditions under which “top-down”, ES metrics generate sub-optimal schedule assessments against the baseline. The purpose of our work is two-fold: First, we articulate why sub-optimality occurs. Second, utilizing our schedule variance path level metrics (SVP(t)), we address the ES limitation. The validity and practicality of our approach were demonstrated using three execution scenarios with simulation stress-tests. Our bottom-up approach considers schedule progress on critical and non-critical paths and utilizes total slack when necessary. Our results propose a tractable solution that improves schedule measurement accuracy, particularly in project environments with parallel activities and varying slacks. This enhancement is most significant in complex project topologies where traditional methods fall short, thereby underscoring the critical importance of detailed path-level analysis in scheduling across diverse tracking periods.