Sensitivity of Earned Value Schedule Forecasting to S-Curve Patterns

Abstract

This paper examines sensitivity of the performance of seven project duration forecasting methods in the earned value management (EVM) literature to characteristic patterns of planned value and earned value S-curves. Specifically, this paper aims at identifying relative robustness and early warning capacity of six deterministic methods and one probabilistic method with respect to the nonlinearity of progress curves and the schedule delay patterns. The sensitivity analysis in this paper shows that forecast accuracy and early warning credibility of deterministic methods are very sensitive to the S-curve patterns, especially early in a project. The results also indicate that the probabilistic method (the Kalman filter earned value method) is the only method among the seven alternatives that is robust with respect to the progress curve nonlinearity and the schedule delay patterns. Consequently, this paper would positively contribute to the practice of project schedule control by providing practical guidance for and valuable insights into a sanity test on the forecasts and warning signals from the forecasting methods so that more informed decisions are made and unnecessary control actions triggered by false warning can be effectively prevented.