Abstract
Several studies have been conducted in the Project Management field further to improve the Earned Value Management (EVM) methodology to forecast the project cost estimate at completion (EAC). This work aims at developing a linear model to increase the accuracy of the standard EAC and minimize the variance of the error. The research is conducted on an EVM data set comprising 29 real-life projects for a total of 805 observations. Multiple linear regression analysis is performed to evaluate the number of regressors, the priority of the candidate EVM variables into the regression model, and to assess the diagnostics of the model fit. The new EAC formulation is benchmarked, the results show the model to provide higher accuracy and lower variance compared to the standard formulation.
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