Cost performance as a stochastic process: EAC projection by Markov chain simulation

Earned value analysis (EVA) has been widely used in the construction industry for cost prediction at completion. The EVA's accuracy of early cost projections is low since the method assumes static cost performance during construction. A project's cost performance is evidenced as a stochastic process. In an effort to improve the EVA's accuracy of early cost predictions, this work reports a modified method of Markovian simulation cost projection (MSCP). Based on Markov chain simulation, MSCP simulates the probability distribution of the cost performance indicators for each period of a project, and predicts the final cost using the summation of each simulated period cost. The MSCP method is demonstrated and validated through a case study of a real-world power plant project. Data analysis indicates that MSCP improves the prediction accuracy four times higher than EVA. Findings also suggest that MSCP is able to capture erratic changes of cost performance throughout a project's lifecycle and thus provides better EAC (estimate at completion) predictions and early warnings.