A Bayesian approach to set the tolerance limits for a statistical project control method

In this paper, we address the project schedule control problem under an uncertain environment. We propose a new method to set the tolerance limits based on the Earned Value Management/Earned Schedule (EVM/ES) schedule performance metrics. These tolerance limits can help a project manager to identify whether the schedule deviations from the baseline schedule are within the possible deviations derived from the expected variability of the project or if corrective actions must be taken to get the project back on track. We view the project control problem as a statistical hypothesis test with the null hypothesis being that the project progress is out of control. First, a simulation is performed to generate two types of empirical conditional distributions of the monitored schedule indicator. Afterwards, an algorithm that uses the derived conditional distributions as inputs is proposed to optimise the tolerance limits. An extensive computational experiment is carried out to assess the performance of the proposed approach. Additionally, sensitivity experiments are conducted to analyse four underlying factors that may influence the power of the proposed method. Experimental results show that our approach can keep the first type error under the required level ( $\alpha = 0.05 $α=0.05) in any situation, meanwhile reducing the second type error significantly compared with three other methods in the literature.