The capability to elaborate a reliable estimate at completion for a project since the early stage of project execution is the prerequisite in order to provide an effective project control. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders increase project complexity and raise the need to exploit all the available knowledge sources in order to improve the forecasting process. Therefore, drawing on a set of case studies, this paper proposes a Bayesian approach to support the elaboration of the estimate at completion in those industrial fields where projects are denoted by a high level of uncertainty and complexity. The Bayesian approach allows to integrate experts’ opinions, data records related to past projects and data related to the current performance of the ongoing project. Data from past projects are selected through a similarity analysis. The proposed approach shows a higher accuracy in comparison with the traditional formulas typical of the Earned Value Management (EVM) methodology.
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