All complex projects take place in environments of great uncertainty. Maintaining a monitoring and control system from the early stages of execution is a critical factor in the success of this type of project. Large hydroelectric power station construction projects are regarded as highly complex because they are affected by factors such as the risks inherent in a variety of fields of engineering, geology and the environment, the long execution times, and the large number of multidisciplinary activities to be carried out in parallel, among others. These types of projects are commonly affected by cost overruns and delays. This work develops a methodology for the monitoring and control of complex construction projects in the hydroelectric sector that enables a periodical calculation of metrics for physical progress, financial progress, and predictions for costs and durations on completion of the project. The verification of the efficiency of this methodology was based on stochastic simulation models applied to real projects in the hydropower sector. The results showed that the proposed methodology improved efficiency compared with existing traditional methodologies. The proposed methodology allows the simultaneous consideration of costs, deadlines, criticality, and risks of the activities of the analyzed projects and also incorporates multicriteria decision techniques to manage the influence of key aspects during the development of the project.

All projects take place within a context of uncertainty. That is especially noticeable in complex hydroelectric power generation projects, which are affected by factors such as the large number of multidisciplinary tasks to be performed in parallel, long execution times, or the risks inherent in various fields like geology, hydrology, and structural, electrical, and mechanical engineering, among others. Such factors often lead to cost overruns and delays in projects of this type. This paper analyzes the efficiency of the Earned Value Management technique and its Earned Schedule extension, as means of forecasting costs and deadlines when applied to complex hydroelectric power production projects. It is worth noting that this analysis was based on simulation models applied to real-life projects. The results showed that cost forecasting becomes very accurate over time, whereas duration forecasting is not reliably accurate.

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