Earned Value Probabilistic Forecasting Using Monte Carlo Simulation

The aim of this article is to present a proposal of interconnection between models and probabilistic simulations of project as possible ways to determine EAC (Final cost) through Earned Value Analysis. The article proves that the use of the 3 main models of projection (constant index, CPI and SCI) as the basis of a triangular probabilistic distribution that, through Monte Carlo simulation will permit associate and determine the probability according to the accomplishment of budgets and costs of the project. Earned Value Analysis Earned Value focuses on the relation between actual costs and the work done in the project within a certain time limit. The focus is on the performance obtained in comparison to what was spent to obtain it (FLEMING & KOPPELMAN, 1999a). Earned Value can be defined as the evaluation between what was obtained according to what was truly spent and to what was planned to be spent, in which it is suggested that the value to be earned initially by an activity is the value budgeted for this activity. As each activity or task of a project is accomplished, that value initially budgeted for the activity, now builds the Earned Value of the project. In order to formalize the concepts mentioned before, based on the norm ANSI/EIA 748 of the American National Standards Institute, a specific terminology was made up, based on data of the forecasted cost, real cost and earned value. The 3 elements of the Earned Value Analysis A project that will be controlled through the Earned Value Analysis needs to be planned through the management basic principles, applicable to any kind of project. Exhibit 1 evidences these management processes. Firstly, the work to be done is defined. In a second moment, the schedules and budgets are developed. The measurement and evaluation of the results of Earned Value are then, determined and compared to the planned values.