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Forecast Actual Cost of a Project to Improve Earned Value Management System
BibTeX
@MISC{Iranmanesh_forecastactual,
author = {Seyed Hossein Iranmanesh and Mansoureh Zarezadeh},
title = {Forecast Actual Cost of a Project to Improve Earned Value Management System},
year = {}
}
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Abstract
Abstract—This paper presents an application of Artificial Neural Network (ANN) to forecast actual cost of a project based on the earned value management system (EVMS). For this purpose, some projects randomly selected based on the standard data set, and it is produced necessary progress data such as actual cost,actual percent complete, baseline cost and percent complete for five periods of project. Then an ANN with five inputs and five outputs and one hidden layer is trained to produce forecasted actual costs. The comparison between real and forecasted data show better performance based on the Mean Absolute Percentage Error (MAPE) criterion. This approach could be applicable to better forecasting the project cost and result in decreasing the risk of project cost overrun, and therefore it is beneficial for planning preventive actions.
Keyphrases
forecast actual cost improve earned value management system actual cost standard data set percent complete preventive action baseline cost necessary progress data project cost hidden layer project cost overrun value management system mean absolute percentage error forecasted actual cost artificial neural network actual percent complete
