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.
Abstract: Estimation time and cost of work completion in a
project and follow up them during execution are contributors to
success or fail of a project, and is very important for project
management team. Delivering on time and within budgeted cost
needs to well managing and controlling the projects. To dealing with
complex task of controlling and modifying the baseline project
schedule during execution, earned value management systems have
been set up and widely used to measure and communicate the real
physical progress of a project. But it often fails to predict the total
duration of the project. In this paper data mining techniques is used
predicting the total project duration in term of Time Estimate At
Completion-EAC (t). For this purpose, we have used a project with
90 activities, it has updated day by day. Then, it is used regular
indexes in literature and applied Earned Duration Method to
calculate time estimate at completion and set these as input data for
prediction and specifying the major parameters among them using
Clem software. By using data mining, the effective parameters on
EAC and the relationship between them could be extracted and it is
very useful to manage a project with minimum delay risks. As we
state, this could be a simple, safe and applicable method in prediction
the completion time of a project during execution.