Application of Artificial Neural Network to Forecast Actual Cost of a Project to Improve Earned Value Management System
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.
[1] C. Bower, B.Arch."New Directions in Project Performance and Progress
Evaluation" Property and Project Management, July 2007.
[2] C. F. Gray and E. W. Larson, Project Management: The Managerial
Process. 2nd ed., NJ: McGraw-Hill, 2003, pp 419-448.
[3] David S. Christensen, Richard C. Antolini, John W. McKinney," a
review of estimate at completion" Journal of Cost Analysis and
Management, Spring 1995, pp.41-62.
[4] Morrison, David C. "Deep-Sixing the A-12." Government Executive,
March 1991, pp. 30-35.
[5] Parker, Charles W. C/SCSC and C/SSR Cost Performance Analysis
Programs. DCAS Residency Harris, DCASMS, Orlando, Florida, 20
October 1980.
[6] Haydon, Joseph J. and Richard O Reither. Methods of Estimating
Contract Cost at Completion. ManTech International Corporation:
Virginia, 31 January 1982.
[7] Sincavage, John T. Time Series Analysis for the Army Internal Systems
Management (TSARISM). Cost Analysis Division. Army Aviation
Systems Command, St. Louis, Missouri, 3 October 1974.
[8] Watkins, Harry. An Application of Rayleigh Curve Theory To Contract
Cost Estimation and Control. MS. Thesis.Naval Postgraduate School,
Monterey, California, March 1982.
[9] Karsch, Arthur O. A Production Study Sequel to the Cost Performance
Forecasting Concept and Model. Cost Research Report No. 132.
Aeronautical Systems Division, Wright-Patterson AFB, Ohio, August
1976.
[10] Price, James B. An Evaluation of CPRA Estimate at Completion
Techniques Based Upon AFWAL Cost/Schedule Control System
Criteria Data. MS thesis, AFIT/LSY/GSM/855-28. School of Systems
and Logistics, Air Force Institute of Technology, Wright-Patterson AFB,
Ohio, September 1985 (AD-AI62282).
[11] H. Iranmanesh, N. Mojir, S. Kimiagari A new formula to "Estimate At
Completion" of a Project's time to improve"Earned value management
system" International Journal of Project Management, 2007.
[12] D. F. Cioffi "Designing project management: A scientific notation and
an improved formalism for earned value calculations.", International
Journal of Project Management vol.24, no.2, 136-144, 2006.
[13] S. Vandevoorde, M. Vanhoucke. "A comparison of different project
duration forecasting methods using earned value metrics", International
Journal of Project Management vol. 24, no.4, 289-302, 2006.
[14] C. F. Gray and E. W. Larson, Project Management: The Managerial
Process. 2nd ed., NJ: McGraw-Hill, 2003, pp 419-448.
[15] Zhang, G., Patuwo, B.E., Hu, M.Y., 1998." Forecasting with artificial
neural networks: the state of the art". International Journal of
Forecasting 14, 35-62.
[16] Kolisch, R., Hartman, S., 2000. Experimental evaluation of state-of-theart
heuristics for the resource constrained project scheduling problem.
European Journal of Operational Research 127, 394-407.
[17] The MathWorks, MATLAB (Online). Available:
http://www.mathworks.com.
[1] C. Bower, B.Arch."New Directions in Project Performance and Progress
Evaluation" Property and Project Management, July 2007.
[2] C. F. Gray and E. W. Larson, Project Management: The Managerial
Process. 2nd ed., NJ: McGraw-Hill, 2003, pp 419-448.
[3] David S. Christensen, Richard C. Antolini, John W. McKinney," a
review of estimate at completion" Journal of Cost Analysis and
Management, Spring 1995, pp.41-62.
[4] Morrison, David C. "Deep-Sixing the A-12." Government Executive,
March 1991, pp. 30-35.
[5] Parker, Charles W. C/SCSC and C/SSR Cost Performance Analysis
Programs. DCAS Residency Harris, DCASMS, Orlando, Florida, 20
October 1980.
[6] Haydon, Joseph J. and Richard O Reither. Methods of Estimating
Contract Cost at Completion. ManTech International Corporation:
Virginia, 31 January 1982.
[7] Sincavage, John T. Time Series Analysis for the Army Internal Systems
Management (TSARISM). Cost Analysis Division. Army Aviation
Systems Command, St. Louis, Missouri, 3 October 1974.
[8] Watkins, Harry. An Application of Rayleigh Curve Theory To Contract
Cost Estimation and Control. MS. Thesis.Naval Postgraduate School,
Monterey, California, March 1982.
[9] Karsch, Arthur O. A Production Study Sequel to the Cost Performance
Forecasting Concept and Model. Cost Research Report No. 132.
Aeronautical Systems Division, Wright-Patterson AFB, Ohio, August
1976.
[10] Price, James B. An Evaluation of CPRA Estimate at Completion
Techniques Based Upon AFWAL Cost/Schedule Control System
Criteria Data. MS thesis, AFIT/LSY/GSM/855-28. School of Systems
and Logistics, Air Force Institute of Technology, Wright-Patterson AFB,
Ohio, September 1985 (AD-AI62282).
[11] H. Iranmanesh, N. Mojir, S. Kimiagari A new formula to "Estimate At
Completion" of a Project's time to improve"Earned value management
system" International Journal of Project Management, 2007.
[12] D. F. Cioffi "Designing project management: A scientific notation and
an improved formalism for earned value calculations.", International
Journal of Project Management vol.24, no.2, 136-144, 2006.
[13] S. Vandevoorde, M. Vanhoucke. "A comparison of different project
duration forecasting methods using earned value metrics", International
Journal of Project Management vol. 24, no.4, 289-302, 2006.
[14] C. F. Gray and E. W. Larson, Project Management: The Managerial
Process. 2nd ed., NJ: McGraw-Hill, 2003, pp 419-448.
[15] Zhang, G., Patuwo, B.E., Hu, M.Y., 1998." Forecasting with artificial
neural networks: the state of the art". International Journal of
Forecasting 14, 35-62.
[16] Kolisch, R., Hartman, S., 2000. Experimental evaluation of state-of-theart
heuristics for the resource constrained project scheduling problem.
European Journal of Operational Research 127, 394-407.
[17] The MathWorks, MATLAB (Online). Available:
http://www.mathworks.com.
@article{"International Journal of Business, Human and Social Sciences:62268", author = "Seyed Hossein Iranmanesh and Mansoureh Zarezadeh", title = "Application of Artificial Neural Network to Forecast Actual Cost of a Project to Improve Earned Value Management System", 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.
", keywords = "Earned Value Management System (EVMS),Artificial Neural Network (ANN), Estimate At Completion,Forecasting Methods, Project Performance Measurement.", volume = "2", number = "6", pages = "672-4", }
