Estimation of Project Performance Using Earned Value Management and Fuzzy Regression
Subject Areas : Business StrategyMohammad Mahdi Asgari Dehabadi 1 , Mostafa Salari 2 , Ali Reza Mirzaei 3
1 - Department of Industrial engineering,
University of Economic Science
Tehran, Iran
2 - Department of Industrial engineering,
Sharif University of Technology
Tehran, Iran
3 - Department of Technology Management,
Allameh Tabataba’ee University
Tehran, Iran
Keywords: Project Management, Estimation, Fuzzy regression, earned value management,
Abstract :
Earned Value Management is a critical project managementmethodology that evaluates project performance from cost andschedule viewpoints. The novel theoretical framework presented in thispaper estimates future performance of project regarding the past relativeinformation. It benefits from fuzzy regression (FR) models in estimationprocess in order to deal with the vagueness and impreciseness ofreal data. Furthermore, fuzzy-based estimation is evaluated using linguisticterms to interpret different possible condition of projects. Theproposed model can greatly assists project managers to assess prospectiveperformance of project and alerts them in taking of necessaryactions. Finally, one illustrative case associated with a constructionproject has been provided to illustrate the applicability of theoreticalmodel in real situations.
[1] PMI. (2012), Practice standard for Earned Value management. PMI Publication.
[2] Al-Jibouri, S. H. (2003), Monitoring systems and their effectiveness for
project cost control in construction. International Journal of Project Management,
21 (2), 145-154.
[3] Bagherpour, M., Zareei, A., Noori, S., and Heydari, M. (2010), Designing
a control mechanism using earned value analysis: An application to production
environment. International Journal of Advanced Manufacturing
Technology, 49 (5-8), 419-429.
[4] Baumeister, A. and Floren, A. (2011), Optimizing the Configuration of
Development Teams Using EVA: The Case of Ongoing Project Adjustments
Facing Personnel Restrictions. International Journal of Information
Technology Project Management, 2 (1), 62-77.
[5] Moselhi, O., Li, J., and Alkass, S. (2004), Web-based integrated project
control system. Construction Management and Economics, 22 (1), 35-46.
[6] Oven, J. K. (2007), Implementing EV management in a R & D environment.
AACE International Transactions EVM, 1, 01-05.
[7] Anbari, F. (2003), Earned Value Project Management Method and Extensions.
Project Management Journal, 34 (41), 12-23.
[8] Cioffi, D. F. (2006), Designing project management: A scientific notation
and improved formalism for EV calculations. International Journal
of Project Management , 24, 134-144.
[9] Jacob, D. S. (2003), Forecasting project schedule completion with earned
value metrics. The Measurable News, 1, 7-9.
[10] Lipke, W. (2003), Schedule is different. The Measurable News, 31-34.
[11] MoslemiNaeni, L. and Salehipour, A. (2011), Evaluating fuzzy earned
value indices and estimates by applying alpha cuts. Expert Systems with
Applications, 38 (7), 8193-8198.
[12] Salari, M., Bagherpour, M., and Kamyabniya, A. (2014), Fuzzy extended
earned value management: A novel perspective. Journal of Intelligent and
Fuzzy Systems. Pre-press.
[13] Salari, M., Bagherpour, M., and Wang, J. (2014), A novel earned value
management model using Z-number. International Journal of Applied Decision
Sciences, 7 (1), 97-119.
[14] Jacob, D. S. and Kane, M. (2004), Forecasting schedule completion using
earned value metrics revisited. The Measurable News, 1, 11-17.
[15] Barraza, G. A., Back, W. E., and Mata, F. (2004), Probabilistic forecasting
of project performance using stochastic S curves, 130 (1), 25.
[16] Dillibabu, R. and Krishnaiah, K. (2005), Cost estimation of a software
product using COCOMO II. 2000 model-A case study. International Journal
of Project Management, 23 (4), 297-307.
[17] Lipke, W., Zwikael, O., Henderson, K., and Anbari, F. (2009), Prediction
of project outcome: The application of statistical methods to earned
value management and earned schedule performance indexes. International
Journal of Project Management, 27 (4), 400-407.
[18] Warburton-Roger, D. H. (2011), A time-dependent earned value model
for software projects. International Journal of Project Management, 29
(8), 1082-1090.
[19] Feylizadeh, M. R., Hendalianpour, A., and Bagherpour, M. (2012), A
fuzzy neural network to estimate at completion costs of construction
projects. International Journal of Industrial Engineering Computations,
3 (3), 477-484.
[20] Azman, M. A., Abdul-Samad, Z., and Ismail, S. (2013), The accuracy
of preliminary cost estimates in Public Works Department (PWD) of
Peninsular Malaysia. International Journal of Project Management, 31
(7), 994-1005.
[21] Caron, F., Ruggeri, F., and Merli, A. (2013), A Bayesian Approach to
Improve Estimate at Completion in Earned Value Management. Project
Management Journal, 44 (1), 3-16.
[22] Zadeh, L. (1965), Fuzzy sets. Information and Control, 338-353.
[23] Naeni, L., Shadrokh, S., and Salehipour, A. (2011), A fuzzy approach for
earned value management. International Journal of Project Management,
29, 764-772.
[24] Vandevoorde, S. and Vanhoucke, M. (2005), A comparison of different
project duration forecasting methods using EV metrics. International
Journal of Project Management, 24, 289-302.
[25] Tanaka, H., Uejima, S., and Asai, K. (1982), Linear regression analysis
with fuzzy model. IEEE Transaction on Systems, Man and Cybernetics,
12 (6), 903-907.
[26] Change, Y. and Ayyub, B. (2001), Fuzzy regression methods-a comparative
assessment. Fuzzy sets and systems, 119 (2), 187-203.
[27] D’Urso, P. (2003), Linear regression analysis for fuzzy/crisp input and
fuzzy/crisp output data. Computational Statistics & Data Analysis, 42,
47-72.
[28] Yang, M. S. and Lin, T. S. (2002), Fuzzy least squares linear regression
analysis for fuzzy input-output data. Fuzzy Sets and Systems, 126, 389-
399.
[29] Kiossi, M. C. and Shapiro, A. (2006), Fuzzy formulation of the Lee-Carter
model for morality forecasting. Insurance, Mathematics and Economics,
39, 287-309.
[30] S´anchez, J. d. A. (2006), Calculating insurance claim reserves with fuzzy
regression. Fuzzy Sets and Systems, 157 (23), 3091-3108