Estimation of Project Performance Using Earned Value Management and Fuzzy Regression

Subject Areas : Business Strategy

Mohammad Mahdi  Asgari Dehabadi ¹ (Department of Industrial engineering, University of Economic Science Tehran, Iran)
Mostafa  Salari ² (Department of Industrial engineering, Sharif University of Technology Tehran, Iran)
Ali Reza  Mirzaei ³ (Department of Technology Management, Allameh Tabataba’ee University Tehran, Iran)

Received: 2015-01-03 Accepted : 2015-03-01 Published : 2016-01-01

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.

References:

[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