An Assess of Operational Project Performance by Integrating Earned Value Management and Learning Curve Theory

Document Type: Research Paper


Department of Industrial Engineering, Payam Noor University of Babol, Mazandaran, Iran


This study adopts an integrated performance measurement and prediction model based on a combination of earned value management approach and the learning curve theory under risk condition. The research has two main parts: the first part concerns the project performance measurement and the second part focuses on forecasting performance indicators in terms of time and cost of the project subject to the errors and risks. The contributions of the present study are threefold. First, this study extends to the traditional performance measurement model, which focuses only on forecasting time at completion, by extending the performance measurement domain to analyse both time and cost. Secondly, the learning curve models are explicitly used as a basis to assess the nonlinear effect of learning on the performance. Novel risk performance metrics are proposed and adopted for knowledge-based projects. Thirdly, compared with classic deterministic and static performance measurement models, the proposed performance analysis employs the Kalman-Filter approach to predict the final time and cost performance accurately by taking into account the probabilistic risk factors and the errors in the performance forecasting procedures. The validity of the integrated performance measurement model is justified based on a case study. The computational results demonstrate that the developed performance measurement framework affords more accurate forecasts for the future performance than the traditional deterministic earned value methodology. The integrated performance measurement model developed in this study affords probabilistic prediction bounds and generates less errors than those achieved in classic EVM.


Main Subjects

  1. ربیعی, ایمان و همکاران ()، «به‌کارگیری و مقایسة روش‌های پیش بینی جهت تخمین هزینة تکمیل پروژه در روش ارزش حاصله»، نشریة مهندسی صنایع، 1390، شمارة 45، صص 145-157.
  2. شکری، مهنوش؛ جهانگشای رضائی، مصطفی و ایزدبخش، حمیدرضا ()، «ارائة مدل جامع ارزیابی عملکرد در محیط رقابتی با رویکرد ترکیبی تحلیل پوششی داده‌ها، کارت امتیازی متوازن و تئوری بازی‌ها (مطالعة موردی: شرکت‌های سیمان)»، نشریة مهندسی صنایع، 1394، شمارة 49، صص 45-54.
    1. Neely, A., Gregory, M., and Platts, K. (1995). “Performance Measurement System Design: A Literature Review and Research Agenda”, International Journal of Operations and Production Management, Vol. 15, No. 4, PP. 80-116.
    2. Rubio, J., Muñoz, J., and Otegi, J. (2015). Engineering Projects Assessment Using Earned Value Management with Performance Indexes Evaluation and Statistical Methods, In Project Management and Engineering, Springer PP. 61-72.
    3. Vanhoucke, M., (2013). Earned Value Management, In Project Management With Dynamic Scheduling. 2013, Springer. P. 217-240.
    4. Fleming, Q. W., and Koppelman, J. M. (2000). Earned Value Project Management, Project Mangement Institute.
    5. Chen, H. L., Chen, W.T. and Lin, Y. L. (2016). “Earned Value Project Management: Improving the Predictive Power of Planned Value”, International Journal of Project Management, Vol. 34, No. 1, PP. 22-29.
    6. Wong, P.S., On Cheung, S., and C. (2007). “Hardcastle, Embodying Learning Effect in Performance Prediction”, Journal of Construction Engineering and Management, Vol. 6, No. 133, PP. 474-482.
    7. Badiru, A. B., and A. O. (2009). “Ijaduola, Half-Life Theory Of Learning Curves For System Performance Analysis”, Systems Journal, IEEE, Vol. 3, No. 2, PP. 154-165.
    8. Hinze, J., and Olbina, S. (2009). “Empirical Analysis of the Learning Curve Principle in Prestressed Concrete Piles”, Journal of Construction Engineering and Management, Vol. 5, No. 135, PP. 425-431.
    9. Farghal, S. H., and Everett, J. G. (1997). “Learning Curves: Accuracy in Predicting Future Performance”, Journal of Construction Engineering and Management, Vol. 1, No. 123, PP. 41-45.
    10. Anzanello, M. J., and Fogliatto, F. S. (2011). “Learning Curve Models And Applications: Literature Review and Research Directions”, International Journal of Industrial Ergonomics, Vol. 41, No. 5, PP. 573-583.
    11. Laihonen, H., (2015). “Performance Improvement in Twenty-First Century Organizations: Models, Tools, Techniques”, Measuring Business Excellence, Vol. 19, No. 3, PP. 1-8.
    12. Shah, A. H. (2014). Examining the Perceived Value of Integration of Earned Value Management with Risk Management-Based Performance Measurement Baseline, 2014, Capella University.
    13. Van Horenbeek, A., and Pintelon, L. (2014). “Development of a Maintenance Performance Measurement Framework—Using the Analytic Network Process (ANP) for Maintenance Performance Indicator Selection”, Omega, Vol. 42, No. 1, PP. 33-46.
    14. Yahanpath, N., and Islam, M. A Conceptual Framework to Incorporate'Risk Perspective'into the Balanced Scorecard: Towards a Sustainable Performance Measurement System. Available At SSRN 2474481, 2014.
    15. Malyusz, L., and Pem, A. (2014). “Predicting Future Performance by Learning Curves”, Procedia-Social and Behavioral Sciences, No. 119, PP. 368-376.
    16. Khamooshi, H., and Golafshani, H. (2014). “EDM: Earned Duration Management, a New Approach to Schedule Performance Management and Measurement”, International Journal of Project Management, Vol. 6, No. 32, PP. 1019-1041.
    17. Kim, B. C. (2015). “Probabilistic Evaluation of Cost Performance Stability in Earned Value Management”, Journal of Management in Engineering, Vol. 32, No. 1, PP. 401-502.
    18. Iranmanesh, S. H. and Hojati, Z. T. (2015). “Intelligent Systems in Project Performance Measurement and Evaluation”, In Intelligent Techniques in Engineering Management, Springer. PP. 581-619.
    19. Plaza, M., And Turetken, O. (2009). “A Model-Based DSS for Integrating the Impact of Learning in Project Control”, Decision Support Systems, Vol. 47, No. 4, PP. 488-499.
    20. Wilson, B., Frolick, M., and Ariyachandra, T. (2013). “Earned Value Management Systems: Challengs And Future Direction”, Journal of Integrated Enterprise Systems, Vol. 4. No. 1, PP. 1-9.
    21. Jaber, M. Y. (2011). Learning Curves: Theory, Models, and Applications, CRC Press.
    22. Adler, P. S., and Clark, K. B. (1991). “Behind the Learning Curve: A Sketch of the Learning Process”, Management Science, Vol. 37, No. 3, PP. 267-281.
    23. Argote, L., (1996). “Organizational Learning Curves: Persistence, Transfer And Turnover”, International Journal of Technology Management, Vol. 11, No. 7 and 8 PP. 759-769.
    24. Paliwal, K., KALMAN FILTERING. 1987.
    25. Siu, N. (1994). “Risk Assessment for Dynamic Systems: An Overview”, Reliability Engineering & System Safety, Vol. 1, No. 1, PP. 43-73.
    26. Juang, J. N. et al. (1993). “Identification of Observer/Kalman Filter Markov Parameters-Theory and Experiments. Journal of Guidance”, Control, and Dynamics,Vol. 16, No. 2, PP. 320-329.
    27. Kalman, R. E. (1960). “A New Approach to Linear Filtering and Prediction Problems”, Journal of Basic Engineering, Vol. 1, No. 82, PP.33-45.
    28. Kim, B. C., and Reinschmidt, K. F. (2010). “Probabilistic Forecasting of Project Duration Using Kalman Filter and the Earned Value Method”, Journal of Construction Engineering and Management, Vol. 8, No. 136, PP. 834-843.
    29. De Marco, A., and Narbaev, T. (2013). “Earned Value-Based Performance Monitoring of Facility Construction Projects”, Journal of Facilities Management, Vol. 11, No. 1, PP. 69-80.
    30. De Marco, A., Briccarello, D., and Rafele, C. (2009). “Cost and Schedule Monitoring of Industrial Building Projects: Case Study”, Journal of Construction Engineering and Management, Vol. 9, No. 135, PP. 853-862.
    31. Hammad, M. W., Abbasi, A., and Ryan, M. J. (2016). “Developing a Novel Framework to Manage Schedule Contingency Using Theory of Constraints and Earned Schedule Method”, Journal of Construction Engineering and Management, Vol. 144, No. 4, PP. 18-40.
    32. Nasr, W., Yassine, A., and Kasm, O. A. (2016). “An Analytical Approach to Estimate the Expected Duration and Variance for Iterative Product Development Projects”, Research in Engineering Design, Vol. 1, No. 27, PP. 55-71.