Project regularity: Development and evaluation of a new project characteristic

Article

Abstract

The ability to accurately characterize projects is essential to good project management. Therefore, a novel project characteristic is developed that reflects the value accrue within a project. This characteristic, called project regularity, is expressed in terms of the newly introduced regular/irregular-indicator RI. The widely accepted management system of earned value management (EVM) forms the basis for evaluation of the new characteristic. More concretely, the influence of project regularity on EVM forecasting accuracy is assessed, and is shown to be significant for both time and cost forecasting. Moreover, this effect appears to be stronger than that of the widely used characteristic of project seriality expressed by the serial/parallel-indicator SP. Therefore, project regularity could also be useful as an input parameter for project network generators. Furthermore, the introduction of project regularity can provide project managers with a more accurate indication of the time and cost forecasting accuracy that is to be expected for a certain project and, correspondingly, of how a project should be built up in order to obtain more reliable forecasts during project control.

Keywords

Project management earned value management time and cost forecasting empirical database Monte Carlo simulation project control system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We acknowledge the support provided by the “Nationale Bank van België” (NBB) and by the “Bijzonder Onderzoeksfonds” (BOF) for the project with contract number BOF12GOA021. Furthermore, we would also like to thank Gilles Bonne, Eveline Hoogstoel and Gilles Vandewiele for their efforts in developing PMConverter. And last but not least, we would like to express our gratitude towards the anonymous referees who helped to improve the quality of this paper through their constructive comments and valuable suggestions.

References

  1. [1]
    Agrawal, M., Elmaghraby, S. & Herroelen, W. (1996). DAGEN: a generator of testsets for project activity nets. European Journal of Operational Research, 90: 376–382.CrossRefMATHGoogle Scholar
  2. [2]
    Anbari, F. (2003). Earned value project management method and extensions. Project Management Journal, 34: 12–23.Google Scholar
  3. [3]
    Batselier, J. & Vanhoucke, M. (2015a). Construction and evaluation framework for a real-life project database. International Journal of Project Management, 33: 697–710.CrossRefGoogle Scholar
  4. [4]
    Batselier, J. & Vanhoucke, M. (2015b). Empirical evaluation of earned value management forecasting accuracy for time and cost. Journal of Construction Engineering and Management, 141: 05015010.CrossRefGoogle Scholar
  5. [5]
    Christensen, D. (1998). The costs and benefits of the earned value management process. Acquisition Review Quarterly, Fall: 373–386.Google Scholar
  6. [6]
    Cioffi, D. (2005). A tool for managing projects: an analytic parameterization of the S-curve. International Journal of Project Management, 23: 215–222.CrossRefGoogle Scholar
  7. [7]
    Cioffi, D. (2006). Completing projects according to plans: an earned-value improvement index. Journal of the Operational Research Society, 57: 290–295.CrossRefMATHGoogle Scholar
  8. [8]
    Demeulemeester, E., Vanhoucke, M. & Herroelen, W. (2003). RanGen: a random network generator for activity-on-the-node networks. Journal of Scheduling, 6: 17–38.MathSciNetCrossRefMATHGoogle Scholar
  9. [9]
    Egnot, J. (2011). Earned progress management-a unified theory of earned value & earned schedule concepts. The Measurable News, Issue 4: 8–24.Google Scholar
  10. [10]
    Elshaer, R. (2012). Impact of sensitivity information on the prediction of project’s duration using earned schedule method. International Journal of Project Management, 31: 579–588.CrossRefGoogle Scholar
  11. [11]
    Fleming, Q. & Koppelman, J. (2010). Earned Value Project Management (4th ed). Project Management Institute, Newtown Square, Pennsylvania.Google Scholar
  12. [12]
    Hall, N. (2012). Project management: recent developments and research opportunities. Journal of Systems Science and Systems Engineering, 21: 129–143.CrossRefGoogle Scholar
  13. [13]
    Henderson, K. (2007). Earned schedule: a breakthrough extension to earned value management. In: PMI Asia Pacific Global Congress Proceedings, Hong Kong.Google Scholar
  14. [14]
    Jacob, D. & Kane, M. (2004). Forecasting schedule completion using earned value metrics revisited. The Measurable News, Summer: 1, 11–17.Google Scholar
  15. [15]
    Kao, E. & Queyranne, M. (1982). On dynamic programming methods for assembly line balancing. Operations Research, 30: 375–390.CrossRefMATHGoogle Scholar
  16. [16]
    Kim, E., Wells, W. & Duffey, M. (2003). A model for effective implementation of earned value management methodology. International Journal of Project Management, 21: 375–382.CrossRefGoogle Scholar
  17. [17]
    Kolisch, R., Sprecher, A. & Drexl, A. (1995). Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science, 41: 1693–1703.CrossRefMATHGoogle Scholar
  18. [18]
    Lipke, W. (2003). Schedule is different. The Measurable News, Summer: 31–34.Google Scholar
  19. [19]
    Marshall, R. (2007). The contribution of earned value management to project success on contracted efforts. Journal of Contract Management, Summer: 21–33.Google Scholar
  20. [20]
    Mastor, A. (1970). An experimental and comparative evaluation of production line balancing techniques. Management Science, 16: 728–746.CrossRefMATHGoogle Scholar
  21. [21]
    Meredith, J. & Mantel, S. (2003). Project Management: A Managerial Approach (5th ed.). Wiley, New York.Google Scholar
  22. [22]
    PMI (2008). A Guide to the Project Management Body of Knowledge (PMBOK Guide) (3rd ed). Project Management Institute, Newtown Square, Pennsylvania.Google Scholar
  23. [23]
    Rujirayanyong, T. (2009). A comparison of three completion date predicting methods for construction projects. Journal of Research in Engineering and Technology, 6: 305–318.Google Scholar
  24. [24]
    Schwindt, C. (1995). A new problem generator for different resource-constrained project scheduling problems with minimal and maximal time lags. WIOR-Report-449. Institut für Wirtschaftstheorie und Operations Research, University of Karlsruhe.Google Scholar
  25. [25]
    Tavares, L. (1999). Advanced Models for Project Management. Kluwer Academic Publishers, Dordrecht.CrossRefGoogle Scholar
  26. [26]
    Tavares, L., Ferreira, J. & Coelho, J. (1999). The risk of delay of a project in terms of the morphology of its network. European Journal of Operational Research, 119: 510–537.CrossRefMATHGoogle Scholar
  27. [27]
    Van De Velde, R. (2007). Time is up: assessing schedule performance with earned value. PM World Today, 9: 1–10.Google Scholar
  28. [28]
    Vandevoorde, S. & Vanhoucke, M. (2006). A comparison of different project duration forecasting methods using earned value metrics. International Journal of Project Management, 24: 289–302.CrossRefGoogle Scholar
  29. [29]
    Vanhoucke, M. (2010a). Measuring time-improving project performance using earned value management. Volume 136 of International Series in Operations Research and Management Science. Springer.MATHGoogle Scholar
  30. [30]
    Vanhoucke, M. (2010b). Using activity sensitivity and network topology information to monitor project time performance. Omega-The International Journal of Management Science, 38: 359–370.CrossRefGoogle Scholar
  31. [31]
    Vanhoucke, M. (2011). On the dynamic use of project performance and schedule risk information during project tracking. Omega-The International Journal of Management Science, 39: 416–426.CrossRefGoogle Scholar
  32. [32]
    Vanhoucke, M. (2012a). Measuring the efficiency of project control using fictitious and empirical project data. International Journal of Project Management, 30: 252–263.CrossRefGoogle Scholar
  33. [33]
    Vanhoucke, M. (2012b). Project Management with Dynamic Scheduling: Baseline Scheduling, Risk Analysis and Project Control. Volume XVIII. Springer.CrossRefGoogle Scholar
  34. [34]
    Vanhoucke, M. (2014). Integrated project management and control: first comes the theory, then the practice. Management for Professionals, Springer.CrossRefGoogle Scholar
  35. [35]
    Vanhoucke, M., Coelho, J., Debels, D., Maenhout, B. & Tavares, L. (2008). An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research, 187: 511–524.CrossRefMATHGoogle Scholar
  36. [36]
    Vanhoucke, M. & Vandevoorde, S. (2007). A simulation and evaluation of earned value metrics to forecast the project duration. Journal of the Operational Research Society, 58: 1361–1374.CrossRefMATHGoogle Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Faculty of Economics and Business AdministrationGhent UniversityGhentBelgium
  2. 2.Technology and Operations Management AreaVlerick Business SchoolGhentBelgium
  3. 3.UCL School of ManagementUniversity College LondonLondonUK

Personalised recommendations