Author
Listed:
- Majid Damavandi
(University of Tehran)
- Mahdieh Tavakoli
(University of Tehran)
- Fariborz Jolai
(University of Tehran)
Abstract
The most issue in project management is estimating the required budget for the project. Today, despite all the progress and relative achievement of forecasting models, project managers face many problems in financial and planning which sometimes lead to non-completion and abandonment. In recent years, several methods have been presented to estimate the cost of projects, but the uncertainty and ambiguities in projects are rarely taken into account while some studies emphasized that the actual cost of a project is independent of time and varies randomly under various uncertainties. Therefore, in this study, a framework for project cost forecasting considering the uncertainty is proposed using earned value management concepts combined with Markov chain concepts. In fact, it has been tried to use the concepts of the Markov chain to involve the uncertainty in project cost forecasting, so that the limitations of the traditional methods could be reduced. Finally, in order to compare the efficiency of the proposed method and traditional methods, the values obtained with the total final cost in a large project in a case study have been presented and evaluated. The results show that the use of this method on average estimates the final cost of the project more than 50% higher and more realistic than the estimate made by classical formulas.
Suggested Citation
Majid Damavandi & Mahdieh Tavakoli & Fariborz Jolai, 2025.
"Project cost forecasting based on earned value management and Markov chain,"
Annals of Operations Research, Springer, vol. 351(1), pages 1023-1048, August.
Handle:
RePEc:spr:annopr:v:351:y:2025:i:1:d:10.1007_s10479-024-05889-7
DOI: 10.1007/s10479-024-05889-7
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:351:y:2025:i:1:d:10.1007_s10479-024-05889-7. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.