Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Duration Estimate at Completion: Improving Earned Value Management Forecasting Accuracy

  • 24 Accesses


Earned Value Management (EVM) has been established as a project management technique for project monitoring and control. The traditional EVM performs well in forecasting Cost Performance index and other cost metrics. However, in terms of schedule performance, the accuracy of the forecasted schedule metrics through the traditional EVM approach are always questionable. The schedule performance is not measured in time unit but rather in monetary units or uses cost information, which may cause misleading in the interpretation of the EVM schedule metrics. The schedule performance is not accurately forecasted, resulting in underestimating the estimate at completion (t). Even the renowned Earned Schedule also uses cost as a proxy to determine the earned schedule. This paper presents a new EVM tool, Duration estimate at completion (DEAC-model) developed to accurately forecast the time estimate at completion. DEAC-model uses the actual time spent on each activity, either in progress or upon completion, where the Performance is measured in time units. The benefits of DEAC-model to project management team and researchers are that it can be used: 1) to forecast schedule metrics accurately so that resources can be effectively allocated to complete the remaining activities, 2) as a gauge to assess if the project can be completed within the plan schedule, and 3) to apply time series and simple linear regression model concepts using excel worksheet syntax to forecast duration estimate at completion that is easily applicable.

This is a preview of subscription content, log in to check access.


  1. Abba WF (1997) Earned value management-reconciling government and commercial practices. Program Manager 26:58–63

  2. Acebes F, Pereda M, Poza D, Pajares J, Galán JM (2015) Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques. International Journal of Project Management 33(7):1597–1609, DOI: https://doi.org/10.1016/j.ijproman.2015.06.012

  3. APM (2012) Association for project management body of knowledge. Imprint Digital, Buckinghamshire, UK, 162–168

  4. Chen HL (2014) Improving forecasting accuracy of project earned value metrics: Linear modeling approach. Journal of Management in Engineering 30(2):135–145, DOI: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000187

  5. Chen HL, Chen WT, Lin YL (2016) Earned value project management: improving the predictive power of planned value. International Journal of Project Management 34(1):22–29, DOI: https://doi.org/10.1016/j.ijproman.2015.09.008

  6. Cheng MY, Peng HS, Wu YW, Chen TL (2010). Estimate at completion for construction projects using evolutionary support vector machine inference model. Automation in Construction 19(5):619–629, DOI: https://doi.org/10.1016/j.autcon.2010.02.008

  7. Colin J, Vanhoucke M (2014) Setting tolerance limits for statistical project control using earned value management. Omega 49:107–122, DOI: https://doi.org/10.1016/j.omega.2014.06.001

  8. Czemplik A (2014) Application of earned value method to progress control of construction projects. Procedia Engineering 91:424–428, DOI: https://doi.org/10.1016/j.proeng.2014.12.087

  9. Kalekar PS (2004) Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi School of Information Technology 4329008(13):1–13

  10. Kerkhove LP, Vanhoucke M (2017) Extensions of earned value management: Using the earned incentive metric to improve signal quality. International Journal of Project Management 35(2):148–168, DOI: https://doi.org/10.1016/j.ijproman.2016.10.014

  11. Khandare Manish A, Vyas Gayatri S (2012) Project duration forecasting using earned value method and time series. International Journal of Engineering and Innovative Technology 1(4):218–224

  12. Lipke W, Zwikael O, Henderson K, Anbari F (2016) 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, DOI: https://doi.org/10.1016/j.ijproman.2008.02.009

  13. Mishakova A, Vakhrushkina A, Murgul V, Gardiner AS (2016) Project control based on a mutual application of pert and earned value management methods. Procedia Engineering 165:1812–1817, DOI: https://doi.org/10.1016/j.proeng.2016.11.927

  14. Montgomery DC, Johnson LA, Sazonova T (1990) Forecasting and time series analysis. McGraw-Hill, New York, NY, USA, 138–156

  15. Naeni LM, Shadrokh S, Salehipour A (2011) A fuzzy approach for the earned value management. International Journal of Project Management 29(6):764–772, DOI: https://doi.org/10.1016/j.ijproman.2010.07.012

  16. Najafi A, Azimi F (2016) An extension of the earned value management to improve the accuracy of schedule analysis results. Iranian Journal of Management Studies 9(1):63–75, DOI: https://doi.org/10.22059/ijms.2016.55035

  17. Ostertagová E, Ostertag O (2011) The simple exponential smoothing model. The 4th international conference on modelling of mechanical and mechatronic systems, Technical University of Košice, Herl’any, Slovak Republic, 380–384

  18. Tratar LF, Mojškerc B, Toman A (2016) Demand forecasting with four-parameter exponential smoothing International Journal of Production Economics 181(Part A):162–173, DOI: https://doi.org/10.1016/j.ijpe.2016.08.004

  19. Vandevoorde S, Vanhoucke M (2006) A comparison of different project duration forecasting methods using earned value metrics. International Journal of Project Management 24(4):289–302, DOI: https://doi.org/10.1016/j.ijproman.2005.10.004

  20. Willems LL, Vanhoucke M (2015) Classification of articles and journals on project control and earned value management. International Journal of Project Management 33(7):1610–1634, DOI: https://doi.org/10.1016/j.ijproman.2015.06.003

Download references


This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.NRF-2018R1A5A1025137).

Author information

Correspondence to Byung-Soo Kim.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sackey, S., Lee, D. & Kim, B. Duration Estimate at Completion: Improving Earned Value Management Forecasting Accuracy. KSCE J Civ Eng 24, 693–702 (2020). https://doi.org/10.1007/s12205-020-0407-5

Download citation


  • Earned value management
  • Forecasting
  • Performance measurement
  • Project duration
  • Earned schedule