Abstract
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 via an institution to check access.
Similar content being viewed by others
References
Abba WF (1997) Earned value management-reconciling government and commercial practices. Program Manager 26:58–63
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
APM (2012) Association for project management body of knowledge. Imprint Digital, Buckinghamshire, UK, 162–168
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
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
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
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
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
Kalekar PS (2004) Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi School of Information Technology 4329008(13):1–13
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
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
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
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
Montgomery DC, Johnson LA, Sazonova T (1990) Forecasting and time series analysis. McGraw-Hill, New York, NY, USA, 138–156
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
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
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
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
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
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
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.NRF-2018R1A5A1025137).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sackey, S., Lee, DE. & Kim, BS. 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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-020-0407-5