Probabilistic Forecasting of Project Duration Using Kalman Filter and the Earned Value Method
Publication: Journal of Construction Engineering and Management
Volume 136, Issue 8
Abstract
The earned value method (EVM) is recognized as a viable method for evaluating and forecasting project cost performance. However, its application to schedule performance forecasting has been limited due to poor accuracy in predicting project durations. Recently, several EVM-based schedule forecasting methods were introduced. However, these are still deterministic and have large prediction errors early in the project due to small sample size. In this paper, a new forecasting method is developed based on Kalman filter and the earned schedule method. The Kalman filter forecasting method (KFFM) provides probabilistic predictions of project duration at completion and can be used from the beginning of a project without significant loss of accuracy. KFFM has been programmed in an add-in for Microsoft Excel and it can be implemented on all kinds of projects monitored by EVM or any other S-curve approach. Applications on two real projects are presented here to demonstrate the advantages of KFFM in extracting additional information from data about the status, trend, and future project schedule performance and associated risks.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ansley, C. F., and Kohn, R. (1985). “Estimation, filtering, and smoothing in state space models with incompletely specified initial conditions.” Ann. Stat., 13(4), 1286–1316.
Awwad, H. M., Valdes, J. B., and Restrepo, P. J. (1994). “Streamflow forecasting for Han River basin, Korea.” J. Water Resour. Plann. Manage., 120(5), 651–673.
Barraza, G. A., Back, W. E., and Mata, F. (2000). “Probabilistic monitoring of project performance using SS-curves.” J. Constr. Eng. Manage., 126(2), 142–148.
Barraza, G. A., Back, W. E., and Mata, F. (2004). “Probabilistic forecasting of project performance using stochastic S curves.” J. Constr. Eng. Manage., 130(1), 25–32.
Brookner, E. (1998). Tracking and Kalman filtering made easy, Wiley, New York.
Casals, J., Jerez, M., and Sotoca, S. (2000). “Exact smoothing for stationary and non-stationary time series.” Int. J. Forecast., 16(1), 59–69.
Christensen, D. S., and Heise, S. R. (1993). “Cost performance index stability.” National Contract Management Journal, 25(1), 7–15.
de Jong, P. (1989). “Smoothing and interpolation with the state-space model.” J. Am. Stat. Assoc., 84(408), 1085–1088.
Fleming, Q. W., and Koppelman, J. M. (2006). Earned value project management, 3rd Ed., Project Management Institute, Newtown Square, Pa.
Grewal, M. S., and Andrews, A. P. (2001). Kalman filtering: Theory and practice using MATLAB, 2nd Ed., Wiley, New York.
Henderson, K. (2003). “Earned schedule: A breakthrough extension to earned value theory? A retrospective analysis of real project data.” The Measurable News, Summer, 13–17, 21–23.
Henderson, K. (2004). “Further developments in earned schedule.” The Measurable News, Spring, 15–16, 20–22.
Henderson, K. (2005). “Earned schedule in action.” The Measurable News, Spring, 23–28, 30.
Kalman, R. E. (1960). “A new approach to linear filtering and prediction problems.” J. Basic Eng., 82 (Series D), 35–45.
Keefer, D. L., and Bodily, S. E. (1983). “Three-point approximations for continuous random variables.” Manage. Sci., 29(5), 595–609.
Kim, B. C. (2007). “Forecasting project progress and early warning of project overruns with probabilistic methods.” Ph.D. dissertation, Texas A&M Univ., College Station, Tex.
Leach, L. (2005). “EVM & critical chain project management.” Proc., NASA Project Management Challenge 2005, Houston.
Lee, D. -E. (2005). “Probability of project completion using stochastic project scheduling simulation.” J. Constr. Eng. Manage., 131(3), 310–318.
Lipke, W. H. (2003). “Schedule is different.” The Measurable News, Summer, 31–34.
Lipke, W. H. (2006). “Earned schedule leads to improved forecasting.” Proc., 3rd Int. Conf. on Project Management (ProMAC 2006), Sydney, Australia.
Payne, K. I. (1990). “An investigation of the stability of the cost performance index.” MS thesis, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.
Perry, C., and Greig, I. D. (1975). “Estimating the mean and variance of subjective distributions in PERT and decision analysis.” Manage. Sci., 21(12), 1477–1480.
Project Management Institute. (2008). A guide to the project management body of knowledge, 4th Ed., Newtown Square, Pa.
Seiler, J. (1985). “Cost and schedule data analysis and forecasting.” Int. J. Proj. Manage., 3(1), 45–49.
Sen, Z., Altunkaynak, A., and Ozger, M. (2004). “Sediment concentration and its prediction by perceptron Kalman filtering procedure.” J. Hydraul. Eng., 130(8), 816–826.
Short, J. W. (1993). “Using schedule variance as the only measure of schedule performance.” Cost Eng. 35(10), 35–40.
Vandevoorde, S., and Vanhoucke, M. (2006). “A comparison of different project duration forecasting methods using earned value metrics.” Int. J. Proj. Manage., 24(4), 289–302.
Vanhoucke, M., and Vandevoorde, S. (2007). “A simulation and evaluation of earned value metrics to forecast the project duration.” J. Oper. Res. Soc., 58(10), 1361–1374.
Wang, C. -H., and Bai, Y. -L. (2008). “Algorithm for real time correction of stream flow concentration based on Kalman filter.” J. Hydrol. Eng., 13(5), 290–296.
Welch, G., and Bishop, G. (2001). An introduction to Kalman filter, Univ. of North Carolina, Chapel Hill, N.C.
Zarchan, P., and Musoff, H. (2000). Fundamentals of Kalman filtering: A practical approach (progress in astronautics and aeronautics), American Institute of Aeronautics and Astronautics, Inc. (AIAA), Reston, Va.
Zwikael, O., Globerson, S., and Raz, T. (2000). “Evaluation of models for forecasting the final cost of a project.” Proj. Manage. J., 31(1), 53–57.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 136 • Issue 8 • August 2010
Pages: 834 - 843
Copyright
© 2010 ASCE.
History
Received: Oct 17, 2008
Accepted: Jan 15, 2010
Published online: Jan 22, 2010
Published in print: Aug 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.