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Anastasopoulos, P C, Labi, S, Bhargava, A, Bordat, C and Mannering, F L (2010) Frequency of Change Orders in Highway Construction Using Alternate Count-Data Modeling Methods. Journal of Construction Engineering and Management, 136(08), 886–93.
El Asmar, M, Lotfallah, W, Whited, G and Hanna, A S (2010) Quantitative Methods for Design-Build Team Selection. Journal of Construction Engineering and Management, 136(08), 904–12.
Ji, S, Park, M and Lee, H (2010) Data Preprocessing–Based Parametric Cost Model for Building Projects: Case Studies of Korean Construction Projects. Journal of Construction Engineering and Management, 136(08), 844–53.
Kent, D C and Becerik-Gerber, B (2010) Understanding Construction Industry Experience and Attitudes toward Integrated Project Delivery. Journal of Construction Engineering and Management, 136(08), 815–25.
Kim, B and Reinschmidt, K F (2010) Probabilistic Forecasting of Project Duration Using Kalman Filter and the Earned Value Method. Journal of Construction Engineering and Management, 136(08), 834–43.
- Type: Journal Article
- Keywords: Forecasting; Scheduling; Kalman filters; Construction management; Forecasting; Scheduling; Kalman filter; Project controls; Schedule management; Construction management;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000192
- 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.
Korkmaz, S, Riley, D and Horman, M (2010) Piloting Evaluation Metrics for Sustainable High-Performance Building Project Delivery. Journal of Construction Engineering and Management, 136(08), 877–85.
Lai, A W Y and Pang, P S M (2010) Measuring Performance for Building Maintenance Providers. Journal of Construction Engineering and Management, 136(08), 864–76.
Mostafavi, A and Karamouz, M (2010) Selecting Appropriate Project Delivery System: Fuzzy Approach with Risk Analysis. Journal of Construction Engineering and Management, 136(08), 923–30.
Nguyen, L D and Ibbs, W (2010)  Case Law and Variations in Cumulative Impact Productivity Claims. Journal of Construction Engineering and Management, 136(08), 826–33.
Xu, Y, Chan, A P C and Yeung, J F Y (2010) Developing a Fuzzy Risk Allocation Model for PPP Projects in China. Journal of Construction Engineering and Management, 136(08), 894–903.
Zheng, S and Tiong, R L K (2010) First Public-Private-Partnership Application in Taiwan’s Wastewater Treatment Sector: Case Study of the Nanzih BOT Wastewater Treatment Project. Journal of Construction Engineering and Management, 136(08), 913–22.
Zou, P X W, Chen, Y and Chan, T (2010) Understanding and Improving Your Risk Management Capability: Assessment Model for Construction Organizations. Journal of Construction Engineering and Management, 136(08), 854–63.