On the dynamic use of project performance and schedule risk information during projecttracking

Project scheduling, risk analysis and project tracking are key parameters to a project's success or failure. Research on the relative sensitivity of project activities during the project scheduling phase as well as research on project performance measurement during project progress have been published throughout the academic literature and the popular press. Obviously, the interest in activity sensitivity information and project performance measurement from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has knowledge about the current project performance and has a certain feeling of the relative sensitivity of the various project activities on the project objective, a better management focus and a more accurate response during project tracking should positively contribute to the overall performance of the project. In this article, two alternative project tracking methods to detect project problems are presented and their efficiency on the quality of corrective actions to bring the project back on track is measured and evaluated. More precisely, a bottom-up and a top-down project tracking approach within a corrective action framework is applied on a large and diverse set of fictitious projects that are subject to Monte-Carlo simulations to simulate fictitious project progress under uncertainty. The top-down tracking approach relies on state-of-the-art earned value management performance metrics, while the bottom-up tracking mechanism makes use of the well-known schedule risk analysis method. A computational experiment shows that a top-down project tracking approach is highly efficient for project networks with a serial activity structure while a bottom-up approach performs better in a parallel structured project network. Moreover, it will also be shown that dynamic thresholds to trigger corrective actions, which gradually increase or decrease the project manager's attention along the project progress, outperform the static thresholds for both tracking approaches.

[1]  Mario Vanhoucke,et al.  A simulation and evaluation of earned value metrics to forecast the project duration , 2005, J. Oper. Res. Soc..

[2]  Mario Vanhoucke,et al.  Measuring the accuracy of earned value/earned schedule forecasting predictors , 2007 .

[3]  Quentin W. Fleming,et al.  Earned Value Project Management , 1996 .

[4]  Salah E. Elmaghraby.  On criticality and sensitivity in activity networks , 2000, Eur. J. Oper. Res..

[5]  L. V. Tavares,et al.  The risk of delay of a project in terms of the morphology of its network , 1999, Eur. J. Oper. Res..

[6]  Simaan M. AbouRizk,et al.  FITTING BETA DISTRIBUTIONS BASED ON SAMPLE DATA , 1994 .

[7]  F.T. Anbari,et al.  Earned Value Project Management Method and Extensions , 2003, IEEE Engineering Management Review.

[8]  Yahya Fathi,et al.  On the sensitivity of project variability to activity mean duration , 1999 .

[9]  Erik Demeulemeester,et al.  RanGen: A Random Network Generator for Activity-on-the-Node Networks , 2003, J. Sched..

[10]  B. Yum,et al.  An uncertainty importance measure of activities in PERT networks , 1997 .

[11]  Mario Vanhoucke,et al.  Earned value forecast accuracy and activity criticality , 2008 .

[12]  Emily K. Lada,et al.  Introduction to Modeling and Generating Probabilistic Input Processes for Simulation , 2006, Proceedings of the 2006 Winter Simulation Conference.

[13]  Ian N. Durbach,et al.  Using expected values to simplify decision making under uncertainty , 2009 .

[14]  Mario Vanhoucke,et al.  Using activity sensitivity and network topology information to monitor project time performance , 2010 .

[15]  Terry Williams.  Criticality in Stochastic Networks , 1992 .

[16]  Mario Vanhoucke,et al.  An evaluation of the adequacy of project network generators with systematically sampled networks , 2008, Eur. J. Oper. Res..

[17]  Anand Paul,et al.  Analysis of the Effects of Uncertainty, Risk-Pooling, and Subcontracting Mechanisms on Project Performance , 2000, Oper. Res..

[18]  Terry Williams.  Towards realism in network simulation , 1999 .

[19]  Genaro J. Gutierrez,et al.  Parkinson's Law and Its Implications for Project Management , 1991 .

[20]  A. R. Klingel,et al.  Bias in Pert Project Completion Time Calculations for a Real Network , 1966 .

[21]  Mario Vanhoucke,et al.  Measuring Time: Improving Project Performance Using Earned Value Management , 2009 .

[22]  Richard J. Schonberger,et al.  Why Projects Are “Always” Late: A Rationale Based on Manual Simulation of a PERT/CPM Network , 1981 .

[23]  Ofer Zwikael,et al.  Prediction of project outcome The Application of statistical methods to earned value management and earned schedule performance indexes , 2009 .

[24]  Mario Vanhoucke,et al.  A comparison of different project duration forecasting methods using earned value metrics , 2006 .