Practical Application and Empirical Evaluation of Reference Class Forecasting for Project Management

Traditionally, project managers produce cost and time forecasts by predicting the future course of specific events. In contrast, reference class forecasting (RCF) bypasses human judgment by basing forecasts on the actual outcomes of past projects similar to the project being forecasted. The RCF technique is compared with the most common traditional project forecasting methods, such as those based on Monte Carlo simulation and earned value management (EVM). The conducted evaluation is entirely based on real-life project data and shows that RCF indeed performs best, for both cost and time forecasting, and therefore supports the practical relevance of the technique.

[1]  Mario Vanhoucke,et al.  Classification of articles and journals on project control and earned value management , 2015 .

[2]  Mario Vanhoucke,et al.  Empirical Evaluation of Earned Value Management Forecasting Accuracy for Time and Cost , 2015 .

[3]  D. Kahneman,et al.  Delusions of success. How optimism undermines executives' decisions. , 2003, Harvard business review.

[4]  A. Tversky,et al.  Intuitive Prediction: Biases and Corrective Procedures , 1982 .

[5]  Mario Vanhoucke,et al.  Construction and evaluation framework for a real-life project database , 2015 .

[6]  Walt Lipke,et al.  Project Duration Forecasting … A Comparison of Earned Value Management Methods to Earned Schedule , 2009 .

[7]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[8]  Paul Teicholz.  Forecasting Final Cost and Budget of Construction Projects , 1993 .

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

[10]  Mario Vanhoucke,et al.  Empirical perspective on activity durations for project management simulation studies , 2016 .

[11]  Bent Flyvbjerg,et al.  From Nobel Prize to Project Management: Getting Risks Right , 2006, ArXiv.

[12]  Kenneth R. Baker,et al.  Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation , 2012, Eur. J. Oper. Res..

[13]  Søren L. Buhl,et al.  Underestimating Costs in Public Works Projects: Error or Lie? , 2002, 1303.6604.

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

[15]  Roger D. H. Warburton.  A time-dependent earned value model for software projects , 2011 .

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

[17]  Daniel Kahneman.  New challenges to the rationality assumption , 1994 .

[18]  J. Scott Armstrong,et al.  Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners , 1982 .

[19]  Byung-Cheol Kim,et al.  Combination of Project Cost Forecasts in Earned Value Management , 2011 .

[20]  Søren L. Buhl,et al.  How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation , 2005, 1303.6654.

[21]  Bent Flyvbjerg.  Eliminating bias in early project development through reference class forecasting and good governance , 2007 .

[22]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[23]  Franco Caron,et al.  A Bayesian Approach to Improve Estimate at Completion in Earned Value Management , 2013 .

[24]  Martin Wachs,et al.  Ethics and Advocacy in Forecasting for Public Policy , 1990 .