Bayesian Integration of Internal and External Views in Forecasting Project Performance
Keywords:
Project control, Estimate at Completion, Bayesian inference, Oil and Gas industryAbstract
This paper focuses on the project control process and aims at improving the accuracy of the Estimate at Completion at Time Now, both in terms of cost and time. This objective requires the use of all the available information, in particular the information related to the actual performance of the current project, corresponding to the “internal view”, and the information related to the cluster of similar projects completed in the past, corresponding to the “external view”. In order to integrate both types of information, a Bayesian model has been developed, allowing for the updating of a prior estimate based on the external view by means of the data records collected during the progress of the current project, in order to obtain a posterior estimate of the final cost and duration of the project. This approach allows for the mitigation of possible biases which can affect the project control process, particularly at the project early stage. The Bayesian model has been applied to three cases in the Oil and Gas industry. Notwithstanding the great difference between the projects, the integration of the internal and external views in the Bayesian model resulted in a better accuracy compared to the traditional formulas used in the Earned Value Management approach and, moreover, a better stability of the estimates from the early stage along the entire life cycle of the project.
References
Amabile, T. M. (1985), “Motivation and creativity: effects of motivational orientation on creative writers”, Journal of Personality and Social Psychology, Vol.48, No.2, pp. 393-399.
Anbari, F. (2003), “Earned value project management method and extensions”, Project Management Journal, Vol. 34, No. 4, pp. 12-23.
Bar-Hillel, M. (1973), “On the subjective probability of compound events”, Organizational Behavior and Human Performance, Vol. 9, No.3, pp. 396-406.
Barraza, G.A., Back, W.E., Mata, F. (2000), “Probabilistic Monitoring of Project Performance Using SS-Curves”, Journal of Construction Engineering and Management, Vol.126, No.2, pp.142-148.
Birgit, W. (2011), “Perspectives on optimism within the context of project management: a call for multilevel research”, IMB Institute of Management Berlin, Vol. 59.
Buehler, R. and Griffin, D. (2003), “Planning, personality and prediction: the role of future focus in optimistic time predictions”, Organizational Behavior and Human Decision Processes, Vol. 92, pp. 80-90.
Buehler, R., Griffin, D. and Ross, M. (1994), “Exploring the “Planning Fallacy”: why people underestimate their task completion times”, Journal of Personality and Social Psychology, Vol. 67, No.3, pp. 366-381.
Caron, F. (2013), Managing the continuum: certainty, uncertainty, unpredictability in large engineering projects, Springer
Christensen, D. (1996), “Project Advocacy and the estimate at completion problem”, Journal of Cost Analysis and Management, Spring 1996, pp. 35-60.
Coget, J.F. and Keller F. (2010), “The critical decisions vortex: Lessons from the emergency room”, Journal of Management Inquiry, Vol. 19, No. 1, pp. 56-67.
Communities and local government: London (2007), “Adjusting for optimism bias in regeneration projects and programs”, available at: http://www.communities.gov.uk (accessed 15 October 2011).
D’Agostini, G. (1999), “Overcoming priors anxiety”, in Bayesian Methods in the Sciences, ed. Bernardo, J.M, Revista de la Real Academia de Ciencias, Madrid, Vol.33, No.3.
Christensen, D.S. (1998), “The costs and benefits of the earned value management process”, Acquisition Review Quarterly, Vol.3, No.1, pp. 373-385.
De Finetti, B. (1974), Theory of Probability, John Wiley & Sons.
Demenlemeester, E.L. and Herroelen, W. (2002), Project Scheduling: A Research Handbook, Kluwer’s International Series, USA.
El Sabban, Z. (1973), “Forecast of cost/schedule status utilizing cost performance reports of the Cost/Schedule Control Systems Criteria: a Bayesian approach”, US Army Aviation Systems Command, St. Louis Missouri, AD-754576.
Eroglu, C. and Croxton, K.L. (2010), “ Biases in judgmental adjustments of statistical forecasts: the role of individual differences”, International Journal of Forecasting, Vol. 26, pp. 116-133.
Evans, D., Heuvelink, A. and Nettle, D. (2003), “The evolution of optimism: a multi-agent based model of adaptive bias in human judgment”, Proceedings of the AISB’03 Symposium on Scientific Methods for the Analysis of Agent-Environment Interaction, University of Wales, pp. 20-25.
Flyvbjerg, B. (2005), “Policy and planning for large infrastructure projects: problems, causes, cures”, World Bank Policy Research, Working Paper no. 3781.
Flyvbjerg, B. (2006), “From Nobel prize to project management: getting risk right”, Project Management Journal, Vol. 37, pp. 5-15.
Flyvbjerg, B. (2009a), “Optimism and misrepresentation in early project development”, Williams, T.M., Samset, K. and Sunnevag, K., Making Essential Choices with Scant Information: Front-end Decision Making in Major Projects, Palgrave McMillan.
Flyvbjerg, B. (2009b), “Survival of the unfittest: why the worst infrastructure gets built – and what we can do about it”, Oxford Review of Economic Policy, Vol. 25, No 3, pp. 344-367.
Flyvbjerg, B. (2004), “Procedures for dealing with optimism bias in transport planning”, The British Department for transport, London, UK.
Flyvbjerg, B., Holm M.S. and Buhl S. (2002), “Underestimating costs in public works projects: error or lie?”, Journal of the American Planning Association, Vol. 68, No.3 (Summer), pp. 279-295.
Flyvbjerg, B., Garbuio, M. and Lovallo, D. (2009), “Delusion and deception in large infrastructure projects”, California Management Review, Vol. 51, No.2, pp.170-193.
Galavotti, M.C. (1991), Subjectivism, Objectivism and Objectivity in Bruno De Finetti’s Bayesianism, Department of Philosophy, available at: http://www.swif.it/biblioteca/lr, (accessed 15 October 2011).
Gardoni P., Reinschmidt K.F. and Kumar R. (2007), “A probabilistic framework for Bayesian adaptive forecasting of project progress”, Computer-Aided Civil and Infrastructure Engineering, Vol.22, pp.182-196
Gibbons, J.D. and Prat, J.W. (1975), “P-values: Interpretation and Methodology”, The American Statistician, Vol.29, No.1, pp.20-25.
Goodwin, P. (2005), “How to integrate management judgment with statistical forecasts”, Foresight: The International Journal of Applied Forecasting, Vol.1, No.1, pp. 8-12.
Kahneman, D. and Tversky, A. (1977), “Intuitive Prediction: Biases and corrective procedures”, Defense Advanced Research Projects Agency.
Kahneman, D. and Tversky, A. (1979), “Intuitive prediction: biases and corrective procedures”, TIMS Studies in Management Science, Vol. 12, pp. 313–327.
Kahneman, D. and Tversky, A. (2007), “Judgment under uncertainty: Heuristics and Biases”, Science, New Series, Vol. 185, No.4157, pp. 1124-1131.
Kim, B. and Reinschmidt, K.F. (2009), “Probabilistic Forecasting of Project duration Using Bayesian Inference and Beta Distribution”, Journal of Construction Engineering and Management, Vol.135, No.3, pp.178-186.
Kim, B. and Reinschmidt, K.F. (2009), “Probabilistic Forecasting of Project duration Using Kalman Filter and the Earned Value Method”, Journal of Construction Engineering and Management, Vol.136, No.8, pp.834-843.
Kleim, R. and Ludin, I. (1998), Project management practitioner’s handbook, AMACOM.
Koole, S. and Spijker, V.M. (2000), “Overcoming the Planning Fallacy through willpower: effects of implementation intentions on actual and predicted task-completion times”, European Journal of Social Psychology, Vol. 30, pp. 873-888.
Kutsch, E., Lupson, J., Maylor, H. and Weyer, B. (2011), “Performers, trackers, lemmings and the lost: sustained false optimism in forecasting project outcomes”, International Journal of Project Management, Vol.29, pp. 1070-1081.
Lavine, M. (2007), Introduction to statistical thought, available at: http://www.math.umass.edu/~lavine/Book/book.html (accessed 15 October 2011).
Lipke, W. (2003), “Schedule is different”, The Measurable News, Summer 2003, pp. 31-34,
Lovallo, D. and Kahneman, D. (1993), “Timid choices and bold forecasts: a cognitive perspective on risk taking”, Management Science, Vol. 39, No. 1, pp. 17-31.
Lovallo, D. and Kahneman, D. (2003), “Delusion of Success: How optimism undermines executives’ decisions”, Harvard Business Review, Vol. 81, July 2003, pp. 56-63.
Marshall, R.A., Ruiz, P., and Bredillet, C.N. (2008), “Earned value management insights using inferential statistics”, International Journal of Managing Projects in Business, Vol.1, No.2, pp. 288-294
McGraw, K.O. and McCullers, J.C. (1979), “Evidence of detrimental effect of extrinsic incentives on breaking a mental set”, Journal of Experimental Social Psychology, Vol. 3, pp. 285-294.
McKinney, J.W. (1991), “Estimate at Completion Research”, Air Force Institute of Technology.
Merrow, W.E. (2011), “Oil Industry Megaprojects: Our Recent Track Record”, Offshore Technology Conference.
Nau, R. F. (2001), “De Finetti was right: probability does not exist”, Theory and Decision, Vol. 51, pp. 89-124.
Project Management Institute (2008), A guide to the Project Management Body of Knowledge (PMBOK) 4th ed., Project Management Institute
Project Management Institute (2011), Practice standard for Earned Value Management, Newton Square, PA, Project Management Institute
Roy, M.M., Christenfeld, N. J. S. and McKenzie, R. M. (2005), “The broad applicability of memory bias and its coexistence with the planning fallacy: reply to Griffin and Buehler”, Psychological Bulletin, Vol. 131, pp. 761-762.
Slovic, P., Finucane, M. L., Peters, E. and Mac Gregor, D. (2004), “Risk as analysis and risk as feelings: some thoughts about affect, reason, risk and rationality”, Risk Analysis, Vol.24, No. 2, pp. 311-321
Stevens, W.M. (1986), “Cost Control: Integrated Cost/Schedule Performance”, Journal of Management in Engineering, Vol.2, No.3, pp.157-164
Suppes, P. (2007), Where do Bayesian priors come from?, Springer.
Taylor, S.E. and Brown, J.D. (1994), “Positive illusions and well-being revisited separating fact from fiction”, Psycological Bulletin, Vol. 116, pp. 21-27.
Walter, C., Greene, B.A. and Mansell, R.A. (2006), “Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement”, Learning and Individual Differences, Vol. 16, pp. 1-2.
Weick, M. and Guinote, A. (2010), “How long will it take? Power biases time predictions”, Journal of Experimental Social Psychology, Vol. 46, No.4, pp. 595-604.
Ying, Z., Ayelet, F. and Ravi, D. (2007), “When Thinking Beats Doing: The Role of Optimism Expectations in Goal-Based Choice”, Journal of Consumer Research, Vol.34.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Franco Caron, Fabrizio Ruggeri, Cristiano Borgarucci
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
