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Innovators Spotlight
Overcoming Optimism
Bias in Portfolio Planning
Today’s Speakers
Yael Grushka-
Cockayne, PhD
Darden School of
Business, UVA
GrushkaY@darden.virginia.edu
Tearsa Coates
Decision Lens
Sr. Product Marketing
Manager
tcoates@decisionlens.com
Decision Lens is a strategic
prioritization and enterprise
resource optimization solution for
critical decision-making in R&D and
capital planning.
Dozens of Leading, Global Organizations Use
Decision Lens for Portfolio Prioritization
The need for prioritization is clear
With Strategic PrioritizationWithout Strategic Prioritization
Innovators Spotlight: Dr. Yael Grushka-Cockayne
6
Optimism Bias in Innovation Portfolio
Planning
Title: Assistant Professor of Business Administration
at the Darden School of Business, University of
Virginia
Research: Decision analysis, forecasting, project
management and behavioral decision making
Journal Publications: Management Science and
Operations Research
Affiliations: INFORMS Decision Analysis Society,
Project Management Institute (PMI),
UVA Excellence in Diversity fellow
Yael Grushka-
Cockayne, PhD
Darden School of
Business, UVA
Overcoming Optimism Bias in Portfolio & Project Planning
Today’s Topic
We will investigate project performance and
the factors that contribute to the planning
fallacy.
You will learn techniques to combat
optimism bias, overconfidence and strategic
maneuvering to improve the accuracy of
your forecasted project goals and portfolio
selection.
Let’s talk about project performance…
Let’s talk about project performance…
The perils of Planning Fallacy
•Projects cost more, take longer, and deliver less than
expected
Planning Fallacy: “The tendency to underestimate task-
completion times and costs”
Lovallo and Kahneman, HBR 2003
• 3 times the cost
• Two years late
• 1/3 the size
Constraints on planning and forecasting
Eubanks, Read and Grushka-Cockayne, 2013
Cognitive Biases and
Bounded Rationality
• Confirmation,
overconfidence and
anchoring biases
• Optimism Bias and the
Winner’s Curse
• Out-of-sight / Out-of-
mind
• Parkinson’s Law
Organization and Agency
Constraints
• The “Olympics effect”
• Failure to account for
organizational,
externalities, and
coordinate costs
• Student syndrome and
planning to a schedule
• Competitor neglect
Overcoming Optimism Bias in Portfolio Planning
• City of New York Department of
Parks and Recreation:
•1800+ projects (1998 - 2008):
• 50%+ over budget (on average by
48%)
• 55% delayed (on average by 22%)
• UK Construction Industry Data
•BCIS: Building Cost Information
Service
•808 projects (2003 – 2006)
Comparative case study of optimism bias
• NYC DPR exhibits strong planning fallacy.
Forecast/Actual cost ratio below 1, and
invariant with project magnitude.
• Practically every project is over budget.
Evidence of NYC DPR’s optimism bias…
Electrical Tree Landscape Plant Pool HVAC Playgrd
Count 106 270 58 167 56 17 152
Average
over
budget
$ 31,439 $ 83,905 $ 411,619 $74,188 $ 72,411 $ 77,584 $193,381
Average
duration
overrun
172.2 -45.6 138.7 -58.2 30.5 269.9 95.5
Planning fallacy extended to time overruns
UK construction data indicates little or no planning
fallacy, for either costs or time. Small time overruns
are almost constant and invariant to project
magnitude.
Optimism bias is not inevitable
• Calls into question universality of the fallacy, and
undermines the view that it is {only} due to cognitive
bias.
• Project attributes which lead to faulty parameter
estimation when the fallacy does occur:
• Size ($)
• Type / sector of projects
• Lack of experience of the project team
(measured by volume)
• Tendering contractor selection process
• Procurement systems used
What we learned…
But can we do anything about it?
“[the Rio de Janeiro state
government] said it was
"natural" that some more
work remained to be
done.”
BBC, 2013
Inside View:
• Describing the future
as a narrative
• E.g. CPM: Developing
a series of steps that
leads from beginning
to end of a project.
• How long will each
task take?
• What resources
will be needed?
Outside View:
• Taking a statistical
perspective
• Ignores the detail of the
case at hand
• Estimating task
duration/cost/benefits
by asking about
previous, similar tasks.
• Reference class
forecasting
Kahneman and Lovallo, MS 93
Inside vs. Outside view
1. Select a reference class – previous projects similar on
important characteristics
2. From the reference class, assess the distribution of
outcomes
3. Identify where your project falls in the distribution
4. Assess the reliability of your predictions
5. Adjust estimate toward average based on estimate of
reliability
Lovallo and Kahneman, HBR 2003
Outside view / Reference class forecasting
• Over 3000 projects a year
• Major infrastructure projects
• A domain where Optimism Uplift has been
routinely applied
• Recent 5-year budget request: £35.7 billion
“We run, maintain and develop Britain’s rail
tracks, signaling, bridges, tunnels, level
crossings, viaducts and 17 key stations”
60%
contingency
Case example: UK Network Rail
• 1595 projects which have cost revision requests
(2004-2012)
• Mean earliest Cost estimation: £16.1million
• Mean late Cost estimation: £39.8 million
• Revision requests are always approved (subject to
gathering additional information)
• No past accuracy or performance is reported
• Risk factors are assessed and uplift is applied
Case example: UK Network Rail
• Development of an improved method of outside-
view forecasting with UK Network Rail and HR
Treasury
• Combine aspects of wisdom-of-crowds, case-base
reasoning and reference class forecasting
• Investigate the impact of additional project
attributes (# of tasks, size of organization, project
planning method, etc.)
What’s next?
Learn more about portfolio optimization and
prioritizing investments for higher returns
See Decision Lens in Action
www.decisionlens.com
Videos, Research, and Resources
R&D: innovation.decisionlens.com
Capital Planning: capitalassets.decisionlens.com
Join Portfolio Thought Leaders on LinkedIn
R&D: Leaders in R&D Portfolio Strategy
Capital Planning: Leaders in Capital Planning
• Daniel Kahneman and Amos Tversky, “Intuitive Prediction:
Biases and Corrective Procedures,” in TIMS Studies in
Management Science vol. 12, eds. Spyros Makridakis and
Steven C. Wheelwright (Amsterdam: North Holland, 1979),
313–327.
• Dan Lovallo and Daniel Kahneman. 2003. “Delusions of
Success: How Optimism Undermines Executives’ Decisions”,
Harvard Business Review, 56–63.
• Bent Flyvbjerg, “Delusions of Success: Comment on Dan
Lovallo and Daniel Kahneman,” Harvard Business Review,
December 2003, pp. 121–122.
• Yael Grushka-Cockayne, “New York City Department of Parks
and Recreation”, Darden Business Publishing Case Study, UVA-
QA-0815.
References
Prioritize the portfolio by your values
A ranked list of projects based on the strategic goals and priorities of
your organization.
Compare scenarios to decide what’s best
Compare different funding allocation scenarios with project
prioritization results.
Get deeper insights with visual analytics
Uncover potential opportunities or problems with visualizations.
Balance resources for successful execution
Zero-in on bottlenecks in time, resources, or scheduling before they
occur.

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Overcoming Optimism Bias in Portfolio Planning

  • 2. Today’s Speakers Yael Grushka- Cockayne, PhD Darden School of Business, UVA GrushkaY@darden.virginia.edu Tearsa Coates Decision Lens Sr. Product Marketing Manager tcoates@decisionlens.com
  • 3. Decision Lens is a strategic prioritization and enterprise resource optimization solution for critical decision-making in R&D and capital planning.
  • 4. Dozens of Leading, Global Organizations Use Decision Lens for Portfolio Prioritization
  • 5. The need for prioritization is clear With Strategic PrioritizationWithout Strategic Prioritization
  • 6. Innovators Spotlight: Dr. Yael Grushka-Cockayne 6 Optimism Bias in Innovation Portfolio Planning Title: Assistant Professor of Business Administration at the Darden School of Business, University of Virginia Research: Decision analysis, forecasting, project management and behavioral decision making Journal Publications: Management Science and Operations Research Affiliations: INFORMS Decision Analysis Society, Project Management Institute (PMI), UVA Excellence in Diversity fellow Yael Grushka- Cockayne, PhD Darden School of Business, UVA
  • 7. Overcoming Optimism Bias in Portfolio & Project Planning Today’s Topic We will investigate project performance and the factors that contribute to the planning fallacy. You will learn techniques to combat optimism bias, overconfidence and strategic maneuvering to improve the accuracy of your forecasted project goals and portfolio selection.
  • 8. Let’s talk about project performance…
  • 9. Let’s talk about project performance…
  • 10. The perils of Planning Fallacy •Projects cost more, take longer, and deliver less than expected Planning Fallacy: “The tendency to underestimate task- completion times and costs” Lovallo and Kahneman, HBR 2003 • 3 times the cost • Two years late • 1/3 the size
  • 11. Constraints on planning and forecasting Eubanks, Read and Grushka-Cockayne, 2013 Cognitive Biases and Bounded Rationality • Confirmation, overconfidence and anchoring biases • Optimism Bias and the Winner’s Curse • Out-of-sight / Out-of- mind • Parkinson’s Law Organization and Agency Constraints • The “Olympics effect” • Failure to account for organizational, externalities, and coordinate costs • Student syndrome and planning to a schedule • Competitor neglect
  • 13. • City of New York Department of Parks and Recreation: •1800+ projects (1998 - 2008): • 50%+ over budget (on average by 48%) • 55% delayed (on average by 22%) • UK Construction Industry Data •BCIS: Building Cost Information Service •808 projects (2003 – 2006) Comparative case study of optimism bias
  • 14. • NYC DPR exhibits strong planning fallacy. Forecast/Actual cost ratio below 1, and invariant with project magnitude. • Practically every project is over budget. Evidence of NYC DPR’s optimism bias…
  • 15. Electrical Tree Landscape Plant Pool HVAC Playgrd Count 106 270 58 167 56 17 152 Average over budget $ 31,439 $ 83,905 $ 411,619 $74,188 $ 72,411 $ 77,584 $193,381 Average duration overrun 172.2 -45.6 138.7 -58.2 30.5 269.9 95.5 Planning fallacy extended to time overruns
  • 16. UK construction data indicates little or no planning fallacy, for either costs or time. Small time overruns are almost constant and invariant to project magnitude. Optimism bias is not inevitable
  • 17. • Calls into question universality of the fallacy, and undermines the view that it is {only} due to cognitive bias. • Project attributes which lead to faulty parameter estimation when the fallacy does occur: • Size ($) • Type / sector of projects • Lack of experience of the project team (measured by volume) • Tendering contractor selection process • Procurement systems used What we learned…
  • 18. But can we do anything about it? “[the Rio de Janeiro state government] said it was "natural" that some more work remained to be done.” BBC, 2013
  • 19. Inside View: • Describing the future as a narrative • E.g. CPM: Developing a series of steps that leads from beginning to end of a project. • How long will each task take? • What resources will be needed? Outside View: • Taking a statistical perspective • Ignores the detail of the case at hand • Estimating task duration/cost/benefits by asking about previous, similar tasks. • Reference class forecasting Kahneman and Lovallo, MS 93 Inside vs. Outside view
  • 20. 1. Select a reference class – previous projects similar on important characteristics 2. From the reference class, assess the distribution of outcomes 3. Identify where your project falls in the distribution 4. Assess the reliability of your predictions 5. Adjust estimate toward average based on estimate of reliability Lovallo and Kahneman, HBR 2003 Outside view / Reference class forecasting
  • 21. • Over 3000 projects a year • Major infrastructure projects • A domain where Optimism Uplift has been routinely applied • Recent 5-year budget request: £35.7 billion “We run, maintain and develop Britain’s rail tracks, signaling, bridges, tunnels, level crossings, viaducts and 17 key stations” 60% contingency Case example: UK Network Rail
  • 22. • 1595 projects which have cost revision requests (2004-2012) • Mean earliest Cost estimation: £16.1million • Mean late Cost estimation: £39.8 million • Revision requests are always approved (subject to gathering additional information) • No past accuracy or performance is reported • Risk factors are assessed and uplift is applied Case example: UK Network Rail
  • 23. • Development of an improved method of outside- view forecasting with UK Network Rail and HR Treasury • Combine aspects of wisdom-of-crowds, case-base reasoning and reference class forecasting • Investigate the impact of additional project attributes (# of tasks, size of organization, project planning method, etc.) What’s next?
  • 24. Learn more about portfolio optimization and prioritizing investments for higher returns See Decision Lens in Action www.decisionlens.com Videos, Research, and Resources R&D: innovation.decisionlens.com Capital Planning: capitalassets.decisionlens.com Join Portfolio Thought Leaders on LinkedIn R&D: Leaders in R&D Portfolio Strategy Capital Planning: Leaders in Capital Planning
  • 25. • Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” in TIMS Studies in Management Science vol. 12, eds. Spyros Makridakis and Steven C. Wheelwright (Amsterdam: North Holland, 1979), 313–327. • Dan Lovallo and Daniel Kahneman. 2003. “Delusions of Success: How Optimism Undermines Executives’ Decisions”, Harvard Business Review, 56–63. • Bent Flyvbjerg, “Delusions of Success: Comment on Dan Lovallo and Daniel Kahneman,” Harvard Business Review, December 2003, pp. 121–122. • Yael Grushka-Cockayne, “New York City Department of Parks and Recreation”, Darden Business Publishing Case Study, UVA- QA-0815. References
  • 26. Prioritize the portfolio by your values A ranked list of projects based on the strategic goals and priorities of your organization.
  • 27. Compare scenarios to decide what’s best Compare different funding allocation scenarios with project prioritization results.
  • 28. Get deeper insights with visual analytics Uncover potential opportunities or problems with visualizations.
  • 29. Balance resources for successful execution Zero-in on bottlenecks in time, resources, or scheduling before they occur.

Notas del editor

  1. Bio - Yael Grushka-Cockayne Yael is an Assistant Professor of Business Administration at the Darden School of Business at the University of Virginia. Yael received her BSc in Industrial Engineering from Ben-Gurion University; her MSc in Operational Research from the London School of Economics; and her MRes and PhD in Management Science and Operations from the London Business School. Yael’s research and teaching activities focus on decision analysis, forecasting, project management and behavioral decision making. Her research is published in academic and professional journals such as Management Science and Operations Research. Before starting her academic career, she worked in San Francisco as a marketing director of an Israeli ERP company. As an expert in the areas of project management, she has served as a consultant to international firms in the aerospace and transportation industries. She is the secretary/Treasurer of INFORMS Decision Analysis Society, a UVA Excellence in Diversity fellow and a member of INFORMS and the Project Management Institute (PMI).
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