Hope is not a strategy.
Optimism Bias is one of the most common and detrimental biases in portfolio planning. Portfolio strategists routinely overvalue potential and underestimate risk.
Decision Lens is proud to welcome Professor Yael Grushka-Cockayne of The University of Virginia Darden School of Business to share the impact of Optimism Bias and the best methods to overcome it.
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.
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
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).