These slides summarize some recent findings on projects, risk, black swans and ‘how-to-save-time-and-money-managing-your-projects’

1. /SEEDX
Lost in Transition
Fooled by randomness of projects, black swans,
traditional risk factors and the not-so usual suspects
Geert Jan Beekman
geertjan@xseed.nu

2. Introduction
“The reason why firms succeed or fail is perhaps the central question in strategy. It has
preoccupied the strategy field since its inception ….” is a 1991 quote by strategy guru
Michael Porter*
Replace some 20 years later ‘firms’ by ‘projects’ and the sigh is ever so current
To uncover these ‘reasons’ and to resolve them, once and for all, still is the challenge
of every stakeholder in todays uncertain economic circumstances
In the face of such uncertainties, every stakeholder wants to unambiguously allocate
success (but more so failure) to concrete internal and external factors, quantify their
contributions and nudge them in a positive direction!
These slides summarize some of the most recent findings on projects, risk,
randomness and ‘how-to-save-time-and-money-managing-your-projects’
* M. E. Porter. Towards a dynamic theory of strategy. Strategic Management Journal, 1991, 12, p. 95.

3. Our finished engagements and current research projects have yielded interesting and
sometimes unexpected findings :
• Fooled by Randomness
• Fooled by Black Swans
• Fooled by the Traditional Risk Factors: ‘The Usual Suspects’
• Fooled by the ‘Unusual Suspects’
– Managing the Process …..
– The Human Wiring
Some of our Findings

4. Fooled by Randomness
• Any portfolio of projects breaks down into ‘white
swans’ , ‘black swans’ and ‘lame ducks’
– White swans show a project overrun between -10%
and +40% and are ‘normally’ distributed. This means
that they run over budget in a linear way: 20%
overrun halfway, most likely ends up in a 40% (20%
* 2) overrun
– Black swans show a project overrun larger than the
white swans, and are exponentially distributed. This
means that they run over budget in an ‘exponential’
way: 20% overrun halfway, most likely ends up in a
400% (202) overrun*
– Lame ducks show a project underrun smaller than
the white swan, and are also exponentially
distributed. Unfortunately, these projects are
prematurely terminated and thus appear to
underrun their budget
• This breakdown holds for large as well as small
projects, In IT, business, real estate or facilities
• The left-hand side graph is a log-log plot (Standard
Excel feature) of the cumulative distribution of the
project overrrun [(actual/budget)*100]
* Provided that the exponential of the distribution is a factor ‘2’.
If it is a ‘3’, then the overrun becomes (203), ‘1.2’ it becomes 201.2

5. Fooled by Black Swans
White Swans
• Risk factors balance
– Doing things right on risk factors
(e.g. GOOD support by senior
management) off-sets doing
things wrong (e.g. LITTLE
support) and DAMPENS overrun
Black Swans
• Balance goes off-the-scale
– Doing things right, with few
exceptions, no longer off-sets
doing things wrong and
AMPLIFIES overrun

6. Fooled by Traditional Risk Factors (1/3)
1. Support from senior management
2. Clear and realistic objectives
3. Strong/detailed plan kept up to date
4. Good communications and feedback
5. User/Client involvement
6. Qualified staff
7. Effective change management
8. Competent project management
9. Strong business case / sound basis for the project
10. Sufficient / well allocated resources
11. Good leadership
12. Proven / familiar technology
13. Realistic schedule
14. Risks are assessed, addressed and managed
15. Project sponsor / champion
16. Effective monitoring & control
17. Adequate budget
18. Organizational adaptation / culture / structure
19. Good performance by suppliers / contractors / consultants
20. Planned close down review / acceptance of possible failure
21. Training provision
22. Political stability
23. Correct choice / past experience of PM tools & methods
24. Environmental influences
25. Learning from past experience
26. Project size
27. Appreciation of differing viewpoints
• Scientific research into project risk factors covers a 40 year history en has yielded a valid and reliable list of
27 factors that consistently have roved their effect on budget overruns
• This list is handsomely summarized in a 2006 paper by Joyce Fortune and Diana White*:
* Fortune, J. & D. White. Framing of project critical success factors by a systems model. IJPM, 2006, 53 – 65.
• In 2012 investigators of McKinsey cluster 21 of these 27 factors into 4 groups and conduct a regression
analysis on 5400 IT projects collected from dozens of companies
• They find a total contribution to budget overrun of 45% for the clusters but no significant effect for any of
the individual factors**
** Bloch, M., S. Blumberg & J. Laartz. Delivering large-scale IT projects on time, on budget and on value. McKinsey Quarterly, October 2012. (on line)

7. Fooled by Traditional Risk Factors (2/3)
• Our Transition Compass™
Algorithm calculates the
contribution of the same
factors on actual
portfolios between 30
and 180 projects
• The total contribution of
the clusters differs for
black swans and white
swans and never
surpasses 39%
– White Swans 25%
– Black Swans 39%
• The algorithm also
determines the
contributions of the
individual factors and
their interactions

8. Fooled by Traditional Risk Factors (3/3)
• The total contribution of
the clusters of white
swans is contingent on
the project domain* and
shows a considerable
bandwidth
• Black swans cluster more
evenly around the mean
*IT, Business, Commercieel Vastgoed & Facilities

9. Managing the Process …..
• Our experiences from
practice have uncovered
a fifth cluster with 6
other factors
• The cluster ‘Managing
the Process’ refers to
‘fulfilling your role’ and
‘taking your
responsibilities’ and
contributes considerably
to success and failure
• The total contribution
increases:
– White Swans
• 25%  34%
– Black Swans
• 39%  60%

10. The Human Wiring: Bias & Heuristics
• Our experiences from
practice have uncovered a
sixth cluster with 5 other
factors
• This cluster refers the
simplifying heuristics that
people use to form
judgments and take
complex decisions
• These heuristics are prone
to systematic bias and
contribute, on average, 22%
to success and failure
• The total contribution
increases further
– White Swans
• 34%  56%
– Black Swans
• 60%  82%