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Lost in transition


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

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Lost in transition

  1. 1. /SEEDX Lost in Transition Fooled by randomness of projects, black swans, traditional risk factors and the not-so usual suspects Geert Jan Beekman
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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%