Project Power Laws

An interesting paper finds a heavy-tailed (power law) distribution in IT project performance.

IT projects fall in to a similar category. Calculating the risk associated with an IT project using the average cost overrun is like creating building standards using the average size of earthquakes. Both are bound to be inadequate.

These dangers have yet to be fully appreciated, warn Flyvbjerg and Budzier. “IT projects are now so big, and they touch so many aspects of an organization, that they pose a singular new risk….They have sunk whole corporations. Even cities and nations are in peril.”

They point to the IT problems with Hong Kong’s new airport in the late 1990s, which reportedly cost the local economy some $600 million.

They conclude that it’s only a matter of time before something much more dramatic occurs. “It will be no surprise if a large, established company fails in the coming years because of an out-of-control IT project. In fact, the data suggest that one or more will,” predict Flyvbjerg and Budzier.

In a related paper, they identify the distribution of project outcomes:

We argue that these results show that project performance up to the first tipping point is politically motivated and project performance above the second tipping point indicates that project managers and decision – makers are fooled by random outliers, …

I’m not sure I buy the detailed interpretation of the political (yellow) and performance (green) regions, but it’s really the right tail (orange) that’s of interest. The probability of becoming a black swan is 17%, with mean 197% cost increase, 68% schedule increase, and some outcomes much worse.

The paper discusses some generating mechanisms for power law distributions (highly optimized tolerance, preferential attachment, …). A simple recipe for power laws is to start with some benign variation or heterogeneity, and add positive feedback. Voila – power laws on one or both tails.

What I think is missing in the discussion is some model of how a project actually works. This of course has been a staple of SD for a long time. And SD shows that projects and project portfolios are chock full of positive feedback: the rework cycle, Brooks’ Law, congestion, dilution, burnout, despair.

It would be an interesting experiment to take an SD project or project portfolio model and run some sensitivity experiments to see what kind of tail you get in response to light-tailed inputs (normal or uniform).

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