Power Laws and Pull Platforms

Today’s post is guest authored by Craig Woll.  Craig has a unique background in developing and applying instructional learning systems to lean manufacturing.

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I always appreciate reading an article written by someone with a heavier head than mine. In this case John Hagel contemplates on a serious topic "The Power of Power Laws". His article caught my attention quite some time ago but I’ve struggled articulating what I find so intriguing about his perspective. Maybe it is the mere fact that I had to use Merriam-Webster to look up half the words.

A gaussian distribution is used "as a as a way to characterize the probability of events – most of us know it as the familiar bell curve with a significant hump in the middle and two relatively modest tails on either side of the hump." On the other hand you have the "Pareto, or power law, probability distribution". One form of this is called the 80/20 rule.

We often attempt to describe the world in view of one of these two paradigms. I found myself on the left side of the bell curve in my statistics class so I won’t even attempt further explanation at the risk of sounding foolish. However, I will take the liberty of quoting a journal article from Bill McKelvey and Pierpaolo Andriani:

Gaussian and Paretian distributions differ radically. The main feature of the Gaussian distribution . . . can be entirely characterized by its mean and variance . . . A Paretian distribution does not show a well-behaved mean or variance. A power law, therefore, has no average that can be assumed to represent the typical features of the distribution and no finite standard deviations upon which to base confidence intervals . . .

Apparently these guys also didn’t appreciate the way my previous manager used to distribute his employees across the bell curve during the annual performance review.

The article postulates that the "average consumer" is the target of most businesses in the 20th century. Maybe I’m going out on a limb but that would mean that if you have a normal bell curve you would only be meeting the need of mean/median consumer. Everyone else would be some deviation from the mean. If I take it a step further you might then argue that consumer value diminishes as one deviates from the mean. Imagine then that your consumer is actually living in a paretian environment but you are operating using a gaussian paradigm. Theoretically you would end up with a skewed curve and therefore have even fewer consumers receiving exactly what they wanted.

Ok, I know that is a major stretch but I just wanted to emphasize the point that it may be important for us to think about our paradigm for viewing our organization, our customers, and ourselves.

So what does this all mean? I don’t know, but here is what Hagel says:

Our institutions (not just businesses, but also educational and governmental) are largely designed for a Gaussian world where averages and forecasts are meaningful. As a result, we have evolved a sophisticated set of push programs that have delivered significant efficiency. In a world of sudden, severe and difficult to anticipate shifts, push programs become much less viable and we need to become a lot more creative in terms of designing pull platforms… Bottom line: our institutional architectures, not to mention our technology architectures, will need to be redesigned to cope with a Paretian world.

Now, where have I heard about pull platforms? It seems to ring a bell. If what he postulates is true then it becomes ever more important to be able to pull value through a system at a rapid pace. Those companies that learn to have a Paretian viewpoint can use pull systems to provide value to most customers and not just the average customer. The outsourcing lemmings and others that are focusing on Gaussian tools will be unlikely to capitalize on the benefits of being able to rapidly respond to their customer. I’ll leave the final word to Mr Hagel and his heavy head.

"Small moves, smartly made, can lead to exponential improvements in wealth creation"