Posts Tagged ‘s-curve’

Last week I introduced the concept of self-similarity and showed its relevance for power law distributions. In this post I discuss the applicability of self-similarity in S-curves.

To recap briefly, self-similarity implies that a structure looks essentially the same at all levels of “magnification” or scale. You can zoom in on any part of a “power curve,” and it will look like… a power curve, with basically the same appearance as at the higher scale.

The same phenomenon can be seen in s-curves, with the difference that the scale invariance is less apparent, at least initially. The following diagram shows how each phase on an s-curve can be broken out into smaller, constituent s-curves at the next lower level. By extension, each of these subordinate s-curves can be parsed in the same, self-similar way. The structure is recursive and nested. If you want to grow, develop, or improve in any way, you must see it as a succession of s-curves at all levels of scale.

self-similarity-s-curve

This is why I’ve titled this post “Growth is a stairway, not a high jump!”. You make progress in increments, climbing from one step to the next in a succession of achievable bounds. This breaks progress and improvement into (to paraphrase Neil Armstrong) a series of “one small step” moves so you can make “one giant leap” for your bigger purpose… or goals.

This is more manageable from a psychological standpoint as well as logistically. It also makes risk more manageable. As I illustrate in the following diagram, there are risks at each transition to a succeeding s-curve. Risk can arise from making a jump–even a small one–to a higher level of performance and engagement. It can also arise from a drop in performance at this critical juncture. We can seldom know and do everything that is needed at the new level. We need to learn–which is why progress is depicted as an s-curve in the first place. We start out with low performance at the new level and a high potential for mistakes. If we’re focused on learning from our mistakes and on improvement, we get progressively better until we hit the rapid growth stage, and continue up the “learning” curve from there on in. When we hit the inevitable plateau, we must jump–or drop–to the next curve.

risk-at-thresholds

The final point is that performance or growth can bog down or slip at any point, for any number of reasons. We can stop or slip back down the curve we’re already on. I call this regression. Even more consequential is when we drop back to a previous curve. I call this retrogression, and I’ve illustrated it in detail in the following diagram. It shows how you can fall from any performance level to any other, usually through neglect, over-confidence, smugness, or simply through inattention to changing conditions in the environment. For instance, new technology, new competitors, changing demographics, all these can make our current success or standing shaky or even irrelevant.

retrogression-s-curve

I don’t say this to be overly pessimistic, but rather realistic. Stasis is death. Movement is crucial. Business, life, performance, everything, they are what is called a “red queen” race. You have to work just to stay in place and work even harder to make progress, grow, develop, get better.

We’ll address these issues and many more in my coming posts under the topic of “Ideas,” so stay tuned to this space.

My name is Richard Martin and, as indicated by the title of this blog, I’m an expert on applying readiness principles to position companies and leaders to grow and thrive by shaping and exploiting change and opportunity, instead of just passively succumbing to uncertainty and risk.

© 2016 Alcera Consulting Inc. This article may be used for non-commercial use with proper attribution.

By Richard Martin, Expert in Business Readiness and Exploiting Change

One of the most useful ideas for conceptualizing any kind of change process is the S-curve. Perhaps you’ve seen one of these before. It looks like this:

typical-s-curve

The S-curve is the way natural and human phenomena grow and develop over time. For instance, the plot of a growth of bacteria or yeast in a laboratory follows the exact S-curve. Technically, it’s known as a logistic function, and when we plot it as a rate of growth, rather than cumulative growth, it forms a bell curve, although it doesn’t follow a normal, Gaussian, distribution. In other words, when something starts growing or spreading, it first starts very slowly, then it speeds up until it hits it hits a maximum, after which the growth/spread rate slows down until it basically tends to zero.

The S-curve approximates the cumulative growth or spread of just about any natural or man-made phenomenon, such as:

  • Penetration of a new market segment
  • Growth of new product/service category
  • Learning stages
  • Interest in topics
  • Abilities (which tend to plateau after a time)
  • Etc.

One of the more relevant business applications is in strategy formulation and execution. Take a look at the following S-Curve application. It shows how we can map the different phases of a product or market life cycle onto the S-curve. This gives an intuitive understanding that all good things must come to an end or, as I imply in the title of this piece, “What goes up, must (eventually) come down (or at least level off).”

product-market-life-cycle-phases

New products or markets start as ideas, often as an external start up. I pluralize this because there should be a relatively high number of “experiments” and trials underway at any one time within a diversified company. Another strategy is to watch out for promising startups outside the business (or in an internal “skunkworks”) and then invest in them or simply acquire them once they start entering their rapid growth phase. Companies should have businesses (various combinations of product-market mix) in all stages of the life cycle in order to ensure a constant stream of growth generating ideas and strategic business units.

Another important phenomenon to note is the presence of a decline phase. Unless there is continuing investment in a business line or concept, it will eventually go into decline. We don’t necessarily know when, but we DO know it will happen at some point. This is another reason to be constantly replenishing the pipeline at the earlier life cycle stages of startup and rapid growth. The capital needed to invest in future ideas and growth will often come from the “milk cows” that are businesses in the maturity or plateau stage, although the latter can also provide a good source of financial capital through divestment.

© 2016 Richard Martin. Reproduction, forwarding, and quotes are permitted with proper attribution.