
New technologies follow a fairly well-understood “curve”. We can think of these curves in terms of profitability, but this is oversimplifying things. Much of the thinking about technology curves in the business world comes from the work of Nikolai Kondratiev in the 1920s.
The Kondratiev Curve
In his work, Kondratiev suggested that there are long-term cycles of economic development and growth, characterized by periods of prosperity and innovation followed by periods of stagnation and decline. These periods are driven by technological innovation.
During the periods of prosperity, known as Kondratiev upswings, there are rapid advances in technology and infrastructure, as well as increased productivity and economic growth. These upswings are typically accompanied by new inventions and innovations that have a significant impact on society and the economy. For example, the Industrial Revolution, which began in the late 18th century, is considered a Kondratiev upswing.
In contrast, the periods of stagnation and decline, known as Kondratiev downswings, are characterized by slower economic growth, decreased innovation, and declining productivity. These downswings are typically marked by economic crises, such as recessions and depressions, and may be accompanied by social and political instability.
Technology Curves in Strategy
While the application of this theory to economic thinking may have drawbacks, it is quite useful in understanding the path of a specific technology in an industry. Technological adoption and improvement in the early years of a technology tends to lead to rapid growth and profitability for the key players in an industry, while the downswings can indeed lead to crises an instability.
In the business world, most of the darling companies of the past 50 years have been companies in the upswing part of the cycle. This make sense, since the way most analysts value stocks relies heavily on the Black-Sholes model and puts a premium on growth.
Strategically, if you are in the upswing phase of your technology, congratulations. Your business is likely to be viewed favorably by investors and your main focus should be market share over anything else, including profit.
When your technology reached the top of the curve and begins the downswing, your strategic priorities should shift. At this point, market share is still a priority, but profit becomes much more important as your industry shifts from building the technology to exploiting the technology. Efficiency, execution and branding become much more important in this phase, and growth may be best achieved through identifying and exploiting niches or consolidation of the industry. If your business has geographic elements, you may also want to seek growth through geographic expansion.
Understanding where you are on the technology curve can be a critical element of setting good strategy. Simplified Strategic Planning offers a tremendous advantage for setting and executing such strategies. If you’d like to book a workshop on the process and its application in your organization, contact us at www.cssp.com/inhouse-workshop/



Should you use more than one?
There are three basic reasons why 
One of the great challenges in executing a strategic plan is getting the team to perform and be motivated by the strategy. Indeed, strategic performance in implementation is the achilles heel of strategic planning. It’s common to hear people say “We did strategic planning, but it didn’t change anything.” Obviously, the way you approach strategic planning should be oriented to getting better strategic performance from your whole organization. It turns out that by looking at things that enhance individual performance, we can find corollaries in team performance that are very useful for executing your strategic plan.

With oscillating curves, you have a slightly more difficult task predicting with leading indicators. This is because the wavelike form of the oscillating curve can vary a lot in both frequency (the number of waves per time period) and amplitude (the distance between the top and the bottom of the curve). Usually, we can understand both by examining historical data. For example, if you look at the graph here, you see data on car sales in the USA from 1951-2019. You can see that there is clear oscillation, largely driven by purchasing ability. While the frequency varies from decade to decade, there is a clear 3-5 year space between the peaks in the curve and the valleys that follow in most cycles. This would be a decent frequency to assume for future cycles in auto sales.
In any complex system built around process flows and limits, we can graph changes over time. It’s common to refer to a simple XY graph over time as a curve, and the behavior of curves is a very important part of understanding how these systems will change. If we want to predict automobile sales, new technology adoption, population trends or even the stock market, some simple ideas about the shape of these curves can lead to much better predictions about the future.






