New technologies that changes conditions in a market, and often for society as a whole, are called disruptive. The phenomenon has been known since at least the early 20th century (see Joseph Schumpeter and Creative destruction) although concepts and models have changed.
The biggest disruptive changes in recent history have been the steam engine and the electrification, respectively. The steam engine became the starting point for the industrial revolution that fundamentally changed the whole of society. The last two examples of major disruptive technologies are the computers followed by the internet. These two have not had as strong an impact as the steam engine, but they have nevertheless fundamentally changed how our society works today.
For the individual, changes in the labor market that follows a technology shift are the great drama. Jobs that previously existed in abundance disappear. Companies are being forced to make significant rationalizations. Lots of administrative tasks have disappeared with computerization. This was often simple routine jobs that few workers today would want, but which were important to those who once had them.
For companies, disruptive technology is a constant threat. But a business can be part of the transformation and thus turn them to their advantage by working on innovative cutting-edge product development. Being late will mean lost market share or, in the worst case, bankruptcy.
The difficulty with disruptive technologies is that they are insidious. Electric cars are an example of such a change. In 2010, a few thousand battery-powered electric cars were sold in the world. In 2020 sales will be counted in millions of vehicles. Sales of electric cars are now increasing rapidly at the same time as sales of fossil fueled cars are declining. If we follow the trend curve, electric cars will have completely taken over the market during the second half of this decade.
This example demonstrates the challenge that companies face. Investing in development and manufacturing of disruptive technologies involves enormous costs for many years before any profits can be made. It may take more than 10 years to scale up production and become profitable. It is an extremely long time compared to the payback time many companies have set as a limit for an investment.
When a new technology emerges, it is often (but not always) more expensive to manufacture compared to the existing technology. That comes down to the volumes manufactured annually.
There are several “laws” that show how costs fall over time with increased volumes. Best known is perhaps Moore’s Law, formulated by Gordon Moore, one of the founders of Intel, which says that the number of transistors on a chip doubles every two years. Thus, the cost of computing power also decreases at the same pace. Moore’s Law has proven to be fairly accurate for all possible areas of technology, although researchers have pointed out that there is a fundamental flaw – the assumption that the cost drops over time. A more accurate law, according to the researchers, is Wright’s Law, which says that cost falls with rising volumes.
But why will the cost drop when volumes increase?
The intuitive answer is that gradually as the volumes increase, the players learn to handle the new technology and how to organize their communication. Errors, hassles, rework and misunderstandings are becoming fewer as they are sorted out, unnecessary steps will be taken away, tailor-built production lines will become available and the supply chain is trimmed.
Another way to explain what happens is based on complexity theory. The number of actors in the system increase as the volume increases. And the actors start to self-organize. When actors in the system increase their ability to self-organize and create relevant emergent information, local conflicts decrease. That is, the cost decreases as the actors are allowed to self-organize and thus can learn to master the technology and their communication.
But not all new technologies will ever reach the volumes that will substantially cut cost. One reason might be that the market is too small and the number of potential customers is limited. One other important reason is that time matters. It is not possible to pursue challenging development if the execution is too slow. Not only will the investors hesitate to put in money when the payback time is very long and the risk very high. There is also a growing decision debt that eventually will jeopardize the success.
A company doing product development can be seen as a system that both creates its own information (new knowledge) and receives information from the outside world. As new technology is being developed, the work gives rise to more information. This information must be handled and decisions made at the same pace as information arrives at the system. Then the information becomes useful in defining the product and process design, thus creating boundaries that will channel the energy.
If decision-making is too slow, the emergent information will increase and the business’s ability to self-organize will decrease. That will be seen as a lot of information, that doesn’t create any meaning, is circulating in the system. The cost will go up as momentum slows down, making the hill to climb ever steeper. The business has created a decision debt that will slow down an already slow progress even more.
In order to maintain the balance between self-organization and emergent information, work must be provided, in this case in the form of decisions. The proof follows from the second law of thermodynamics; work cannot arise out of thin air.
We can also learn from this reasoning that volume alone is not the key to low cost. That can be seen from many acquisitions and mergers that have failed to deliver added value. Instead it is the self-organization among many actors and the ability to make decisions that will lower the cost.
So the first conclusion is – in order to make innovations, the business must be able to make quick decisions to get momentum.
I can see that many businesses today fail in that respect, not only because the internal decision–making process is too slow, but mainly because a lot of time is spent on waiting, e.g. waiting for suppliers, waiting for production, waiting for test results. With those obstacles you are doomed to fail. Vertical integration is a popular word today. It means that the business is able to do more in-house. To succeed in innovative product development, you have be able to build a mock-up and test it within a few days, you should be able to build a prototype within weeks, and you should be able to produce the first demo product to customers within months. Projects that take longer than 4 months will probably not succeed. This is a tempo many thought was impossible, until Tesla started to execute in this speed. When we consider the importance of fast execution, Moore’s Law can be seen as a deliberate strategy for Intel. To develop new chips, they needed to keep the two-year cycle of development. Tesla currently dominates and in a rapidly growing market and they have managed to increase their market shares thanks to the pace of decision-making and actions.
So the second conclusion is – in order to make innovations, the business must be able to do more work in-house (vertical integration) so that feedback is generated faster and more abundantly.
There is also a magic wand in the increasing volumes. When actors in the system increase their ability to self-organize and create relevant emergent information, complexity increases and reaches its maximum. Complexity is here a good thing, because at that turning point the system as such can expand, over time generating more value overall. The system is said to “be balancing on the edge of chaos”.
One consequence of this is that the decision-making process has to continue speeding up to keep up with the increasing demands. That is especially tricky as the business is growing. More actors are introduced, more communication is generated, and more decisions are needed. The organization must be built so that it can handle new problems, new products, and new working methods. What is needed is an agile organization, that over time can learn, that is self-organize, to handle a large number of cases of widely different types and thereby reduce time and cost for each individual case. An agile organization can handle situations that have never happened before and also grow with increasing demands for fast decision-making.
So the third conclusion is – in order to make innovations, agility of the strategic management team is crucial to focus the resources.
There is never a lack of good ideas, some of them even brilliant, in a business. But the resources are always limited, so only a very small proportion of the ideas should be pursued. The bottleneck will always be decision making on a strategic level. What is the long term vision, what are the major steps towards that goal, what actions are needed today to come closer, and what ideas and options should be left untouched? With the strategic decisions, the business can focus its resources and step by step achieve the long-term goals.
Innovations will thus require agile strategic management, which in practice means that the management team meets daily or bi-daily to do work and make decisions. The strategic and operational levels also need to be closely connected. Problems, decisions, actions and feedback should flow at a high rate between the management team and the projects, as they do in a Parmatur Pulse-network. Then the projects are able to get strategic decisions within a couple of days. With a network of pulsed meetings, the business can also reshape itself as the system expands and can thus meet the increasing demand for quick decisions.
So the fourth conclusion is – in order to make innovations, strategic management and operational work must interact closely, so that problems, decisions, actions and feedback can flow freely between them.
As a wrap up follows my conclusions on what is needed in order to make innovations. The business must be able to:
- Make quick decisions to get momentum. Speed in execution is essential. Otherwise the decision debt will grow and slow down progress.
- Do more work in-house (vertical integration) so that feedback is generated faster and more abundantly.
- Maintain high agility of the strategic management team to focus the resources.
- Interact closely between strategic management and operational work, so that problems, decisions, actions and feedback can flow freely between the levels.
Reference: Fernández N.; Maldonado C., Gershenson C. (2014) Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis. In: Prokopenko M. (eds) Guided Self-Organization: Inception. Emergence, Complexity and Computation, vol 9. Springer, Berlin, Heidelberg