Professor of Acoustics, Geophysics, Innovation emeritus







  • The more complex the system, the more scientists should rely on measurements.

  • We can never achieve a big step forward if we stay within the same concept

  • ‘Logic based activities will be taken over by smart robots. In the future mankind will focus on creativity and empathy’.


Cyclic Innovation Model (CIM)


Limitations of current innovation models

Traditionally, innovation models are linear (explicit or implicit). They describe the processes along the innovation path as a causal sequence (much like in relay): investments in scientific research must lead to application-oriented development routes that subsequently ought to result in successful market introductions. If we invest enough in science and technology the rest will work out all right, that is often the reasoning1. Such a linear science-push approach in innovation policies is still taking place on a large scale, with the result that most innovation systems underperform. The limitations of current innovation models can be summarized as follows:

  • Most models show innovation paths, representing a stage-gate type of activity and controlling the progress from idea to market introduction, rather than giving insight in the dynamic properties of the innovation processes themselves;

  • Science is viewed primarily as technology orientated (natural and life sciences) and R&D is closely linked to manufacturing, causing insufficient attention to the social and behavioral sciences. As a consequence, the emotional (or soft) components of innovation – being responsible for many failures – are hardly addressed;

  • The complex interactions between new technological capabilities and emerging markets are a vital part of the innovation process, but they are underexposed in current models;

  • The role of the entrepreneur (individual or team) is not captured.

Everything around us is changing, innovation processes need to change as well

In almost every natural system, feedback is an essential phenomenon. This means that there exists a path that carries part of the output back to the input. Mathematically, this phenomenon is described by an integral equation of the second kind. In ecological systems we find an abundance of feedback paths. That makes them very complex and, therefore, human interference often has unexpected consequences which we do not understand. Think of the complex human interaction with the earth’s natural system and the related debate on climate change. Another example is on micro scale, where we observe that the living cell – considered to be the most advanced chemical factory – is full of feedback (see Figure 1). Molecular biologists believe that nature aims at minimum-energy systems and that minimum energy requires many feedback paths. If this is the case, we must conclude that our current models, being characterized by little feedback, are squandering a lot of energy. This also applies to innovation models. Innovation processes should not be forced into simple one-way pipelines, but rather be organized by interconnected cycles with feed forward and feedback connections: from linear to nonlinear thinking. In that way, a dynamic network environment is created in which the soft sciences are linked to engineering, and where the hard sciences connect with market goals (Berkhout, 2000). This is what is captured in the proposed innovation framework. Supported by today’s powerful communication technology, serial process management along a linear path is replaced by parallel networking along a largely self-organizing circle. Vital decisions in innovation do not occur in the gates of a staged project management pipeline, but do occur at the process floor itself. Or, in terms of the proposed model, at the nodes of the cyclic networks. It is our experience that young people like to work in such an environment.

1 The innovation policy in the European Union aims at R&D budgets of the member states that amount to at least 3% of their GNP.Guus Berkhout

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Figure 1: The human cell is one of the most impressive examples of intelligence by feedback.

In the following we will introduce the concept of the cyclic innovation, showing the model of a high-information, low-energy innovation system.


Double dynamics around technological change

Figure 2 shows two linked cycles, forming a double loop with technological change in a central position. The cyclic interaction processes for the development of new technologies take place in the hard sciences cycle (left-hand side of Figure 2) with the help of a wide range of disciplines from the natural and life sciences. Technological change in this cycle is a cross-disciplinary activity: a team of scientists from different disciplines of the hard sciences is needed to develop a new technological ability (many-to-one relationship). In the last decades, we have seen that industrial firms have outsourced a large part of their science-based technological research to universities. Note that in Figure 2 the hard sciences deal with quantitative models that not only explain the properties of physical systems (‘know-why’), but are increasingly capable of predicting their behavior as well. This predictability allows us to develop reliable technology with fully repeatable behavior (‘know-how’).


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Figure 2: The dynamics surrounding technological change are driven by the cyclical interaction between new discoveries in the hard sciences (left-hand side) as well as technical specifications for new product combinations (right-hand side).

Similarly, the cyclical interaction processes for the development of new products take place in the integrated engineering cycle (right-hand side of Figure 2). Modern product development is a cross- technology process in which a package of different (often patented) technological abilities is needed to design and prototype a new product (many-to-one relationship). As in cross-disciplinary science, here too we see that many different experts are needed to succeed. Nowadays, we observe that in most industrial firms specialized skills of technical suppliers from outside the firm play an important role in making the engineering process successful. This is consistent with the open innovation concept (Chesbrough, 2003). Note that ‘products’ refer to everything mankind builds: not only tangible products like houses, cars and computers, but also non-tangible products like websites, games, insurance policies, agreements, rules and laws. And combinations thereof. This means that engineering should integrate hard and soft components.

Figure 2 visualizes that in the hard (natural & life) sciences cycle, technological change is driven by new scientific insights: science push. It also shows that in the engineering cycle technological change is driven by new functional requirements in product development: function pull. The dynamics in technological change are therefore driven by new scientific insights as well as new product specifications. In a dynamic technological infrastructure, scientists and engineers must constantly inspire each other. To achieve this, research must be organized in a different manner: no barriers between the two cycles. In Figure 2, the technological node should function as a knowledge-driven roundabout.


Double dynamics around market transitions

Figure 3 also shows two linked cycles. In this case it is the world of market change rather than the world of technological change that plays the central role. The cyclical interaction processes for the development of new insights into emerging changes in demand – causing rising and falling markets – take place in the soft sciences cycle (left-hand side of Figure 3) with the help of a wide range of different disciplines from the behavioral and social sciences. With these insights, new socio-technical solutions can be developed faster and with less economic risk. Understanding changes in demand is very much a cross-disciplinary activity: a team of disciplinary experts from the soft sciences is needed to assess and foresight shifts in societal needs and emotions as well as changes in trade conditions and regulations (many-to-one relationship). We see in all industrial sectors an increasing interest for this type of research, meaning a shift toward a more scientific approach to market intelligence. Note that in Figure 3 the soft sciences deal with socio-economic models to explain the properties of markets and the underlying behavior of consumers. Until today, the predictive power of these models needs improvement.


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Figure 3: The dynamic surrounding market transitions are driven by the cyclical interaction between new scientific insights in changing behavior of consumer groups (left-hand side) as well as industrial propositions of new product-service combinations (right-hand side).

Likewise, the cyclic interaction processes required to serve the changing society with new product-service combinations take place in the differentiated services cycle (right-hand side of Figure 3). In this cycle, services are seen as an invaluable link between products and markets: the combination of products and services determines customer value. Users play an increasing role in making the innovation process successful: product development 2.0. Utilizing the creative input of customers is known as democratizing innovation (Von Hippel, 2005). It is interesting to note that in recent years the services sector has expanded considerably, not only because of the greater demand for services from the end-user but also because industry has outsourced many of its non-core processes. This trend is still going on, and will play an indispensable role in the forthcoming cleantech era.


Combining two different worlds

If we compare Figures 2 and 3, the dual nature of scientific exploration and product development becomes clear: science has both hard and soft aspects, and product development has both technical and social aspects (Figure 4). In innovation it is essential that these aspects are integrated early in the process. This is exactly what is proposed in the Cyclic Innovation Model (Berkhout, 2000; Berkhout et al, 2007). CIM provides a cross-disciplinary view of change processes (and their interactions) as they take place in an open innovation arena (see Figure 5). Behavioral sciences and engineering as well as natural sciences and markets are brought together in a coherent system of synergetic processes with four principal nodes that function as roundabouts. The combination of the involved changes leads to a wealth of business opportunities. Here, entrepreneurship plays a central role: making use of those opportunities. The message is that without the drive of entrepreneurs there is no innovation, without innovation there is no new business, and without a new business there if no economic growth. Figure 5 shows that the combination of change and entrepreneurship is at the basis of new business.


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Figure 4: In innovation it is essential that the hard world of changing technical capabilities is combined with the soft world of changing needs and concerns, leading to one holistic system.

The most important feature of Figure 5 is that the model architecture is not a chain but a circle: innovations build on innovations. Ideas create new concepts, successes create new challenges, and failures create new insights. Note that new ideas may start anywhere in the circle, causing a wave that propagates clockwise and anti-clockwise through the circle. In an innovative society partnerships are abundant and the speed of propagation along the circle is high, resulting in minimum travel time along the innovation path. Today, time is a crucial factor in innovation.


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Figure 5: The Cyclic Innovation Model (CIM) presents the processes in innovation by a circle of change. Changes in science (left) and industry (right), and changes in technology (top) and markets (bottom) are cyclically connected. Nodes function as roundabouts, entrepreneurs generate the driving forces.

Figure 5 also shows that the proposed innovation model portrays a system of dynamic processes – circle of change – with four “nodes of change”: scientific research, technological change, product development, and market transitions. But more importantly, between these nodes there are “cycles of change” by which the dynamic processes in the nodes influence each other. In other words, they inspire, correct, and supplement each other (first-order dependency). This produces a system of linked cycles, which in turn also influence each other (higher-order dependencies). The result is a more or less synchronized regime of highly non-linear dynamic processes that spark a creative interaction between changes in science (left- hand side) and industry (right-hand side), and between changes in technology (top) and market (bottom). These are the dynamics referred to by Richard Florida (2005). Note that in this type of self-organizing complexity, causality is not a meaningful concept anymore. Borrowing an observation from the famous Austrian physicist Wolfgang Pauli (Donati, 2004), many processes interact and we can no longer distinguish between cause and effect. Innovation resides in the world of self-organized chaos, steered by the ambitions of the entrepreneurs. At a lower level, CIM reveals that each cycle consists of a network with a high degree of self-organisation.

Autonomous societal transitions manifest themselves in markets as changes in the need for products and services (the demand). Think of the huge influence of education and emancipation on a society. On the other hand, autonomous technological changes generate new products and services (the supply). Think of the huge influence of mobile and web-based communication technology on society. It is the cyclic interaction of both autonomous innovation drivers, social and technical, that will create new business with a high value for society. In that respect, specialized versions of the proposed model can be formulated, depending on which values we particularly aim for. For instance, if we would like to emphasize changes in today’s energy system – aiming at a decentralized green alternative – then “market transitions” should be replaced by “energy transitions” in Figure 5. Similarly, if we would like to emphasize changes in the global ecological system – aiming at maintaining biological diversity on our planet – then “market transitions” should be replaced by “ecological transitions”. For the coming decades, quality of life will become one of the biggest drivers in innovation worldwide. This means that the transition node in the cyclic process model should be focused on the changing values in society at large: “societal transitions”.

Figure 5 shows that the left-hand side of the innovation circle is directed to research activities of the science community while the right-hand side addresses the innovation activities of the business community. In a productive innovation system, science and business will challenge each other continuously on technology-related (upper part) and market-related (lower part) issues. The transformation to a sustainable society may be the biggest challenge mankind is facing. It requires changes in technology as well as in behavior. The hard and soft sciences should work together with industry leaders to make this transformation happen. In terms of CIM, moving to a sustainable society requires synergy of activities around the entire innovation circle. The question is: who will be the system master?


System errors

From the above it follows that the innovation circle acts as a socio-technical framework that gives insight into the heart of the innovation process by asking the relevant questions, such as: What needs to be done where? Who are the collaborating parties? Where are they active in the circle? Is there a balance in investments between the different parts of the circle? Nobel laureate Robert Lucas points out that exchange of ideas is the principal driver for innovation (Lucas, 1988). So, in terms of the innovation circle (Figure 5), the key question is whether there exists sufficient interaction around the circle. Particularly, for disruptive innovations an environment must be created where a large diversity of people with a broad range of backgrounds can freely interact, discuss ideas, and exchange information. This type of environment requires a significant change in the current institutional and social structures, as disciplinary boundaries are deeply rooted in our organizations and solutions are often a collection of isolated optimizations.

Despite living in an interconnected world, the barriers to creating new business by innovation remain. Figures 6a, b can be used to indicate two possible obstacles in today’s innovation practice. First, The scientific research may be excellent, but lack of interaction with the industry will lead to a situation that results are not translated into business applications (in time). Here, science and industry have separate agendas (Figure 6a). Second, a company or industry or society that may excel in designing and building technical solutions, but it may still underperform because of a lack of interaction between the technical and consumer communities. Technology push can only succeed when connected to (emerging) consumer needs (Figure 6b).


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Figure 6: A society may be excellent in science, but it may underperform in innovation due to insufficient synergy between science and industry (left-hand picture). A society may be excellent in technology, but it may underperform in innovation due to insufficient synergy between technical capabilities and consumer needs (right-hand picture).

Synergetic alliances and complementary partnerships will empower the innovation processes along the innovation circle far beyond what we see today. I expect that we will move to a symbiotic society.


Conclusions

Innovation projects must not be managed along the familiar linear pipeline but should be organized via cross-disciplinary networks along an innovation circle with ample internal feedback paths. Innovation may start anywhere on the circle and previous innovations will inspire new ones: innovations build on innovations. In such an organized chaos, causality is a meaningless concept and modern communication tools are indispensable. Experience shows that in innovation a shared mental framework is essential to allow synergy between the large number of highly diverse players. The Cyclic Innovation Model (CIM) is such a framework, being proposed to transform our current economy to a circular system: the circular economy.




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