Pantea Lotfian PhD, Camrosh Ltd.
The process of innovation is usually considered a complicated affaire. A lot of uncertainty and unknowns characterise the early stages and as the innovation process is followed systematically, it becomes refined, and gradually the best options are selected, details emerge and innovation becomes a reality in the form of a new process, product, strategy etc. However, there is something else happening in parallel with this, which we rarely pay attention to. Innovation is complex not complicated and is becoming increasingly so. This is because as we move through the process the environment is continuously changing and as the innovation is taking shape the impact of a changing environment will nudge innovation from being a complicated process to complex process. It is crucial to be able to identify when we are facing complexity instead of something that is just complicated.
We use the words complex and complicated in different contexts on a daily basis, and often interchangeably. However, there are crucial differences between complicated and complex:
A complicated system has many components that can have very many interactions with each other. However, these interactions all follow a specific pattern. This makes it possible to predict how a complicated system behaves. For example the majority of transport systems we use today such as a cars, trains, ships, airplanes and spacecraft are complicated systems with varying degrees of complicatedness, however due to the existence of predictable and purposefully designed components and their well understood interactions in the process of their respective functioning, operating them becomes possible with a high degree of predictability and safety.
Complex systems are also multi component and may behave in patterned ways, however; their interactions are continuously changing. The properties of a complex system depends on three elements:
- the number of potentially interacting elements
- the interdependence level of connectedness of those elements
- the level of heterogeneity of the components
Complexity of a system increases with the increase in any one, or all of the above mentioned factors: multiplicity, interdependence and heterogeneity. The implication is that in complex systems the same starting conditions as in a complicated system will produce very different outcomes, depending on the interactions of the units in the system. This makes such systems emergent; that means they can show unexpected behaviours resulting from the internal reactions between units or interactions with the environment. Examples of complex systems are financial markets or an air traffic control system. Nature as a whole is obviously the ultimate complex system, but here I would like to limit the discussion to man made complex systems.
The problem starts by the very act of trying to create a reproducible process within a naturally emergent system where we try to reduce the complexity to manageable complicatedness through creating boundaries based on our assumptions.
In order to be able to create anything at all we have to make assumptions and set boundaries; however, in order to be successful when dealing with complexity before making our first assumption we have to be aware that we are dealing with complexity rather than just something complicated that might have some underlying blueprint that we might figure out at some point.
Why does this matter?
This may all sound a bit philosophical, but often businesses try to increase the pace of innovation by increasing the variety and number of their products or services. This leads then, to the surprise of the keen innovators, to stagnation and decrease of profitability. When the reasons for declining profitability despite innovation initiatives is investigated, the root cause is often identified as increased complexity within the organisation, particularly in operations and as a result unanticipated rising costs. Bad economic data, overoptimistic sales expectations and entrenched managerial assumptions are also causes of the spread of complexity through the organisation. The effects of these factors are interrelated and compound the resulting complexity in unexpected and nonlinear ways.
Increasing complexity also carries other challenges namely unintended consequences, both positive and negative, and difficulty in making sense of a situation.
Unintended consequences are mainly the result of interactions in parts/events in the system, without being initially considered or intently initiated. This often happens when products and services are developed without a full understanding of the environment, which sometimes is not fully accessible .
Environment here stands for variables such as technology and design aspects of a product/service, customer preferences and unexpected behaviour, regulatory and legislative changes and sometimes lack of change in regulation, which can prevent the use of advanced technologies in a context based on trust from customers, such as aviation or medicine. Another example of where complexity can be a barrier to innovation is a highly intertwined supply chain system within and between industries. Often a crisis in one part of an industry or an adjacent industry may cause failure to innovate for a company when it is unaware of how the industry and its supply chains are transforming.
Finally rapid change in the business ecosystem due to rapid advances in digital technologies, in particular of the sort that enables players from entirely different markets to enter and successfully compete with incumbents for products and services can outperform any innovative products that were painstakingly developed for the incumbent’s markets. Sometimes the success of new entrants is mainly due to new business models and network effects that they bring into the new market rather than the product features itself.
More than ever businesses need to scan the environment to detect shifts in technology trends, in markets, and in the competitive landscape that they operate in. The causes at the basis of many of these shifts are:
- New levels of complexity
- Transformation of the basis of competition, mainly through disruptive digital technologies particularly Artificial Intelligence, blockchain, the Internet of Things and, Augmented and Virtual Reality.
- Challenges of regulation for emerging industries
- Skills surplus and shortage
- Security and privacy
- Consumer trust
- Indirect competition (industries that have never been your competitor will become your competitor for higher value products and services)
In order to work with complexity there is a need to break things down into smaller units and simplify. However, the key for success is to maintain and cultivate awareness of the underlying dynamic complexity of the context at hand. This helps to avoid the often detrimental mistake of taking assumptions that were made in order to create “workable simplicity” as facts. Well-informed awareness of complex systems enables the utilisation of their emergent properties to generate truly novel business opportunities.
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- Learning to Live with Complexity by Gökçe Sargut and Rita McGrath Harvard Business Review September 2011.
- Innovation Versus Complexity: What Is Too Much of a Good Thing? By Mark Gottfredson & Keith Aspinall; Harvard Business Review, November 2005.