#261 from R&D Innovator Volume 6, Number 2          February 1997

Applying the Power of Systems Thinking to Innovation  
by Daniel Aronson                                                              

Mr. Aronson works as a systems thinking consultant in Cambridge, Massachusetts. He also hosts the Thinking Page at http://world.std.com/~thinking, and can be reached by phone at 617-433-7138 or by email at Dacce@aol.com.                               

Everywhere competition is more intense, with companies and departments having to do more with less.  As competition increases, the value added by each R&D dollar must go up.  One way to increase value is to target efforts so they result in innovations that have major benefits for the organization.                                                                    

A key to achieving giant leaps forward is understanding where innovation fits into the bigger picture of the company and its needs.  A field of study known as "systems thinking" can play a key role in appreciating the overall system, and this appreciation is needed to target innovation efforts more effectively.  Systems thinking provides a set of tools for constructing maps of systems and determining the points at which change can have the greatest impact on a company's performance.  I'll give you an introduction to some of the foundations and concepts of systems thinking, and will demonstrate how to use it with your innovation efforts.      

The approach of systems thinking is fundamentally different from that of traditional forms of analysis.  Instead of focusing on the individual pieces of what is being studied, systems thinking focuses on the feedback relationships between the thing being studied and the other parts of system.  Therefore, instead of isolating smaller and smaller parts of a system, systems thinking involves a broader view, looking at larger and larger numbers of interactions.

As an example of how this better understanding of the big picture can increase the benefits of innovation, consider a department of an agricultural firm charged with finding a way to reduce crop damage created by insects that have become resistant to common pesticides.  One way to approach the problem would be to create an especially strong pesticide that is sufficiently potent to kill even these unusually resistant insects.  The company might then instruct their R&D department to develop such a strong pesticide. The reasoning behind this course of action can be shown as follows:        

In this diagram, the arrow represents the direction of causality, one element causing the other to change.  The o next to the arrow means that Pesticide application causes Number of target insects to change in the opposite way.  Thus, if the application of pesticides increases, the Number of target insects goes down, a change in the opposite direction.  Similarly, if two things cause each other to change in the same direction, the diagram would indicate this by placing an s next to the arrow.                                         

The problem, in this case, is that the R&D department has been asked to do something based on a faulty understanding of the system, and so the department's success at producing a stronger pesticide may not translate into a successful program for reducing crop damage; in fact, the strategy may backfire.  The strategy, while not wrong per se, is incomplete: it leaves out the feedback relationships involved.                                                                       

The diagram below shows a picture of the system that captures the set of interactions that are likely, in fact, to make the company's strategy backfire:   
                                   

While the application of the stronger pesticide indeed reduces the numbers of the crop-destructive insect--and thus the total crop damage--in the short run (as shown in the inner loop from Application of pesticide to Number of target insects), it kills even more of the other insects in the area than it does of the destructive insect because, as mentioned earlier, the target insect is more resistant to pesticides than other insects. (This effect is shown in the outer loop from Application of pesticide to Insects that naturally control the population of the target insect). Some of the insects killed by the pesticide helped control the population of the destructive insect by preying on them or competing with them (as shown by the connection between Insects that naturally control the population of the target insect). When these insects are killed, the degree of control they exerted on the population of the destructive insect is lessened.                                                                                                                                                 

Eventually, as the target insects recover from the effect of the pesticide, the reduction in the control provided by other insects leads to an explosion in the population of the target insect.  As the population of the destructive insect goes up, so does total crop damage, as the link between Number of target insects and Total crop damage shows (the s indicates that the two change in the same direction--as the numbers of the destructive insects go up, so does the total crop damage).     

This leads to even greater crop damage than before, encouraging the company to apply the pesticide again--in the language of the diagram, as Total crop damage goes up, Application of pesticide goes up (with the s again indicating that they change in the same direction). However, even the temporary gains originally made by applying the new pesticide begin to lessen as the insect becomes more resistant to it and, as a result, crop damage continues to get worse.  What worked well at the beginning does not work nearly as well any more.                                                             

In this case, the very effectiveness with which the R&D department did what it was asked to do--create a stronger pesticide--served to make the original problem worse because the side effects of using a more powerful pesticide were not considered.  An understanding of the interactions that produced these side effects would have enabled the company to see that their plan to use a 

stronger pesticide was likely to backfire.  They would also have been able to consider other options that would not backfire, such as introducing more of the insect’s predators into the area and developing strains of the crops that were more resistant to the target insect.  Giving the R&D department either of these tasks would have led to an innovation that would fit better into the big picture and, as a result, would have created greater long-term benefit.             

As this example shows, systems thinking can provide some of its greatest benefits by directing innovation efforts so as not to be compromised by the lack of appreciating the big picture.

Because of the potential to maximize the big-picture benefits of innovation, it has much to offer all innovators.  I hope you take advantage of it.

Of course, this short article can only provide the flavor of systems thinking.  If I have whetted your appetite, you may want to pursue this subject by reading Peter Senge’s The Fifth Discipline:  The Art and Practice of the Learning Organization and his The Fifth Discipline Fieldbook (Currency Doubleday, New York, 1990, 1994).  You may also wish to visit the Thinking Page, above, for additional references and links to organizations in the systems thinking field.