Using Systems Thinking to Target Innovation

Everywhere, competition is more intense, with companies and departments having to do more with less. As competition rises, so does the importance of getting the greatest benefit out of every innovation effort. One of the most powerful ways to do that is to target those efforts, so they produce innovations that are strategically useful and genuinely valuable to the organization.

Getting the most out of innovation

The key to making an innovation lead to real improvement is understanding where it fits into the bigger picture of the company and its needs. Systems thinking, a field pioneered by Professor Jay Forrester of MIT, plays a central role in producing that understanding. It supplies a methodology and a set of tools for mapping systems and locating the points where change can have the greatest impact on performance. What follows is a short introduction to some of its foundations, and a demonstration of how using it with innovation efforts sharply increases the chances that those efforts create lasting value.

The systems thinking approach

The approach of systems thinking is fundamentally different from traditional analysis. Instead of focusing on the individual pieces of what is being studied, it focuses on the feedback relationships between that thing and the other parts of the system. Rather than isolating smaller and smaller parts, it takes a broader view, taking in larger and larger numbers of interactions. In that way, systems thinking builds a clearer picture of the whole.

Innovation with the system in mind

Consider a department at an agricultural firm asked to reduce the crop damage caused by insects that have grown resistant to common pesticides. One way to approach the problem is to build an especially strong pesticide, potent enough to kill even these resistant insects, and to hand the researchers that assignment. The reasoning behind that course of action looks like this:

Causal link: more pesticide application, fewer insects damaging crops, an opposite relationship.
The conventional view: more pesticide, fewer insects damaging crops. The “o” marks an opposite relationship, so as pesticide application goes up, the number of insects damaging crops goes down.

The trouble is that the researchers have been asked to act on an incomplete picture of the system. Their success at producing a stronger pesticide need not translate into lasting benefit for the company. In fact, the strategy can backfire, because the policy leaves out the feedback relationships involved.

A view of the big picture

The fuller picture captures the interactions that are likely, in fact, to make the strategy backfire:

Causal loop diagram of the full pesticide system: the stronger pesticide reduces the target insect short-term but kills the insects controlling it, so after a delay the target explodes and total crop damage rises, prompting more pesticide.
The whole system. The stronger pesticide cuts the target insect in the short run, but it also kills the other insects that had been holding that insect in check. After a delay, the target population explodes, total crop damage climbs, and the company sprays again.

The stronger pesticide does reduce the target insect, and total crop damage, in the short run. But because the target insect is more resistant than the rest, the pesticide kills even more of the surrounding insects, some of which had been controlling the target insect by preying on it or competing with it. As that control weakens and the target insects recover, their population explodes. Total crop damage rises, which encourages the company to spray again. The temporary gains fade as the target insect grows more resistant, and the damage keeps getting worse. What worked well at the start no longer does, and the benefits of the company’s innovation evaporate.

Local success, global failure

The very effectiveness with which the researchers did what they were asked, creating a stronger pesticide, made the original problem worse, because the side effects of a more powerful pesticide were never considered. An understanding of those interactions would have shown that the plan was likely to backfire. It would also have opened other options that would not: introducing more of the target insect’s predators into the area, or developing crop strains more resistant to insect damage. Either assignment would have produced an innovation that fit the big picture, and created substantial, lasting benefit.

The benefits of big-picture innovation

Systems thinking delivers some of its greatest value by making sure a company’s innovation efforts are not undercut by the absence of a big-picture understanding. Without requiring any additional resources, innovation targeted with the big picture in mind produces greater, lasting benefits, and a company that gets more from its innovation efforts holds a competitive advantage over its rivals.

Daniel Aronson wrote this as a principal at SuccesSystems and a member of the System Dynamics Group at MIT. A version of this article appeared in R&D Innovator (later Innovative Leader), Volume 6, No. 2. © Daniel Aronson.

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