Synectics as a creativity technique

One creativity technique that helps to harness unconscious thought processes for the problem-solving process is synectics. Developed by William Gordon in 1944, the synectics method is based on looking for analogies from nature or from the participants’ personal areas of experience, relating their solution to the existing problem and deriving concrete approaches to solving it.

Experience shows, however, that finding analogies is not easy for many people. An experienced facilitator can make the use of the method more successful. The principle of synectics is based in particular on bionics. It is based on the assumption that nature has developed optimal processes and structures in the course of evolution from which we can learn. Bionics provides sources of ideas that can be applied to problems in mechanical engineering, electrical engineering, product development projects, business processes, and so on.

Two methods are used: (i) according to the top-down principle, solutions can be sought for concrete problems in biology; (ii) recognized solutions in biology can be abstracted and used as templates for concrete challenges in other disciplines (bottom-up). The latter approach is also referred to as biomimicry because nature is deliberately scanned for useful patterns that can be transferred.

Examples of biomimicry include mimicking birds to develop flying objects (motion bionics), developing low-pollution paints and surfaces from observing the microstructure of plant surfaces (structural bionics), transferring biological structural or organizational principles to technical structures and organizations (also structural bionics), deriving technical information systems from natural information transmission systems (neurobionics), or optimizing the stiffening of double-wall structures by understanding biological design elements (design bionics).

Analogy techniques have in common that essential features of the situation in question, in which a problem is to be solved, are captured and then other environments are searched for to which these features also apply. Then it is observed in these environments how the current problem is solved there. Subsequently, the solution path(s) found is/are related to the challenge at hand before concrete solution approaches can finally be derived.


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