Editor | On 14, Oct 2007

Michael S. Slocum

We are accustomed to solving problems using a linear two-step process. This process is as simple as problem Â» solution. Our psychological bias is the foundation for our ability to solve problems using this simple process. We have an innate ability to solve problems rapidly that is derived from our automatic intellectual function. This function is a subjective analytic based on the intersection of our education, experiences, and other cultural and environmental bias found in our surroundings.

Psychologists have demonstrated that the automatic intellectual function is ideal for problem solving involving fight or flight scenarios and less than ideal for other problem solving types. We find that the majority of problems that we face are not fight or flight therefore we find ourselves using a problem solving algorithm that is not the ideal most of the time. This is much of the cause for finding ourselves surrounded by products and services that are awash in compromise and mediocrity. These two characteristics allow us to be susceptible to pressure from our competition and the markets we serve. We need to adopt a problem solving process that involves a higher level of intentionality and objective analysis.

Our ability to problem solve will be greatly increased if we incorporate abstraction and analogic thought into a non-linear multi-step problem solving process. This process in its simplest format would be specific problem à generic problem à generic solution à specific solution. We would leverage abstraction for the transition from the specific problem to the generic problem and we would leverage analogic thought to transition from the generic solution to the specific solution. This non-linear abstract and analogic problem solving model defeats the bias innate in the linear two-step model and allows the user to problem solve using the open and potentially adaptive innovative approach. The quality or solutions generated using this approach will be considerably higher. Also, compromise will be minimized thereby reducing competitive opportunities.

Much is to be gained by adopted non-linear processes for problem solving and we can see evidence of this by looking at the non-linear problem solving model found in Six Sigma. Six Sigma uses a model that is comprised of the following key steps: practical problem à statistical problem à statistical solution à practical solution. This model allows for the utilization of abstraction and analogy with a focus on statistics. Statistics allows for the inclusion of qualitative and objective (data based) decision making. We can include these same benefits in problem solving that does not involve optimizing a closed system. We would call this innovative problem solving. Innovative problem solving allows for the generation of solutions that are outside the system (open) as well as the opportunity to adapt someone else’s solution to solve your problem. These are critical elements of the science of innovative problem solving and are leveraged when a non-linear problem solving method is adopted.