Types of Organization Problems

Too often people jump right into decision-making without fully understanding the problem being solved. And this failure goes beyond clarity of the core problem to how the problem should be classified since that classification provides guidance for the problem-solving methodology. One broad way to classify problems is the four categories found in the Cynefin framework that comes out of the study of complexity science. These categories can best be understood as how cause-effect relationships exist in order to see how the underlying core problem drives the symptoms experienced.

· Simple cause and effect are what we like to think of when we experience the symptoms of a problem — something is causing the symptom and if that cause were eliminated, so too would the symptom disappear. An example is running over a nail that punctures a tire. Even with the quality of tires today, there is likely a small leak that eventually leads to a flat tire. At worst, you have a sudden blow-out. However, things are rarely this simple.

· Complicated cause and effects involve multiple linkages that can be analyzed and understood individually and in a series of impacts. A car in total is very complicated in that there are thousands of parts all working together with a simple point of failure starting a cascade of symptoms. Driving into a curb can damage the wheel or alignment that then impacts both tire wear and steering. Taking the car to the shop, the mechanic may look at the symptoms and ask, “did you accidentally drive into a curb?” Your response might be “no, but I wonder if my son might have.” These are problems we like. Diagnostics show the linkages between all the factors at play, sufficient to know where to make an adjustment to produce the desired result. Correct the wheel alignment and steering and tire wear improve. We can also make the mental note –driving into a curb is ”bad” and likely costs money to fix. These problems are of the Industrial Era where the machine is our model for explaining how things work. Unfortunately, when people are put into the cause-effect relationship this predictability starts to break down.

· Complexity involves feedback loops that make prediction difficult. (Note that we are getting more technical, drawing from complexity science with a critical difference between complicated and complex. When someone says something is “complex” you may need clarification if they are in fact making this differentiation.) Picking up on a car to illustrate this point, the car is complicated but how you feel about it is complex. For example, how does a young man feel about the car he is driving when picking up a girl he wants to impress if the car is his mother’s old SUV with multiple dents and scratches. Compare that to the confidence he might feel and show when the car is his sportscar freshly washed and waxed. Both cars are complicated but the young man’s relationship with the cars would be very different. This does not mean that complex problems cannot be diagnosed, only that probabilities and patterns come into play and not specific linkages of cause and effect relationships. Aspects of management control are built on an assumption of complicated relationships applied to people with freewill (hint, not controllable but can be influenced). This difference between complicated and complex is the driving difference in how we see the world as a machine or living organism with dynamic relationships.

· Chaotic situations are interesting in that things seem random in the present, but relationships can be discovered in hindsight. For example, a multi-car accident on the highway is an extremely chaotic situation to experience. However, using eyewitness reports and the field of destruction, the highway patrol officers can piece together the series of events that occurred. Prior to the event, patterns can be predicted drawing on the complexity of traffic conditions and degree of stress drivers may experience. Going back to the young man heading out on a date — once he crosses into the next lane while texting his girlfriend that he is running late, chaos follows that can only be fully understood in hindsight.

The importance of understanding the type of problem faced becomes evident in how best to respond to that situation. In the Industrial Era, Best Practices worked great — all you had to do was to identify the situation which then connected to a response based on prior learning. This of course requires situations that are repeatable which may apply to problems that are frequently encountered. Of course, this repeatability also establishes a foundation for TQM (Total Quality Management) — identify and prevent the cause to improve the process quality. The reality for most problem-solving and decision-making is complicatedness where there is no best solution. There may be several answers that are “good enough” but each has some limitation that keeps it from being perfect without some modification. Each of the problem categories above has a different way to best respond.

· Simple problems have known solutions that are captured as Best Practices. The problem-solving approach involves Sensing the situation, Categorizing it within the context of best practices, and then, Responding. A flat tire is taken to a tire shop where standards are in place regarding the size and location of the puncture, thereby either fixing or replacing the tire.

· Complicated problems do not have a set of best practices prepared in advance but are knowable. Similar to simple problems, the first step is Sensing the situation but is then followed by Analysis to trace the cause-and-effect relationships in play. The analysis then drives the appropriate Response. A car taken to a mechanic with poor steering will require several observations and possibly a road test to isolate the problem. The problem may be tire wear, alignment, suspension, steering linkage, or ??? Or, a combination of issues. Once a determination is made on the core problem(s), a recommendation for repair can be provided to the customer.

· Complex problems require an Emergent Practice that is crafted for each situation. This requires Probing the situation by clarifying questions or testing, Sensing how the system responds or is likely to respond, and then making a decision on which Response may be best. For example, the choice of which car to take will depend on whether the young man is going out on the town with his girlfriend or taking her and her parents to church. This situation is complex in both purpose that day and who is involved and past patterns can be used to guide the response.

· Chaotic situations occur in the here and now with little guidance. There is no time to sense, probe, or test the situation, action is needed. Only after action is taken, can you sense the impact on the situation and then respond accordingly. Seeing an accident ahead requires quick action — brake (cars behind?), go around (barrier?), or brace for impact (prepare for airbag deployment). However, any action you take is not in isolation but in relationship with other drivers who are also reacting. Each action is not an end-state, but sets up the next decision that may be needed depending on what you did as well as action taken by others. The decisions made will be unique (novel) since that set of circumstances will never happen in the same way again — there may be similarities if you are so unfortunate but still different.

Application

What problem are you now facing? What cause and effect drivers are in play?

Using the Cynefin Framework above, how would you categorize the problem?

How might you proceed (SCR, SAR, PSR, or ASR)?

And, who should you involve to confirm your categorization and set up a coalition for action?