Thursday, 11 April 2013

ROOT CAUSE ANALYSIS





Root Cause Analysis (RCA) is a popular tool in the engineer’s problem-solving tool kit. Virtually, it implies that there is only one root cause, when in reality most problems are usually caused by a complex combination of several factors, some of which are more significant than others.

As quality managers we are constantly facing problems that must be solved. However, the RCA effort sometime does not result in complete success, so it is important to understand how things can go wrong. The first opportunity for increasing the risk of failure is usually caused by the problem-solvers themselves. If they choose to solve the problem by themselves, the probability of getting to a robust solution in a timely manner is put at risk. The smart analyst will opt for having a multidisciplinary team helping him to explore the possible causes and potential solution to the problem.




The additional brainpower will generally help, but if there is no structured approach for investigating the problem, then again the probability of failure increases. To address this issue, the sharp problem-solver will usually have the team brainstorm the problem, its causes, and possible solutions (brainstorming is used by about 90 percent of all problem-solving teams). This approach improves the chances of success, but there are several pitfalls in brainstorming that can severely impair its effectiveness.

The hidden deficiencies of the brainstorming approach are:
  • It promotes linear thinking. 
  • It promotes groupthink. 
  • It may lead to a cause but not the causes. 
  • It can lead to a minor cause and miss a major one. 
  • It offers only a limited recognition of multivariate causality. 
  • It does not recognize nonlinear causes or quantify their effect. 
  • It does not recognize causal interactions or quantify their effect. 
  • In fact, it does not quantify the magnitude of the effect of any causal factor. 

To deal with some of these issues requires a structured approach to brainstorming that guides the problem-solving team through the various categories of potential causal factors so that the team does not overlook some potentially important causes. The tool that is often used is the cause and effect diagram (aka a fishbone or Ishikawa diagram, see figure 1). For manufacturing problems the typical causal categories are manpower, machines, materials, methods, measurements, and the environment, also referred to as the 5Ms and 1E. (There are other methodological and psychological tools to increase the efficiency of the brainstorming activity, but we won’t discuss them in this short article.) Using this structured approach to the investigation helps to mitigate some of the issues listed above, but it does not remove all of them.



Figure 1. The cause and effect diagram.


The next trap the typical problem-solving team members fall into is thinking they know the so-called “root causes” based on their subjective judgment. Looking at a fishbone diagram like the example in figure 1, they declare that X2 and X3 are “root causes.” In one sense they are root causes because they are the ends of a root, but that is just a picture. The real issue is their effect on the response variable Y. The hidden, but invalid, assumption is that there is a perfect correlation between the “root cause” Xs and the response Y. Expressed mathematically this is r(X2, Y) = 1 and r(X3, Y) = 1.

The problem is that the deeper the “root cause” is in the tree diagram, generally the lower the correlation between the so-called “root cause” and the actual effect (Y). Further, it can be shown that the longer the causal chain, generally the smaller the effect on the response variable Y, and the greater the modeling error (i.e., the poorer the predictability of the model). So, in fact, a “root cause” may have only a minor effect on the response Y, and an inconsistent one at that. Unfortunately, frequently the improvement team falls into this trap. It makes the prescribed adjustment to the “root cause” variable, only to discover that process improvement is negligible.

So how can the practitioner improve her chances of finding a viable solution to a given problem? The best approach is a properly designed, sequential set of experiments. If a solution exists, then good DOE offers the best chance for understanding the possibly complex casual relationships while addressing many of the brainstorming deficiencies listed above.

Source: John J. Flaig, Ph.D., Quality Digest, Quality Technology Corner.



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