Ditch your Pareto, take on the Shainin Red X
Many years ago I had a full crop of hair on my head and a keen mind inside it. I am pleased that at least one of these statements remains! At the time I was fortunate to attend training where we explored and tested the Shainin methods of performance improvements. The lesson’s techniques and advise I gained from the training has helped me in many scenarios when addressing performance issues.
We often cite the Pareto method of problem solving as it is a very simple, easy and effective method to make steps occur. In today’s digital age we hold ever more data on various areas of operational and customer behavior. Therefore I feel it is time to revisit the benefits taught to me by Shainin's development of the “Red X”.
The trade marked“Red X” concept grew from Shainin’s work with Joseph Juran. Indeed it was Joseph Juran who is accredited for the “The Pareto Principle”. It was Shainin who extended study and work to conclude that, amongst the potential of variables in a system that could cause a change in the value of an output, one cause-effect relationship would be stronger than the others. Thus the birth of what Shainin called the primary cause the “Big Red X” and further demonstrated that the cause can exist as an interaction among independent variables. The effect of the Red X is then magnified by the square-root-of-the-sum-of-the-squares law, thereby isolating the root cause.
By the use of today’s computing power and standard statistical methods it is possible to test and verify in a cost-effective manner.
In order to determine the "Red X," it is important to swap pairs of parts between functional and faulty equipment/outcomes until the one part responsible for the failure is discovered. Shainin would claim that he could often find the primary defective part within a dozen paired swaps. This principle is has attractive test market, sales, marketing and software applications.
WHY THE RED X Method ?
Shainin's approach and often quoted position of "talking to the parts" was the primary distinguishing factor that set his methods apart from Taguchi's and other design of experiments. The approach reverses the process from often a brainstorm approach to form hypotheses regarding possible causes of a problem. Shainin's methods postpone this theoretical step, requiring first the diagnosis of causes via one or more of four clue generation techniques designed to determine, through the empirical testing of the actual parts in question, the root cause, or "Red X".
There remains many alternative methods of analysis for performance improvement but I respect and take my hat off to Shainin’s contributions.