Hunt for the Red X –
That unknown factor or variable that is the key to success!
X is almost always unknown.
Commonly it is an interaction.
Shainin has found many 4 factor interactions, and in 3 cases it was a 5 factor interaction.
What questions would you ask to find a secret word that has been selected from a dictionary? You are allowed to ask questions that can be answered yes or no.
The Red X is like a secret word. We need efficient clue generation strategies to solve problems quickly.
Process of elimination – rapidly converging power.
With a unabridged dictionary, find any secret word in 17 steps. Open dictionary in the middle, look at bottom corner on left hand page and ask, “is the secret work before NONE?” yes or no…
Do again, by half…
This is called a Binary search, cutting it by half with each try. By the eleventh step you will have the word on one page. This is a very remarkable realization. This is a new way of thinking. This is a new paradigm!!!
Average loses information!
Fire at a duck. 1st shot goes to the right, 2nd shot goes to the left. On average the duck should be dead, but he just flies away.
Dorian Shainin was an American quality consultant, aeronautics engineer, author, and college professor most notable for his contributions in the fields of industrial problem solving, product reliability, and quality engineering, particularly the creation and development of the “Red X” concept.
Shainin’s system is a problem-solving tool for medium- to high-volume processes, where data is cheap and available, statistical methods are widely used, and intervention into the process is difficult. It has been mostly applied in parts and assembly operations facilities.
The Shainin System™ (SS) for quality improvement was developed over many years under his leadership. It is also referred to as Statistical Engineering and Red X® strategy. This is heavily used in the automotive sector.
The Shainin System’s underlying principle can be placed in two groups:
1. The belief that there are dominant causes of variation.
2. The belief that there is a diagnostic journey and a remedial journey (the Shainin system algorithm).
A fundamental tenet of SS is that, in any problem, there is a dominant cause of variation in the process output that defines the problem. This presumption is based on an application of the Pareto principle to the causes of variation.
In this case, a dominant cause is defined as a major contributor to the defects and something that must be remedied before there can be an adequate solution. In SS, the dominant cause is called the Red X.
To clarify, if the effects of causes (i.e., process inputs that vary from unit to unit or time to time) are independent and roughly additive, we can decompose the standard deviation of the output that defines the problem as:
Within SS, there is recognition that there may be a second or third large cause, called the Pink X and Pale Pink X respectively, that contributes to the overall variation and must be dealt with in order to solve the problem. Note that if there is not a single dominant cause, reducing variation is much more difficult, since several large causes must be addressed to substantially reduce the overall output variation.
To simplify the language, we refer to a dominant cause of the problem, recognizing that there may be more than one important cause. There is a risk that multiple failure modes contribute to a problem, and hence result in different dominant causes for each mode.
In one case, a team used the Shainin System to reduce the frequency of leaks in cast iron engine blocks. They made little progress until they realized that there were three categories of leaks, defined by location within the block. When they considered leaks at each location as separate problems, they rapidly determined a dominant cause and a remedy for each problem.
SS uses a process of elimination, called progressive search, to identify the dominant causes. Progressive search works much like a successful strategy in the game ‘‘20 questions,’’ where users attempt to find the correct answer using a series of (yes=no) questions that divide the search space into smaller and smaller regions.
To implement the process of elimination, SS uses families of causes of variation. A family of variation is a group of varying process inputs that act at the same location or in the same time span. Common families include within-part, part-to-part (consecutive), hour-to-hour, day-to-day, cavity-to-cavity and machine-to-machine.