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What is The Shainin System™?

Quality Glossary Definition: Shainin System

The Shainin System™ (SS) is defined as a problem-solving system designed for medium- to high-volume processes where data are cheaply 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™ for quality improvement was developed over many years under the leadership of the late Dorian Shainin. The Shainin System™ is also referred to as Statistical Engineering and Red X® strategy (in parts of the automotive sector).

The overall methodology has not been subject to critical review. However, the underlying principles of SS 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), shown in Figure 1.

The Shainin System™ Algorithm
Shainin System Figure 1

Dominant Causes of Variation and Progressive Search in Shainin Red X®

A fundamental tenet of the Shainin System™ 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 the Shainin System™, the dominant cause is called the Red X1. The emphasis on a dominant cause is justified since, per Shainin, "The impact of the Shainin Red X® is magnified because the combined effect of multiple inputs is calculated as the square root of the sum of squares."

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:

Shainin System Figure 2

A direct consequence of (1) is being unable to reduce the output standard deviation substantially by identifying and removing or reducing the contribution of a single cause, unless that cause has a large effect.

For example, if (stdev due to cause 1) is 30% of the stdev (output), users can reduce the stdev (output) by only about 5% with complete elimination of the contribution of this cause. The assumption that there is a dominant cause (possibly because of an interaction between two or more varying process inputs) is unique to the Shainin System™ and has several consequences in its application.

Within the Shainin System™, there is recognition that there may be a second or third large cause, called the Pink X™ and Pale Pink X™ respectively, that make a substantial contribution 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, in light of (1), several large causes would have to 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 application, 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.

The Shainin System™ 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, the Shainin System™ 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.

At any point in the search, the idea is to divide the inputs remaining as possible dominant causes into mutually exclusive families, and then to carry out an investigation that will eliminate all but one family as the home of the dominant cause.

Shainin System™ Resources

You can also search articles, case studies, and publications for Shainin System™ resources.

Statistical Engineering: Six Decades Of Improved Process And Systems Performance (Quality Engineering) This article describes the existing Shainin-defined statistical engineering discipline and evaluates its effectiveness based on the standards proposed by Snee and Hoerl. The methods are illustrated with an investigation of a performance problem in a complex electromechanical system.

Diagnostic Quality Problem Solving: A Conceptual Framework And Six Strategies (Quality Management Journal) This paper contributes a conceptual framework for the generic process of diagnosis in quality problem solving by identifying its activities and how they are related. It then presents six strategies that structure the diagnostic process by suggesting a certain sequence of actions and techniques.

Lighting The Way (Six Sigma Forum Magazine) Six Sigma is a tool that can aid in process improvement using statistical methods and design of experiments (DoE). One such DoE method is the Shainin method, which breaks down process variation to three or four causing factors. In a case study of the manufacturing of compact fluorescent lamps, both Six Sigma and the Shainin method are used to determine process capabilities and reduce process variation.

Adapted from "An Overview of the Shainin System™ for Quality Improvement," Quality Engineering.

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