Q 471. Why is Shainin DOE considered a simpler alternative to Classical DOE? What are the risks associated with use of Shainin DOE?   

 

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Customer Satisfaction is the one the CTQs for the organization at the all levels. Organization are looking at different improvement methodologies to get delighted their customers and meet their expectations and Six Sigma process proves the capability to control the process within customer specification that determine the accessibility of the process, service, product being manufactured or offered.

Industries’ focus nowadays shifted to quality improvement activities especially in the service and manufacturing to reduce process deviation to meet customer demands.

When I say variability that is meant to a) Process variation b) Measurement variation.

To overcome such variability, we need to dive deep into statistics and Six Sigma served the purpose as it is statistical and scientific approach. Design of Experiment (DOE) is the one which deal with input factors responsible for the variations in the Output.

1. Objective of Design of Experiment (DOE)

An Experiment in which purposeful changes are made to the certain parameters of a system so that one may observe and quantify the changes in the outputs in the most efficient way to meet our purpose.

1.1 Six Sigma majorly classified into 03 category by Pyzdek as follows:

 

1.2   There are 03 approaches for the Experimental Design:

Ø  Why is Shainin DOE considered a simpler alternative to Classical DOE?

Shainin DOE approach is the simple and easy to understand among all approaches that why considered over Classical DOE. It is best to find vital few causes of Problem which have significant impacts and universally applicable across all type of industries.

Shainin approach basically is considered to demonstrate continuous improvement by reducing issues to identify root causes and best part of this technique without using statistical software. In fact, Shainin DOE does not even require any knowledge of difficult statistical tools.

Chronic Quality issues has been classified by Shainin Doe into 03 Xs which contribute over 80% of the variation altogether are as follows:

1.    The Red X

2.    The Pink X

3.    The Pale Pink X

Shainin believes in 02 strategy:

1.       Vital Few

2.       Talk to the Parts followed by Workers

 

 

 

 

Comparison Table between Classical vs Shainin

Characteristic	Classical	Shainin
Tools and Technique	Factorial DOE	Full Factorial, Multi Vari, Paired Comparison etc.
Statistical Knowledge	High	Almost Low
Complexity	High	Low
Ease of Implementation	Moderate	High
Cost	Moderate	Low
Time	Moderate	Low
Effectiveness	Moderate	Highly

 

Risk associated with the use of Shainin DOE
1	Limited use in case of complex problem
2	Not best in term of Low to Moderate Volume Production
3	Less Effective for the controllable Factors
4	No use of statistical Tools
5	Less effective when high degree of optimization required
6	Unsubstantiated and Exaggerated

 

Conclusion:

Shainin DOE gives edge over the 02 DOE approaches within the world of Six Sigma DOE.

Shainin Design of Experiments

Shainin Design of experiments methodology was developed by the Dorian Shainin.

 

Shainin DOE techniques are primarily known to produce continuous improvement by very effective solving the chronic quality problems and process optimization in manufacturer industries. Shainin techniques are highly effective in pinpointing root cause and validating it. Statistical software like Minitab etc.. is not required to analyze the data. In fact, Shainin DoE tool does not even require any knowledge in difficult statistical tool. It involves very simple operations like counts, additions, subtractions, etc. The Shainin DOE techniques success of the Six Sigma project using Shainin method could lead to a very positive impact on the morale of the engineer and employees in terms of convincing them to Six Sigma can be implemented very easily without any knowledge in complex statistics.

 

1. Shainin DOE tools are very simple and to understand by both the engineers and shop floor workers.

2. Semi-skilled workers to understand and handle Shainin DoE tools very easily in manufacturing firms.

3. Shainin DOE ensures continuous improvement in manufacturing industries.

 

Comparison of Classical, Taguchi, and Shainin DOE Techniques

 

There is a risk in that, if multiple failure modes contribute/involve to a problem, and hence outcome / result in different dominant causes for each mode.

It is also very important to select appropriate DoE tool for dealing with different situations as there is a need to identify the suitable DoE tool that could be used to increase the predictability and consistency in Process and Product Quality improvement. It is also useful and advisable to evaluate the Shainin DoE techniques for maintaining Six sigma level in Product and Process Quality in the selected manufacturing companies.

 

 

Design of experiments (DOE) is a powerful data collection and analysis tool that can be used in many types of experimental situations with planning, conducting, analyzing, and interpreting controlled tests to assess the factors that control the value of parameters.

 

The Shainin DOE indicates to a gathering of principles which make up the framework of a constantly developing approach to quality. Shainin DOE tools are used significantly to solve the problem of process optimization. The Shainin DOE delivers the simplest, easiest and most effective ways to get the solution. It is simple to be assumed by both the engineers and shop floor workers since its logical based on basic science and engineering knowledge.

 

The methodology is divided into 4 steps:

 

1)      Detection of factors and decision limits

2)      Departure of significant and insignificant factors

3)      Validation of important factors

4)      Factorial exploration & optimal setting

 

The Shainin DOE used to take the less number of runs to perform the test comparing to Classical DoE which is more complex in term of using high statistical analysis and need a lot of experiments to confident the conclusion. The ease of implementation of Shainin DOE is another added value than classical DOE approach.  Shainin technique depends on nonparametric statistics (which make no expectations about the shape of a distribution).

 

The disadvantage of Shainin DOE is the skill and knowledge required to carry out two tasks: (1) to identify the variables correctly and (2) to allocate those variables to the experiment.

 

              DOE was first proposed by R.Fisher in 1920s. As all factors should be taken into account, Full Factorial Designs (FFD) will undoubtedly give the most accurate results. However, this method is not practically preferred, as it includes too much experiments, which is time consuming and costly. 

             For example, when 4 factors with 2-levels, we have to manage 16 experiments (2^n  = 24). But in practice, the number factors may not be as small as 4. For example; 15 factors with 2-levels need 2^15 = 32768 experiment to do. Therefore, to reduce the number of experiments, fractional factorial design has been developed.

 

1. Taguchi Method (TM)

                  Genichi Taguchi simplified classical DOE by using orthogonal arrays (OA). Taguchi created new methods on the improvement of product and process, which includes, "Taguchi Loss Function" and product/process design with three approaches - "System, Parameter and Tolerance Design". He simplified the Fisher's DOE by using Orthogonal Arrays (OA).

                  He used the signal/noise (SN) ratio to reduce variation  in the experimental design.  TM also used the SN ratio, which is used to predict the loss of quality, to maximize the robust design’s objective function. SN ratio takes the test results’ mean and variance.

 

2. Shainin Method (SM)

                  The modern approach to the DOE is Shainin Methodology. This strategy is based on detection of the one, two or three dominant causes of the process variations by focusing on a problem response.

                  Dorian Shainin developed this method to reduce the process output variability, It is simple, relatively easy to understand and implement, but uses the combination of powerful statistical techniques, to make it more reliable and faster to achieve results.

                  In this method, the problem of the poor quality and causes of this problem are identified by the colors of Green, Red and Pink. These parameters, named Red X, Pink X and Pale Pink X, are ranked based on Pareto Principle.

 

Green Y: Indicates special quality characteristics that are important to customers

Red X: Indicates the dominant cause of the variation and it contains at least 50% of the causes of variation (Green Y) 

Pink X: Indicates the secondary cause to the overall variation. It includes 20-30% of the Green Y.

Pale Pink X: Indicates the tertiary important reason. It causes to 10-15 % of the Green Y .

 

With SM, the analysis variation can be reduced by 75% to 95% for the causes of the Green Y (Red X, Pink X and Pale Pink X). SM has mainly 12 techniques, of which, 9 are  problem solving and 3 are controlling and preventing any repetition of the solved problems.

 

 

Comparison between Taguchi DOE Vs Shainin DOE methods:

 

Also, the Pro's and Con's of the 2 methods are listed below and it helps to choose the best appropriate method, based on the requirement

 

Some of the risks associated with Shainin DOE method is listed below:

 

1. This method focuses only on the analysis of mean response and does not take into account the variability of 2 different responses.

2. It can help only upto 70%-80% reduction of the problem, as it focuses on Vital few. The impact of the remaining causes are to be accounted with further more iterations.

3. Grouping of the causes and progressive elimination method, may result in eliminating some significant causes.

 

P Balakumaaran has highlighted the ease of using Shainin DOE along with the associated risks. Hence, his answer has been selected as the best answer.