Six Sigma vs. Shainin - Content of problems that can be solved by Shainin

[Caydinli]

I'll be trained for Shainin Apprentice and then Journeyman?

I'd like to understand the content of problems that can be solved by Shainin.

And also understand the practical advantages of Six Sigma and Shainin to each other.

Do they fit to all problems faced in manufacturing area

Thanks

[Claes Gefvenberg]

Welcome to the Cove, Caydinli

First of all I'll have to confess that I am unable to answer your question, but I'm convinced you'll get answers from others. I would like to ask you something though: Could you post a progress report now and then as you learn more? I would be very interested in your comments.

/Claes

[Wes Bucey]

Originally Posted by Caydinli

I'll be trained for Shainin Apprentice and then Journeyman?

I'd like to understand the content of problems that can be solved by Shainin.

And also understand the practical advantages of Six Sigma and Shainin to each other.

Do they fit to all problems faced in manufacturing area

Thanks

Welcome to the Cove!

I'm aware that Shainin is a style or method of problem solving through use of
"Design of Experiments," but I wasn't aware there were names or levels similar to Six Sigma's Colored belt system. Apparently the specific training group you are using issues titles like trade unions do (apprentice, journeyman, master, etc.)

Tell us who you are getting your Shainin training from.

I once copied this from somewhere? about Shainin. Perhaps it will trigger memories and useful advice from some other Covers.

The Shainin method consists of the following steps:
1. Determine if the method can solve the specific issue
2. 'To translate' the issue (the characteristic) to a measurable quantity
3. To reduce the search area ('Intelligent Searching')
4. To determine the cause(s)
5. To quantify the effect of the causes
6. To verify the suggested improvement
7. To determine process parameter borders
8. To control the process

The following tools have been developed to apply this method:
1. Components Search
2. Multi vari chart
3. Paired Comparison
4. Variables Search
5. Full Factorials
6. Better vs current
7. Scatter Plots
8. Process Certification
9. Operator Certification

Frankly, some of these concepts and techniques are "old hat," but some I have no clue about - ("Better vs current" just never came into my awareness as a technique)
It would be helpful if you could tell us if these techniques are familiar to you from your studies thus far.

[Tim Folkerts]

Wes,

Just out of curiousity, which do you consider "old hat" and which do you have no clue about?

Let me present my take on Shainin. The Shainin techniques and training are proprietary, so it is hard to find much on the web (unlike six sigma, where any consultant can certify new black belts). If you can find an old copy of "World Class Qualty" by Keki R. Bhote, it describes many of these techniques. And yes, many of them are "old hat".

The goal, as I understand it (I haven't been through the training), is to simplify calculations and present some standardized approaches to problem-solving so that you don't need an advanced degree in statistics or engineering to use the techniques.

Statistical calculations are replaced by simple comparisons.
* In B vs C, suppose you have some samples from your Current process and some samples from the (hopefully) Better process. Any statistician could calculate and interpret a t-test, but suppose you don't have a statistican (or suppose the results are simply a rank order, rather than actual values). B vs C testing provides some simple rules where all you have to do is rank the samples. I don't have the rules handy, but they go something like "if you test 3 C and 3 B, and 3 of the top 4 are C, then C is better." There are many variations depending on how many of each type you have to test. Just rank and check the chart with the rules.
* In precontrol, you simply divide the spec limits into three ranges - the middle half is "green", the upper quarter and lower quarter are "yellow" and anything outside the spec limits is "red". Forget all the "two out of three at least 2 sigma from the center" type rules for traditional control charts. All you do is draw 2 samples. One "red" or 2 "yellows" means you have a problem.

The statistical power will be reduced, but the point is that a floor worker can do the measurements, plot it on a chart, and immediately know what to do without any math.

Similarly, the DOE-type techniques (full factorial, component search, etc) tend to be straightforward to implement and to interpret. They may not be the best techniques if you are an expert, but they are usually easy to use.


Tim F

[Wes Bucey]

OK - Here's what I understand about items in the list:
Also bear in mind that I no longer have the patience or the math skills to do Analysis of Variation (ANOVA) on my own - I need software to do this for me.
Shainins Seven Diagnostic Tools

Multi Vary Charts
These are fundamentally a stratified experiment directed at identifying Red x and Pink x. (Causes of variation). They look like but should not be confused with control charts. OK - this is always a good starting point for a DOE - find the causes of variation
Components Search
This is a tool used where a product can be disassembled and re assembled and is used to find poor quality or failing components Doh! If you can do this, you can probably spot the choke points and stumbling blocks as you look at the components.
Paired Comparisons
This tool uses the above tools to further home in on the family of Red x and Pink x using binary queues. It attempts to use high and low factors to reduce the number of experiments. OK. So?
Better vs current
After a series of tests have been carried out to determine causes of variation B v's C issued to compare 3 current methods of production with 3 better methods with 5 possible outcomes ranging from the possibility that current methods give results at least as good as the better ones through to the option where even the worst results from the 'better' options are better that the best current methods. This was news to me. I'm still not clear where multiple "current methods" came from or how they derive the "better methods" in the first place - it may be simple if you take the course, but mystifies me now. In many industries, it is EXPENSIVE to actually run an alternate process side by side with a current process to make the comparison.
Scatter Plots This is a graphical representation of results from experiments. It aims to look at the tolerance limits of data in order to determine the true cause of variation. Yep! Old hat, but very worthwhile.
Full Factorials
Shainin recommends the use of the full factorials, as they are not likely to miss important data, which he believes is possible with Orthogonal Arrays. Most of the things I've dealt with had too many variables and possible ways to go to take the time and effort to do "Full Factorials." So this is old hat, but not meaningful in my experience because the cost of doing the DOE using this offsets any savings in process.
Staff and process certification leave me a little mystified - exactly how is this different from determining ANY area for improvement? Certainly, you want to make sure of the capability and capacity of personnel and machinery. Is this stuff for a chapter for any but raw greenhorns? Raw greenhorns are not usually in charge of designing experiments for process improvement.

I respect the math and statistical skills of many here in the Cove who are able to do stuff routinely in manipulating numbers when I have to check a cheat sheet for formulas, simply because I don't use them often enough to retain them in memory.

Ultimately, though, a Design of Experiments is supposed to point the way to methods where the process operators do NOT have to use "brute force" by testing every possible modification of a product or process to attain the most cost-effective method of manufacture. A good DOE often involves a team [of experts on materials, machines, function of finished product, market competition, new product designs in the pipeline, etc.] to make the determination when continuing effort to improve a process will not generate sufficient efficiencies to justify the cost of experimenting.

In my opinion, DOE by Shainin or Taguchi, or anyone else needs to keep the "big picture" in focus all the time to avoid "analysis paralysis" where the DOE becomes more important than selling product.

I have no axe to grind against Shainin or any of the other DOE systems. I try to keep in mind DOE is merely one of the tools in my kit. I also have visions of Red Beads and Funnels dancing in my head all the time and always worry about bad processes or "tampering."

[alfawei]

"World Class Qualty" by Keki R. Bhote

A good book on Shainin !

It has been pressed in China .

Originally Posted by Tim Folkerts

Wes,

Just out of curiousity, which do you consider "old hat" and which do you have no clue about?

[alfawei]

I dont think Shainin & Sixsigma ,even PDCA can solve all problem in manufacturing area.

These are just tools of solving problems.

The environment is changing quickly !

The method and it's user must change too.

Originally Posted by Caydinli

I'll be trained for Shainin Apprentice and then Journeyman?

I'd like to understand the content of problems that can be solved by Shainin.

And also understand the practical advantages of Six Sigma and Shainin to each other.

Do they fit to all problems faced in manufacturing area

Thanks

[Wes Bucey]

Originally Posted by alfawei

I dont think Shainin & Sixsigma ,even PDCA can solve all problem in manufacturing area.

These are just tools of solving problems.

The environment is changing quickly !

The method and its user must change too.
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Truer words were never spoken - - it's so seldom we have the courage to admit we do NOT know all the answers. How can we? The questions and the answers are always changing - it's tough to hit a moving target. Good post, alfawei!

"Its" - possessive pronoun - something belongs to "It" - no apostrophe used.
"It's" - contraction for "IT IS" - uses an apostrophe to show an "elision" (missing letters or sounds)