Tony,
Thanks for your insights. I also think Wheeler is very useful and may
order Advanced Topics this weekend. I was wondering from your first
reply what were the alternatives traditional process comparisons and
Wheeler may have the answer.
Thanks, Jim
Tony wrote:
> Forget about cpk, ppk, cpm and hypotesis tests as means of comparing
> processes. Read Ch 18, (p 373) "Advanced Topics in SPC" by Wheeler.
> Wheeler is the "bible" on process management. It is essential reading.
>
>
>
> "gn228169" <s3ss3rph> wrote in message
> news:k4SdnQs1xJyR3sfbnZ2dnUVZ_h-vnZ2d@[EMAIL PROTECTED]
>
>>OK,
>>The 0.09 Ppm example was only an example. I am dealing with theory. My
>>question is how to ensure the improvement is statistically significant.
If
>>I run a t-test, I can use the tables and/or p value to determine if the
>>difference in means is significant. i want to do that with ppm values.
>>Better is defined (for this question) as less variation and closer to
the
>>target. Saving money is im****tant. If we can perform a test with less
>>variation and closer to the target, we are providing more accurate data
to
>>our customers. Supplying this data allows the internal customer to
improve
>>their process to reduce waste and save money so essentially it does save
>>money.
>>Deming and others have proposed increasing quality will save money.
Saving
>>money is always better as you mentioned, but saving money is often more
>>involved than just looking at the initial costs and return. "Nitpicking
>>numbers" is what I want to avoid. Is comparing means nitpicking? Not if
>>you compare statistically using a t-test or ANOVA or ANOM. Is comparing
>>Ppm values nitpicking? Not if I can compare statistically. This is the
>>question I am asking. Nothing more.
>>
>>Raymond J. Johnson Jr. wrote:
>>
>>>gn228169 wrote:
>>>
>>>
>>>>Raymond J. Johnson Jr. wrote:
>>>>
>>>>
>>>>>gn228169 wrote:
>>>>>
>>>>>
>>>>>>When analyzing a new method or process, we usually use a t-test to
see
>>>>>>if the new method is equivalent. If it is not, we look at the
variance
>>>>>>and target values to see if it failed because it was "better" Is
there
>>>>>>a statistical test to check for "better" without checking first for
>>>>>>equivalence and then for variance and target?
>>>>>>Thanks, Jim
>>>>>
>>>>>
>>>>>
>>>>>You've got some 'splainin' to do. What's the difference between a
>>>>>"process" and a "method"? You put "better" in scare quotes,
apparently
>>>>>because you understand that it's a relative concept, but you don't
tell
>>>>>us about what makes "better" better. What are your criteria? Are you
>>>>>considering only output values? Are there fiscal considerations
(i.e.,
>>>>>given equivalent output, is one process better because it's less
>>>>>costly)? What, exactly, are you trying to do?
>>>>
>>>>
>>>>A process and a method are the same thing, sorry. Better is less
>>>>variation and closer to target. A one sided t-test will not tell me if
>>>>the new method has less variation and is closer to the target as one
>>>>person suggested.
>>>>I think cpk,ppk,cpm etc. will work but I still have a question. If a
>>>>cpk, cpm score is greater using the new method, I would assume the new
>>>>method is "better". My question is, How much of an increase in Ppm is
>>>>significant? Example: If my Ppm value was a 0.90 and increased to a
0.99
>>>>using the new method, is this due to common noise or is this a truly
>>>>"better"?
>>>
>>>
>>>It's only "better" if it saves money. Nitpicking the numbers *wastes*
>>>money. It's not clear whether you're trying to solve a particular
problem
>>>or just dealing with theory, but if you're talking about a difference
of
>>>.09 PPM, it's hard to believe it's worth worrying about.
>
>
>
|
