I am having the same issue with low volume high mix product. Can you give
more detail on what you mean about triggering off the new average?
Does this method really help when you have such uneven amounts of boards?
What I am trying do to is avoid having a large run of 280 boards versus 20
boards dictate the structure of the pareto chart. If the floor builds 280
boards and all 280 end up with damaged insulation on wires, that would say
that is are biggest problem. However, truly it is solder bridging.
One thing I am doing right now is taking the defects per overall work
orders. So if I have 114 work orders for the month, I show the highest
defect for those work orders.
I am going to try your idea and see what it does..
Thanks,
Shawn
-----Original Message-----
From: TechNet [mailto:[log in to unmask]]On Behalf Of Kathy Kuhlow
Sent: Thursday, January 02, 2003 8:24 AM
To: [log in to unmask]
Subject: Re: [TN] Normalizing Yield Percentages ?
In a past life I tried to normalize this by adding the standard deviation of
the rolling averages. And then triggered off of the new average. I also do
not use rework in yield calculations at all. The yield info is first pass
data.
Kat
>>> [log in to unmask] 12/27/02 12:09PM >>>
Does anyone have a standard way to normalize yield percentage charts for
wildly varying production volumes ?
My yield percentage charts show 50% yield when there is 1 out of 2
failures or 50 out of 100 failures.
The 50% Yield with 1 out of 2 usually happens during a Christmas week
when there is no production, only rework.
I have tried to normalize the charts by using a constant denominator
based on the nominal volume.
I use 13 week rolling averages but they are messed up by the low volume
yield results.
This low volume percentage reporting can cause incorrect corrective
actions.
Does anyone know of a standard way to normalize the data ?
Cronbach's Alpha,
Split-Half Reliability (Spearman-Brown Coefficient),
Attenuation correction,
Pair Wise Deletion of Missing Data,
Partial correlation,
Parzen Window,
Pearson Curves,
Johnson Curves ?
Paul Signorelli
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