Corrective action

Here is the issue – All 25 batches affect the bottom line of the enterprise. I have faced this in the great world of software development. In that situation, it was only the “top ten” (out of about 30) that were discussed and fixed. However, instead of talking about “batches”, I talked about money. In my situation, the remaining N defects generatged over $900K in customer downtime, manual fixes, call center reports, developer time, etc. to fix. When shown to executive leadership, the remaining defects received a lot more attention. But….in the world of limited resources, not all defects may receive the necessary attention. In my situation, the “work around” became the official resolution for many of the defects.
Reduce your defects to dollars, and you will have a better chance at resolving those batches beyond the top three. I am ASSuming that none of these batches affect “compliance” of “safety” issues.
Dr. Ted
SSMBB
 
 
 
 

In case of repeated defects, have you tried looking for patterns of defectives coming out of the same machine/produced by the same operator/in the same shift/same source of raw material etc.?
If you can brainstorm with different guys and make separate  Cause Effect diagrams for each of the most frequent defects, you can then zoom in on the most likley cause for each defect,  which can then be actioned upon. 
Talk to the low man on the totem pole. He knows more than you assume about what causes the defect. Dont forget to mention his name to top mgmt once you have solved the problem!

Andrews,
There is a pretty basic tool called a Paytner Chart that we used to use to track CA. It ties actions to results. It was part of the Ford TOPS training. There was a post about a year ago where some people found an internet reference for me but I don’t have the link any longer.
Track what you are doing and if it is working leave it alone. If it isn’t working do something else.
That was pretty pretty profound – what can I say it is still before midnight.
Good luck.

Statman,
The difference is that the two ways of handling SPC both work.
The wandering around building, campfires, and singing Kumbaia naked around a campfire has a track record of no results. It is opinion based and has a poor track record.
 

Statman,
Sticking with the data will get you there.
Two different methods of handling SPC gets you there using data and both work. Both stay with the data.
Believing that you can have a discussion with an operator and you have an instant solution is naive particularly when you are pushing a process beyond 3 sigma.

Statman,
I am not sure what the analysis was on the Titans but what passes for anlysis on most of these sports shows is flawed at best.

Hey, there is nothing wrong with dancing naked singing Kum By Ya – just not much of a way to operate or improve a process.

Stan,
Dancing naked is certainly in the finest of Texas traditions as Mathew McConaughey will attest to. Like you said – it is marginal as an improvement methodology.
It does seem to sell well to people from North of the Mason Dixon as a way to re-establish their manhood. Selling snake oil. Does that sound like someone we all know?
Regards,
Mike

Andrews,
Collect the historical data and use pareto chart  to know which parts need to be prioritized. If you will not do this you will be “boiling the ocean” – your problem will not be solved.
Solve first the part with the highest frequency of contribution to the defective 25 batch product ( I would assume that 1 product has 100 part and with 10 parameter each ).
Focus to that part and do MSA for each parameter of that particular part to make sure that the rejection is not due to measurement system. Then, collect data and compare the following:
a) operator to operator
b) work station to work station
c) shift to shift
d) day to day
e) machine to machine ( if any )
f) etc… that may cause the variation.
Use 2 sample t or anova for continuous data collected and 2 proportion or chi square for attribute data.
When you see the significant source(s) of variance, focus on that source, this is the time you would have the C & E brainstorming but should also validate if those possible root cause which are subjectively voted to be the root causes are significat by comparing again. collect data, data and data.
By doing these steps, you are cutting you problem by 50% all the time until you reach the most possible root cause. then do DOE if applicable and make sure you put a control on your improvement to ensure the problem on that particular part number will not reoccur. use poka-yoke, automation if possible.
then, proceed to the next part or make many six sigma teams to take care of the other parts.
hope this would help,
 
AbetF