Karim Mohsen

@kathilee I don’t know what a “huge” definition is. I would suggest you be far more specific about what you’re looking for and why.

And explain why and how what’s already on the site (and internet in general) isn’t providing what you want.

@kathilee It looks like you didn’t search the site before asking this question. A simple search should answer this question for you.

@fausto.galetto   :)  “Right” is spelled the way I typed….not the way in your cute survey.

Yea, I’m “poking the bear” but I think you broke some china in the shop beforehand.   FYI, I’ll restate….doing a control chart on MTBF would seem to be a silly tool for practical use.  If the valve is truly causing a plurality of downtime–break it do…[Read more]

@ginloen You’ve said you’ve tried to solve this yourself. How? What do you think you’re doing right or wrong? The more details you provide, the more likely you are to get a response. The iSixSigma audience is helpful, but they like to see that someone is putting forth a good-faith effort.

MTBF with a control chart seems like a stretch to me–what kind of control plan would you have?  I’d look at pareto of lost time and drill down to the component(s) and fix the issue(s) and see if the pareto of lost time showed a difference.

Is this twitter? :)

Happy new year to my buds such as @mike-carnell and @rubtler and @darth

@Knowwareman Thanks again for letting us know about the issue. It’s been fixed!

@mekaljohn Did you check the iSixSigma Events calendar? https://www.isixsigma.com/events/

I can only imagine your approach COULD work IF…

1.  The model was validated.  No model is 100% so direction better be correct and an acceptable magnitude of error isn’t large relative to predictive changes in results.  The model implies you apply the factors AS IS and get “close” to actual results.  Don’t ask me online (LOL) what close is but…[Read more]

Thanks @cseider — you, too!

Happy Holidays to all this season

@strayer – I’ll also point out here for you and anyone else who comes across this thread that we have a Contact Us link in the footer of every page, which takes you here: https://www.isixsigma.com/contact/.

Yes, the power of statistics is to use RANDOM sampling to get population mean estimates and see if you can shift those estimates after process changes.  Yes, it could be the same for variation.

@sebvlarsen What do YOU think you should do? Why or why not? Be more specific about what problem you’re having and you’re more likely to get a response.  People want to see that you’re putting in a good-faith effort to figure out the solution on your own.

@Sebvlarsen Did you search our site before asking your question? We’ve got a bunch of content on Gage R&R/MSA in our Tools & Templates section: https://www.isixsigma.com/tools-templates/measurement-systems-analysis-msa-gage-rr/

@monu96 Did you search our site before asking your question? We have a section of content that applies in Industries > Energy: https://www.isixsigma.com/industries/energy/

It shouldn’t be any different.  There are losses to attack.

OEE is a great tool but not easy to “measure” if loss capturing isn’t credible.

I’ve helped many clients improve 10% output on poorly performing lines by finding either 1) easy to tackle significant loss buckets or 2) just providing instant feedback (even if just hourly) of how production is occurring so management and supervision has a chance to…[Read more]

Consider what you mean by target of 100%.

Your control chart would be great if no losses were assumed and the theoretical output was included–not planned output.

Planned shutdown times should ONLY include times where no one was employed to run the line.  Routine maintenance and operator maintenance are still losses.

All calendar time should be included except for unscheduled run time for lack of orders or normal unscheduled time that isn’t manned.

Your scenario would have 24 hours in the…[Read more]