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Topic Metrics Explanation of Customer Report

Metrics Explanation of Customer Report

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This topic contains 5 replies, has 5 voices, and was last updated by  Chris Seider 2 days, 10 hours ago.

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  • #707126 Reply

    ReneeMac
    Participant
    Reputation - 24
    Rank - Aluminum

    A customer sent me this report asking for an explanation. Can someone explain to me, what are all of these metrics, and what should I do with this.

    Thanks.

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    #707128 Reply

    @reneemacdebenac You are over filling the wine bottles which is typical of the food and beverage business. He is upset about over filling?

    From the business side that means you are giving away your product. There should be no problem dispensing this type of liquid accurately. If you are bottling a pretty good amount you are giving away a lot of money.

    Just my opinion.

    #707131 Reply

    ReneeMac
    Participant
    Reputation - 24
    Rank - Aluminum

    Thanks Mike, the overfilling is not the question. I want to know about the metrics in the report.

    What I’m confused about is the 2nd, 3rd, and 4th Moment. What does that mean? Is it better to have a Kurtosis of 2.15 or Kurtosis of 1.0, which is better? The other metrics are also unclear, any explanation on the metrics themselves would be appreciated. By the way what is SG Cp, etc?

    #707132 Reply

    I’m not entirely sure what they mean but here is a handy wikipedia article:
    https://en.wikipedia.org/wiki/Moment_(mathematics)#Significance_of_the_moments

    It appears as though they are descriptions of the shape.

    Kurtosis describes the “measure of the heaviness of the tail of the distribution, compared to the normal distribution of the same variance” Essentially the number is showing you that it is skewed towards your upper limit (more likely to over-fill than under-fill compared to your mean).

    I imagine they are included as part of the analysis package but probably don’t apply much to real problem solving.

    #707145 Reply

    What it means is that your data distribution isn’t a normal distribution. Given that your process is going to truncate your output at both the low and the high end this isn’t terribly surprising. From the standpoint of what it means to your customer – nothing. From the standpoint of what it means to you – see Mike Carnell’s post.

    #707307 Reply

    As @Mike-Carnell indicated, you’ve got an opportunity to reduce your frequency of extra overfilling but you better be careful because you’re just at the mean so you need to maintain the requirements of filling at the label requirements.

    I’d suggest you do a quick capability study of your filling machine for this product. I bet you’ll find a poor distribution and 1 or 2 are highly overfilling and another is underfilling.

    Thank the customer!

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