iSixSigma

AG

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  • #59410

    AG
    Participant

    Hi everyone,
    Is no one using FMEA is financial services industry or investment banks. Please do let me know if you have customized the rating scales for Severity, Occurrence, and Detection. Thanks in advance.
    -AG

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    #168793

    AG
    Participant

    Need more clarity to your question.I guess I will be able to help you out.

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    #168792

    AG
    Participant

    Hi Rasu
    Your BB is correct I will give you some examples of data types.
    Data types are two but what you have written below is permutations and combinations possible for these data types.Fpr X(Output) and Y(Input)
    1 Continous-Which can be further divided into different units(AHT,length)
    2 Discrete-Who;e numbers (total no of people,errors)
    Eg of the below
    Continuous Continuous-X-AHT (Average Handle TimeY-ATT-Average Talk Time
    Continuos Discrete-X-AHT Y-Agents or days of the week
    Discrete Continuous- X-No. of bottles produced Y-Length of each bottle
    Discrete Discrete-X-Errors Y-Agents
     
     

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    #131656

    AG
    Participant

    Hi !!
    The explainations given by all of you are great. Its
    just a matter of how one understands it. Let me try
    to make it little simple (for a practitioner):
    0- Null hypothesis is status quo, that is, true till proven wrong
    1- Whatever you’re trying to prove is HA (Opposite of Ho), i.e., prove if there is a difference between means, medians, variance etc.
    2- You are not making conclusions about the samples but about the populations. In simple words you are trying to look at the kids (samples) and find out if they belong to same parent (population) or not. Therefore we always compare samples not the statistic (mean, median, std dev).
    3- (1-P) is the confidence in accepting HA (in statistical world you never accept anything in hypothesis testing, you always reject or fail to reject the null hypothesis but for practioners above statement is simpler.)
    4- in most commercial processes 95% or more confidence is good enough to accept the HA
    5- So when you are testing means of two samples then alternate hypothesis is mean(A) is not equal to mean(B). If you get a p-value of 0.02 then you are (1-0.02 = 0.98) 98% confident that mean(A) is not equal to mean(B) so go for it.Let me know you need more info.Regards,
    AG

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    #64147

    AG
    Participant

    Hi Hariri,Non-normal data tells u more stories about the process than normal data. Right skewed histogram also tells you the story. Typically the bars that you see towards extreme right could represent instances of special causes of variation. You can therefore, analyse those instances in greater detail and find out reasons of their occurrence. If you can identify the reasons and eliminate them from the process then you would be able to reduce the mean and variance both.Hope this helps.regards,
    AG

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    #64104

    AG
    Participant

    DPMO computations in software based on functional or even non-functional requirements as an opportunity could be misleading. Because for the decently sized project the number of opportunities will be a lot and hence the DPMO numbers might reflect a rosy picture, which may not be a true picture perceived by the customers.
    Also, considering Line of Code as an opportunity will also lead to a similar problem of inflating the number of opportunities. At the same time this is not at all a customer centric metric because customer is not really bothered about the size as long as functional & non-functional requirements are met.
    I feel it is more relevant to:
    1- Convert the functional & non-functional requirements to the User Acceptance Tests (UATs)
    2- Short list the critical UATs from the overall list, that is, critical to customer
    3- Treat this Short list of UATs as the number of opportunities and measure DPMO numbers on these opportunities.This will give you a better picture of DPMO of the product quality as perceived by the customer.Thanks!

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    #77626

    AG
    Participant

    Laverne,
    Below are some companies from different locations around the world:
    General Electric

    Sun Microsystems

    Citigroup

    Jaguar

    Amazon

    Motorola

    Allied Signal

    Toshiba

    Sony

    Honda

    Maytag

    Raytheon

    Texas Instruments

    Bombardier

    Canon

    Hitachi

    Lockheed Martin

    Polaroid

    Noranda

    Kodak

    IBM

    Ford

    Johnson Controls

    Lear Corporation

    American Express

    ABB

    BBA Group

    Burlington Industries

    Dow

    DuPont

    IMI Norgren

    McKessen

    HBOC

    Nokia

    Siemens

    Honeywell

    Glaxo

    PerkinElmer

    Cott Corporation

    Maple Leaf Foods

    Smarter Solutions

    Qualitran Professional

    Australian Food Corporation

    Alcoa

    Bendix

    Nylex Polymer Products

    Solectron Telecommunications

    Vision Systems Fire & Security

    Conseco

    Starwood

    American Standard
    Regards… AG

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    #75542

    AG
    Participant

    Mehul,
    May be I can help you. Please send me an e-mail ([email protected]) and I will contact you. Regards… AG

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Viewing 8 posts - 1 through 8 (of 8 total)