# AG

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

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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)