Normal or not Normal

Normal or not Normal

Quality in Manufacturing

I hope that I not boring you folks but I know how important is what I am sharing with you here so please bear with me on this:

We already know how important the Cp/Cpk tool is in order to analyze testing data and help predict the behavior of our manufacturing process.

But, Cp/Cpk requires a normal distribution of our process.

Our sampling results do not always represents a normal distribution. This can happen because of overlap of two or more processes, the existence of too many extreme values in our sampled testing data and because of several other reasons.

It is always recommended to check the exact type of distribution in details with statistical tools which accurately reflects the testing data pulled from our manufacturing process.

 

HOWEVER, we, manufacturing teams are so busy and therefore always look for practical solutions.

Luckily we have the Central limit theorem to help us here. This theory, in a nutshell, claims that that, given a sufficiently large number of iterates of results (at least few hundreds), we can consider the behavior of these results to reflect  approximately a normally distribution, regardless of the underlying distribution.

This let us enjoy the advantage of using Cp/Cpk as an important tool that helps us a lot to improve our manufacturing process.  

Quality in Manufacturing starts here

QualityLine lets you regain control over the quality in manufacturing of your outsourced manufacturing line if it is located in your own facilities or even if it is located on the other side of the world.

The system helps significantly improve quality, increase yield and minimize downtime incidents.

How does it work?

Testing data is automatically and continuously collected from your testing stations located on your manufacturing line, analyze and securely upload it to analytics dashboards exclusively set for you.

You get 24/7 accurate information about each unit tested. You can overview and drill down up to a single unit, conduct quick root cause analysis and improve production quality.

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