Fifth Principle of SPC – shape of the distribution

The Fifth Principle of SPC  states that it is possible to determine the shape of the distribution form the measurements of any process. We can learn abut what the process is doing, against what we want the process to do. For this we need to measure the output of the process with the design specifications.the process can be altered if we donot like the comparison, especially if we see a variation.
We need to address eh variation so that it falls in the required pattern. The variation is due to mainly of 2 types. Common Cause variation and Special Cause Variation.
If the variation in output is caused only by common causes, the output will vary in a normal and predictable manner. In such cases, the process is said to be “stable” or “in a state of Statistical Control”.  While the individual measurements may differ from each other, they tend to follow a Normal Distribution.
The normal distribution is characterized by the following

  • Location (Typical Value)
  • Spread – Amount by which the smaller values differ from the center.

The shape of the distribution will deviate from the normal curve in case of any un usual occourances.  These changes can be called as Assignable causes.

The presence of assignable causes will result in difference from the usual normal curve, either in Shape, or in spread or a combination of both.
Non Normal
some changes are given below. 
Normal


Non Normal

The above findings will lead us to the sixth principle of SPC – Variation due to assignable causes tend to distort the normal distribution curve.

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First Principle of SPC – No two things are exactly alike.

From the past experience of many generations,  we can clearly understand that things are never exactly alike. All you can find is two similar things. Even the “peas of pod” which look alike, may show some differences among them when we have a closer look. The peas are different in size, shape, colour, softness, or some thing else.
If we apply this to a product, say some products, parts or components, we know that no two manufactured parts are exactly alike each other.  In one way or the other, these parts will be slightly different in Size, Shape, or finish.
If two parts are looking alike, the differences can be found if the resolution of the measurement. The more precise you are in measuring, the differences are more clearly understood.
This is a basic problem, which will get us into trouble for making parts interchangeable, which the main aim of mass production. To work around the problem, we use tolerances.

However, our aim is to keep the variation between the parts to be minimum and as small as possible.
The above discussion is the first principle of SPC. Based on the above discussion, we can go to the second principle of Statistical Process Control (SPC) – Variation in a process or product can be measured.

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Basic Principles of Statistical Process Control

The six principles below are the Basic Principles of Statistical Process Control (SPC). These can be clearly understood using Frequency Distributions.
The principles are listed below. The explanation is linked to each sentence

  1. No two things are exactly alike
  2. Variation in a product or process can be measured
  3. Things Vary according to a definite pattern
  4. Whenever things of the same kind are measured, a large group of the measurements will tend to cluster around the middle.
  5. It is possible to determine the shape of the distribution curve for the measured Output (Parts produced, transaction) by any process
  6. Variation is due to assignable causes tend to distort the Normal distribution Curve.
To Understand more on these principles, we can study the data from the output of the process. These foundation principles will be useful for all types of processes.
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