# Sixth principle of SPC – causes for Variation

According to the sixth principle of SPC  a frequency distribution will be deviating from normal distribution, only in the presence of any assignable cause.
A frequency distribution is a tally of measurements that shows the number of times the measurement is included int he tally. From this frequency distribution we can see if there are only chance causes present in the process of any assignable causes are acting.
If there is a distortion from the normal curve, we can say that there is presence of assignable causes. This finding can actually help us to find the causes and address them.
Various effects of the presence of assignable causes, will tend to distort the shape in center, or the spread as sees earlier. This indication forms the basis of various techniques used in Statistical Process Control.

Originally posted 2012-05-02 01:31:00.

# 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.

Originally posted 2012-05-02 01:24:00.

# Fourth Principle of SPC – the shape is like a bell

Fourth Principle of SPC is logical extension of the third principle which is covered in my last post.  In which it was said that most measurements will be clustered around the middle. In Fact it was proved by statisticians that we can make failry accurate predictions of the percentage measurements in the various sections of the frequency distribution curve.

 Frequency curve with normal distribution
You can see this graph Most measurements fall clso to the middle. This is applicable in general. You will find about 68.2% (34.1%+34.1%)of the measurements will be in the two middle sections of this graph.
28%(14%+14%) of the measurements will fall within the next two sections after the middle sections.
About 4.2%(2.1%+2.1%) will fall in the two outside sections.
A very minute percentage of the measurements will fall outside these sections.  This seems to be a bit odd, bu this is a proven fact. However, absense of external conditions is mandatory.
This Curve shown above will be called as a normal distribution. In fact many statsistical theories are centered around the theme of Normal distibution.
The above example will lead to our fifth principle of Statistical Process Control (SPC)- It is possible ot determine the shape of the distribution curve for parts/output produced by any process.

Originally posted 2012-03-03 02:14:00.

# Third Principle of SPC – Things Vary in a pattern

The Third Principle of SPC is extension of the second one. In the last post on Second Principle of SPC,  I mentioned that we notice a Feature if the measurements of the output are analysed.

If we want to see the pattern, all we need to do is plot the individual data points or the measures taken onto a tally form. we will see definite pattern begin to form after several measurements are plotted.
An easy way to demonstrate this is to roll a pair of dice about 50 times or more and record them on a tally sheet. the pattern we see is a frequency distribution.
We can make a frequency distribution curve by enclosing the tally marks in a curved line. The curve you see will have more measurements at the middle and fewer as we go away from middle. It can be seen that the curve looks like a bell.
Whenver one takes a group of measurements, a frequency distribution curve appears.

This would be explaind by the fourth basic principle of Statistical Process Control (SPC)

Originally posted 2012-03-03 01:01:00.

# Second Principle of SPC – Variation can be measured

We have already discussed about the same thing done by us giving different output in the first principle of SPC. The second principle is based on the first principle and states that the variation in the process can be measured.
Some Variation is always inherent to our job and this is acceptable to some extent so far as the variation is within the Tolerance. However, the Variation tends to increase over a period of time. We need to measure and monitor our job to see that the variation is well within the normal expectations. If we donot make an effort to do so, we land up in trouble and the consequences add to the costs.
Even though it is always desirable to Measure the output of a process, it becomes necessary to measure the output of the process or operation to know when the trouble is brewing.
The measurements can be on the characteristics of the output. It can be the Continuous Variables dimensions, or attribute Variables like colour, shape, finish etc.
After collecting the information as described above, we must analyse to see if things are OK. When we check the output of the feature,  we will quickly notice a Feature. This feature noticed is the basis of third principle of Statistical Process control  – Things Vary according to a definite pattern

Originally posted 2012-02-28 01:36:00.

# 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.

Originally posted 2012-02-28 01:12:00.

# 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.