This document provides an overview of statistical process control and control charts. It defines control charts as tools used to distinguish between common and special cause variation in a process. The document traces the history of control charts to their invention by Walter Shewhart in the 1920s. It describes different types of control charts for continuous and discrete data. It also distinguishes between control limits, which indicate a process's natural variation, and specification limits, which define customer requirements. Finally, it explains the concepts of common and special cause variation and how identifying them is important for process improvement.

1. ©UFS
Statistical Process Control
Control Charts
Gaurav Singh
Business Process Professional -Quality
24th June 2011

2. Agenda
 What is a Control Chart– p. 3
 History of Control Chart– p. 4
 Types of Data– p. 5
 Defect and Defective– p. 6
 Types of Control Charts– p. 7
Control  Control Limits Vs Specification Limits – p. 8
Charts
 Understanding Variation – p. 9
 Types of Variation
Common Cause Variation– p. 10
Special Cause Variation– p. 11
 Control Chart Decision Tree-p.12
• Question and Answers– p. 13
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3. What is Control Chart
Definition
A statistical tool used to distinguish between process variation resulting from common
causes and variation resulting from special causes.
Why to use a Control Chart
One goal of using a Control Chart is to achieve and maintain process stability.
Process stability is defined as a state in which a process has displayed a certain
degree of consistency in the past and is expected to continue to do so in the future.
Monitor process variation over time.
Differentiate between special cause and common cause variation.
Assess the effectiveness of changes to improve a process.
Communicate how a process performed during a specific period .
This consistency is characterized by a stream of data falling within control limits based
on plus or minus 3 standard deviations (3 sigma) of the centerline
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4. History of Control Charts
Control charts, also known as Shewhart charts or process-behavior charts, in statistical process control are tools used to
determine whether or not a manufacturing or business process is in a state of statistical control.
The control chart was invented by Walter A.
Shewhart while working for Bell Labs in the 1920s.
The company's engineers had been seeking to
improve the reliability of their telephony transmission
systems.
Walter Andrew Shewhart (pronounced like "shoe-heart", March 18, 1891 - March 11, 1967)
was an American physicist, engineer and statistician, sometimes known as the father of statistical quality control.
Shewhart framed the problem in terms of Common- and special-causes of variation and, on May 16, 1924,
wrote an internal memo introducing the control chart as a tool for distinguishing between the two. Dr.
Shewhart’s boss, George Edwards, recalled: "Dr. Shewhart prepared a little memorandum only about a page
in length. About a third of that page was given over to a simple diagram which we would all recognize today
as a schematic control chart. That diagram, and the short text which preceded and followed it, set forth all of
the essential principles and considerations which are involved in what we know today as process quality
control."[3] Shewhart stressed that bringing a production process into a state of statistical control, where there
is only common-cause variation, and keeping it in control, is necessary to predict future output and to manage
a process economically.
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5. Types of Data
Discrete Data Continuous Data
Discrete data is data that can be counted. (You can’t have a half Continuous data can be assigned an infinite number of values
a person). between whole numbers.
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6. Defects and Defectives
Defects are the subset of defectives. There may be n no. of defects to have one defective product. Say for ex.
Consider a cylindrical rod as a final product. In that the possible defects are crack, soft, bend, dimensional
tolerance, eccentricity etc. For getting one defective rod, is it sufficient to have one or more defects as mentioned
earlier.
At least one or more defects tends to have a defective product.
Ex. 2: Consider a human being. If he is not feeling well, it is something similar to a defective. Why this illness
happened? This is because of fever or stomach pain or diarrhea (defects)etc. All these illness are defects. At least
if one appears, then that person will not be feeling good. If all the diseases has come, again the same person will
be suffering.
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7. Types of Control Charts
Continuous Data Discrete Data
I and MR Chart (individual and moving range) N Chart
X-bar and R Chart NP Chart
X-bar and S Chart C chart
U chart
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8. Control Limits Vs Specification Limits
Control Limits - Control limits, also known as Specification Limits -Specification limits, Boundaries or
natural process limits, are horizontal lines drawn on parameters that define acceptable performance for a process
a statistical process control chart, usually at a expressed as a target limit as well as an upper and lower limit.
distance of ±3 standard deviations of the plotted
statistic from the statistic's mean.
The table below contrasts control limits and specification limits:
Control Limits Specification Limits
Voice of the process Voice of the customer
Calculated from Data Defined by the customer
Appear on control charts Appear on histograms
Apply to subgroups Apply to items
Guide for process actions Separate good items from bad
What the process is doing What we want the process to
do
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9. Understanding Variation
Variation is everywhere. No two things are alike. Variation exists in all processes, in all products, and in all
companies. Even things that appear consistent have variation when you take a closer look. Service processes in
particular, when compared to manufacturing processes, can have significant variation. Processes are complicated,
customers change their minds, and exceptions are the norm. Process variation results in errors, defects, rework
and longer cycle times; all costly to the business. More importantly, customers experience this variation. Customers
do not experience “the average.” They experience what they experience; which is the variation around the average.
Yet, businesses continually report average results on end-of-month reports and scorecards. Decisions are made
without any knowledge about the inherent variation of the process.
Variation is the enemy. It’s
the enemy to quality. It’s the
enemy to improvement. It’s
the enemy to management.
There is perhaps no more
misunderstood concept in
business today than the
concept of variation
A main focus of Six Sigma is reducing variation in process performance and output. This requires
distinguishing between common cause and special cause variation, as different techniques are required for
dealing with each type of variation. Learn to identify and manage both types effectively.
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10. Common Cause Variation
what is “common cause” variation?
When all variation in a system is due to common causes, the result is a stable system said to be in statistical control.
The practical value of having a stable system is that the process output is predictable within a range
A common cause of variation is a variation from the mean that is
caused by the system as a whole. This variation is not due to an
assignable cause, but rather represents variation inherent in the
process you are studying.
When a work process has only common causes of variation and
no special causes, that process is "in control." This means that it
is stable, consistent, and predictable. It might be predictably
good or predictably bad, or it might be a very regular mix of good
and bad results.
Common causes are problems inherent in the system itself. They are always present and effect the output of the
process.
Examples of common causes of variation are poor training, inappropriate production methods, and poor workstation
design.
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11. Special Cause Variation
what is “Special cause” variation?
A special cause is present in the process if any points fall above the upper control limit or below the lower control limit.
Special Cause variation is created by a non-random
event leading to an unexpected change in the process
output. The effects are intermittent and unpredictable.
If Special Causes of variation are present, the process
output is not stable over time and is not predictable. All
processes must be brought into statistical control by first
detecting and removing the Special Cause variation.
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12. Control Chart Decision Tree
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13. Question and Answer
Now
Questions
Page 13

14. Thank you
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