Statistical quality control (SQC) uses statistical tools to monitor and improve production processes. Walter Shewhart pioneered control charts in the 1920s to distinguish normal variation from problems. W. Edwards Deming helped spread SQC in the US and Japan. Descriptive statistics describe quality characteristics, while control charts monitor processes over time. Variables charts like X-bar and R charts monitor measurable attributes, while P and C charts monitor discrete attributes like defects. Process capability evaluates a process's ability to meet specifications by comparing variability to tolerance limits. Key metrics include Cp, Cpk, and process centering.
2. What is SQC ?
Statistical quality control (SQC) is the
term used to describe the set of
statistical tools used by quality
professionals.
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3. History
SQC was pioneered by Walter A. Shewhart at
Bell Laboratories in the early 1920s.
Shewhart developed the control chart in 1924 and
the concept of a state of statistical control.
Shewhart consulted with Colonel Leslie E. Simon in
the application of control charts to munitions
manufacture at the Army's Picatinney Arsenal in
1934.
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4. History
W. Edwards Deming invited Shewhart to speak at the
Graduate School of the U.S. Department of Agriculture, and
served as the editor of Shewhart's book Statistical Method
from the Viewpoint of Quality Control (1939) which
was the result of that lecture.
Deming was an important architect of the quality
control short courses that trained American industry in
the new techniques during WWII.
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5. Deming traveled to Japan during the Allied Occupation and met
with the Union of Japanese Scientists and Engineers(JUSE)in an
effort to introduce SQC methods to Japanese industry
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8. Descriptive Statistics
The Mean- measure of central tendency
The Range- difference between largest/smallest
observations in a set of data
Standard Deviation measures the amount of
data dispersion around mean
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9. The Mean
To compute the mean we simply sum all the observations and
divide by the total no. of observations.
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10. The Range
Range, which is the difference between
the largest and smallest observations.
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11. Standard Deviation
Standard deviation is a measure of dispersion of a
curve.
It measures the extent to which these values are
scattered around the central mean.
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12. • Extend the use of descriptive statistics to monitor
the quality of the product and process
• Statistical process control help to determine the
amount of variation
• To make sure the process is in a state of control
Statistical process
control
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13. Variation in Quality
No two items are exactly alike.
Some sort of variations in the two items is bound to be there. In
fact it is an integral part of any manufacturing process.
This difference in characteristics known as variation.
This variation may be due to substandard quality of raw
material, carelessness on the part of operator, fault in
machinery system etc..
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15. Variation due to chance
causes/common causes
Variation occurred due to chance.
This variation is NOT due to defect in machine, Raw
material or any other factors.
Behave in “random manner”.
Negligible but Inevitable
The process is said to be under the state of statistical
control.
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16. Variation due to assignable
causes
Non – random causes like:
Difference in quality of raw material
Difference in machines
Difference in operators
Difference of time
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18. Specification and control limits
No item in the world can be a true copy of another item.
It is not expressed in absolute values but in terms of a range.
For Eg:
The diameter of a pen is expected by its
manufacturer not as 7mm but as 7mm ± 0.05.
Thus, the diameter of a pen produced by the
manufacturer can vary from 6.95 mm to 7.05 mm.
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21. SPC Methods-Control Charts
Control Charts show sample data plotted on a
graph with CL, UCL, and LCL
Control chart for variables are used to
monitor characteristics that can be measured, e.g.
length, weight, diameter, time
Control charts for attributes are used to
monitor characteristics that have discrete values
and can be counted, e.g. % defective, number of
flaws in a shirt, number of broken eggs in a box
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22. Control Charts for Variables
x-bar charts
It is used to monitor the changes in the mean of a
process (central tendencies).
R-bar charts
It is used to monitor the dispersion or variability of the
process
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23. Constructing a X-bar chart
( sigma is not given)
A factory produces 50 cylinders per hour. Samples of 10
cylinders are taken at random from the production at
every hour and the diameters of cylinders are measured.
Draw X-bar and R charts and decide whether the
process is under control or not.
(For n=4 A2= 0.73 D3= 0, D4=2.28)
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32. Constructing a X-bar Chart
(Sigma is given)
A quality control inspector at the Coca-Cola soft drink
company has taken twenty-five samples with four observations
each of the volume of bottles filled. The data and the
computed means are shown in the table. If the standard
deviation of the bottling operation is 0.14 ounces, use this
information to develop control limits of three standard
deviations for the bottling operation.
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37. Control Charts for Attributes
Attributes are discrete events; yes/no, pass/fail
Use P-Charts for quality characteristics that are
discrete and involve yes/no or good/bad decisions
Number of leaking caulking tubes in a box of 48
Number of broken eggs in a carton
Use C-Charts for discrete defects when there can
be more than one defect per unit
Number of flaws or stains in a carpet sample cut from a
production run
Number of complaints per customer at a hotel
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38. P-Chart Example
A Production manager of a BKT tire company has
inspected the number of defective tires in five random
samples with 20 tires in each sample. The table below
shows the number of defective tires in each sample of 20
tires. Calculate the control limits.
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42. C - Chart Example
The number of weekly customer complaints are
monitored in a large hotel using a c-chart. Develop
three sigma control limits using the data table below.
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46. Process Capability
Evaluating the ability of a production process to meet or
exceed preset specifications. This is called process
capability.
Product specifications, often called tolerances, are
preset ranges of acceptable quality characteristics, such
as product dimensions.
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47. Two parts of process capability
1) Measure the variability of the output of a process, and
2) Compare that variability with a proposed specification or
product tolerance.
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48. Measuring Process Capability
To produce an acceptable product, the
process must be capable and in control
before production begins.
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6
LSLUSL
Cp
49. Example
Let’s say that the specification for
the acceptable volume of liquid is
preset at 16 ounces ±.2 ounces,
which is 15.8 and 16.2 ounces.
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50. Figure (a)
The process produces 99.74 percent (three sigma) of the
product with volumes between 15.8 and 16.2 ounces.
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1pC
51. Figure (b)
The process produces 99.74 percent (three sigma)
of the product with volumes between 15.7 and 16.3
ounces.
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1pC
52. Figure (c)
the production process produces 99.74 percent (three
sigma) of the product with volumes between 15.9 and
16.1 ounces.
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1pC
55. Process capability ratio
(off centering process)
There is a possibility that the process mean may shift over a
period of time, in either direction, i.e., towards the USL or the
LSL. This may result in more defective items then the expected.
This shift of the process mean is called the off-centering of the
process.
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3
,
3
min
LSLUSL
C kp