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Lecture 5
Virtual University of Pakistan 1
Control Charts
originally developed by
Walter A. Shewhart
History - beginnings
• The control chart - invented by Walter Shewhart
• How to improve the reliability of telephony transmission systems?
• Shewhart framed the problem in terms of common and special causes of
variation
• In 1924, May 16, in an internal memo Shewhart introduced the control chart
as a tool for distinguishing between the two causes of variation.
• Shewhart's boss, George Edwards: "Dr. Shewhart prepared a little
memorandum only about a page in length… all of the essential principles and
considerations which are involved in what we know today as process quality
control."
Control Charts
The control chart is a statistical quality control
tool used in the monitoring variation in the
characteristics of
a product or service
Control Charts
The control chart focuses on the time
dimension and the nature of the
variability in the system.
Control Charts
The control chart may be used to study
past performance and/or to evaluate
present conditions.
Control Charts
Data collected from a control chart may
form the basis for process improvement.
• Control chart - a graph used in SPC that shows whether a sample of data
falls within the normal range of variation.
• Control chart: central line (CL), upper (UCL) and lower control limits (LCL).
– Control limits separate common from assignable causes of variation
• Control charts for variables
– monitor characteristics that can be measured
• Control charts for attributes
– monitor characteristics that can be counted
Control Charts
• UCL = Process Average + 3 Standard Deviations
• LCL = Process Average - 3 Standard Deviations
Process Average
UCL
LCL
X
+ 3
- 3
TIME
0
20
40
60
1 3 5 7 9 11
X
Time
0
20
40
60
1 3 5 7 9 11
X
Time
• Graph of sample data plotted over time
Assignable Cause
Variation
Random Variation
Process Average

Mean
Control Charts
UCL
LCL
Control Charts
X X
X
Common Cause Variation: no points
outside control limit
Special Cause Variation: two points
outside control limit
Downward Pattern: no points outside
control limit; however, eight or more
points in trend
Control Charts
• Attribute charts
• Variables charts
Control Charts
Charts may
be used for
categorical
variables.
[i.e.: attributes]
Control Charts
Whenever a character of interest is measured
on a nominative or an interval, i.e.:
categorical, scale…an attributes control chart
is used to monitor a process.
Control Charts
• Attributes Control Charts
counts [c-chart]
proportion [p-charts]
Control Charts
• Attributes Control Charts
when sample size are not constant
and/or are unknown
use counts charts
[c-charts]
Control Charts
c Chart for num_defect
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
0
4
8
12
16
20
c
Centerline = 10.00
UCL = 19.49
LCL = 0.51
Control Charts
• Attributes Control Charts
when sample size are constant and are known
use proportion charts
[p-charts]
Control Charts
p Chart for defects/100
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
28.0
29.0
30.0
0
0.03
0.06
0.09
0.12
0.15
p
Centerline = 0.04
UCL = 0.10
LCL = 0.00
Control Charts
Charts may
be used for interval
or ratio data [i.e.:
variables ]
Control Charts
Whenever a character of interest is
measured on an interval or a ratio scale, a
variables control chart is used to monitor a
process.
Control Charts
• Variables Control Charts
Mean and Range charts
[x-bar & R charts]
Control Charts
Variables control charts are typically used in
pairs.
…… one chart monitors process average while
the other monitors the variation in a process.
Control Charts
p Chart for defects/100
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
28.0
29.0
30.0
0
0.03
0.06
0.09
0.12
0.15
p
Centerline = 0.04
UCL = 0.10
LCL = 0.00
X-bar Chart for observa
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
190
194
198
202
206
210
214
X-bar
Centerline = 203.98
UCL = 208.21
LCL = 199.75
Control Charts
Range Chart for observa
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
0
3
6
9
12
15
Range
Centerline = 5.80
UCL = 13.24
LCL = 0.00
Average Run Length (ARL)
• Expected number of samples taken before shift is detected is called
the Average Run Length (ARL)
 
 
1
1
1
1
1
r
r
ARL r 




  


Performance of Any Shewhart Control Chart
• In-Control ARL:
– Average number of points plotted on control chart before a false alarm occurs
(ideally, should be large)
• Out-of-Control ARL:
– Average number of points, after the process goes out-of-control, before the
control chart detects it
(ideally, should be small)
0
1
ARL


1
1
1
ARL



Practice
Virtual University of Pakistan 28

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lecture 5 SPC Charta.pptx

  • 2. Control Charts originally developed by Walter A. Shewhart
  • 3. History - beginnings • The control chart - invented by Walter Shewhart • How to improve the reliability of telephony transmission systems? • Shewhart framed the problem in terms of common and special causes of variation • In 1924, May 16, in an internal memo Shewhart introduced the control chart as a tool for distinguishing between the two causes of variation. • Shewhart's boss, George Edwards: "Dr. Shewhart prepared a little memorandum only about a page in length… all of the essential principles and considerations which are involved in what we know today as process quality control."
  • 4. Control Charts The control chart is a statistical quality control tool used in the monitoring variation in the characteristics of a product or service
  • 5. Control Charts The control chart focuses on the time dimension and the nature of the variability in the system.
  • 6. Control Charts The control chart may be used to study past performance and/or to evaluate present conditions.
  • 7. Control Charts Data collected from a control chart may form the basis for process improvement.
  • 8. • Control chart - a graph used in SPC that shows whether a sample of data falls within the normal range of variation. • Control chart: central line (CL), upper (UCL) and lower control limits (LCL). – Control limits separate common from assignable causes of variation • Control charts for variables – monitor characteristics that can be measured • Control charts for attributes – monitor characteristics that can be counted
  • 9. Control Charts • UCL = Process Average + 3 Standard Deviations • LCL = Process Average - 3 Standard Deviations Process Average UCL LCL X + 3 - 3 TIME
  • 10. 0 20 40 60 1 3 5 7 9 11 X Time 0 20 40 60 1 3 5 7 9 11 X Time • Graph of sample data plotted over time Assignable Cause Variation Random Variation Process Average  Mean Control Charts UCL LCL
  • 11. Control Charts X X X Common Cause Variation: no points outside control limit Special Cause Variation: two points outside control limit Downward Pattern: no points outside control limit; however, eight or more points in trend
  • 12. Control Charts • Attribute charts • Variables charts
  • 13. Control Charts Charts may be used for categorical variables. [i.e.: attributes]
  • 14. Control Charts Whenever a character of interest is measured on a nominative or an interval, i.e.: categorical, scale…an attributes control chart is used to monitor a process.
  • 15. Control Charts • Attributes Control Charts counts [c-chart] proportion [p-charts]
  • 16. Control Charts • Attributes Control Charts when sample size are not constant and/or are unknown use counts charts [c-charts]
  • 17. Control Charts c Chart for num_defect 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 0 4 8 12 16 20 c Centerline = 10.00 UCL = 19.49 LCL = 0.51
  • 18. Control Charts • Attributes Control Charts when sample size are constant and are known use proportion charts [p-charts]
  • 19. Control Charts p Chart for defects/100 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 0 0.03 0.06 0.09 0.12 0.15 p Centerline = 0.04 UCL = 0.10 LCL = 0.00
  • 20. Control Charts Charts may be used for interval or ratio data [i.e.: variables ]
  • 21. Control Charts Whenever a character of interest is measured on an interval or a ratio scale, a variables control chart is used to monitor a process.
  • 22. Control Charts • Variables Control Charts Mean and Range charts [x-bar & R charts]
  • 23. Control Charts Variables control charts are typically used in pairs. …… one chart monitors process average while the other monitors the variation in a process.
  • 24. Control Charts p Chart for defects/100 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 0 0.03 0.06 0.09 0.12 0.15 p Centerline = 0.04 UCL = 0.10 LCL = 0.00 X-bar Chart for observa 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 190 194 198 202 206 210 214 X-bar Centerline = 203.98 UCL = 208.21 LCL = 199.75
  • 25. Control Charts Range Chart for observa 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 0 3 6 9 12 15 Range Centerline = 5.80 UCL = 13.24 LCL = 0.00
  • 26. Average Run Length (ARL) • Expected number of samples taken before shift is detected is called the Average Run Length (ARL)     1 1 1 1 1 r r ARL r          
  • 27. Performance of Any Shewhart Control Chart • In-Control ARL: – Average number of points plotted on control chart before a false alarm occurs (ideally, should be large) • Out-of-Control ARL: – Average number of points, after the process goes out-of-control, before the control chart detects it (ideally, should be small) 0 1 ARL   1 1 1 ARL   

Editor's Notes

  1. (c) 2002-2014 D.H. Jensen & R.C. Wurl
  2. (c) 2002-2014 D.H. Jensen & R.C. Wurl