The document discusses control charts, which are statistical quality control tools used to monitor variation in processes. Control charts were originally developed by Walter Shewhart and focus on measuring variability over time. There are two main types of control charts - attributes charts for categorical data and variables charts for continuous data. Attributes charts include c-charts for counts and p-charts for proportions. Variables charts typically include x-bar and R charts, with the x-bar chart monitoring the process average and the R chart monitoring variation. Control charts can be used to study past performance, evaluate current conditions, and identify sources of variation to help improve processes.

1. DISCOVER . LEARN . EMPOWER
TOPIC OF PRESENTATION
Control Charts
University School of Business
(MBA)
SUBJECT NAME: Operations & Quality Management
SUBJECT CODE: 21BAT632
Simmi Dhyani

2. INTRODUCTION TO OPERATIONS MANAGEMENT

3. Control Charts
originally developed by
Walter A. Shewhart

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 Charts

9. Levine, Prentic-Hall
Control Charts
• UCL = Process Average + 3 Standard Deviations
• LCL = Process Average - 3 Standard Deviations
Process Average
UCL
LCL
X
+ 3
- 3
TIME

10. Levine, Prentice-Hall
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. Levine, Prentice-Hall
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,
ie: 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
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. Control Charts
In conclusion:
the control chart is a means of monitoring
variation in the characteristics of
a product or service

27. Control Charts
Know the
assumptions …
let the
computer do
the hard work.

28. References
TEXT BOOKS
• B. Mahadevan, 2007. Operations Management Theory and Practice, Pearson Education, Second Impression 2007. ISBN:
8177585649
• Dale H. Besterfiled, et at., 2006. Total Quality Management, Pearson Education Asia, Third Edition, Indian Reprint 2006.
ISBN: 978-81-317-3227-4
REFERENCE BOOKS
• R1 William J. Stevenson, 2018. Operations Management Mc Graw Hill Publications, 13th Edition ISBN: 9781259667473
• R2 James R. Evans and William M. Lindsay, 2012. “The Management and Control of Quality”, Cengage Learning, 8th
Edition, First Indian Edition, 2012. ISBN: 978032482706
• R3 Janakiraman. B and Gopal. R. K., 2006. “Total Quality Management-Text and Cases”, Prentice Hall (India) Pvt. Ltd., 2006.
ISBN: 8120329953
Sources:
• https://sixsigmastudyguide.com/statistical-process-control-spc/
• www.uoh.edu.sa/facultymembers/en/M.AICHOUNI/Documents/QEM%20511/Chapter03-Control-Charts-2017.pdf
Video Link:
• https://www.youtube.com/watch?v=lOEqli-YV2I
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29. Assessment Pattern
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30. THANK YOU
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For queries get in touch with me at: simmi.e12923@cumail.in