Linear Programming Operations Management Dr. Ron Tibben-Lembke.
Statistical Process Control Managing for Quality Dr. Ron Lembke.
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Transcript of Statistical Process Control Managing for Quality Dr. Ron Lembke.
Goal of Control Charts collect and present data visually allow us to see when trend appears see when “out of control” point occurs
0102030405060
1 2 3 4 5 6 7 8 9 10 11 12
Process Control Charts Graph of sample data plotted over time
UCL
LCL
Process Average ± 3
Time
X
0102030405060
1 2 3 4 5 6 7 8 9 10 11 12
Process Control Charts Graph of sample data plotted over time
Assignable Cause Variation
Natural Variation
UCL
LCL
Time
X
Definitions of Out of Control1. No points outside control limits
2. Same number above & below center line
3. Points seem to fall randomly above and below center line
4. Most are near the center line, only a few are close to control limits
1. 8 Consecutive pts on one side of centerline
2. 2 of 3 points in outer third
3. 4 of 5 in outer two-thirds region
Attributes vs. VariablesAttributes: Good / bad, works / doesn’t count % bad (P chart) count # defects / item (C chart)
Variables: measure length, weight, temperature (x-bar
chart) measure variability in length (R chart)
Attribute Control Charts Tell us whether points in tolerance or not
p chart: percentage with given characteristic (usually whether defective or not)
np chart: number of units with characteristic c chart: count # of occurrences in a fixed area of
opportunity (defects per car) u chart: # of events in a changeable area of
opportunity (sq. yards of paper drawn from a machine)
p Chart Control Limits
# Defective Items in Sample i
Sample iSize
UCLp p zp 1 p
n
p X i
i1
k
ni
i1
k
p Chart Control Limits
# Defective Items in Sample i
Sample iSize
z = 2 for 95.5% limits; z = 3 for 99.7% limits
# Samples
n
ppzpUCLp
1
p X i
i1
k
ni
i1
k
n ni
i1
k
k
p Chart Control Limits
# Defective Items in Sample i
# Samples
Sample iSize
z = 2 for 95.5% limits; z = 3 for 99.7% limits
n
ppzpUCLp
1
n
ppzpLCLp
1
n ni
i1
k
k
p X i
i1
k
ni
i1
k
p Chart ExampleYou’re manager of a 500-room hotel. You want to achieve the highest level of service. For 7 days, you collect data on the readiness of 200 rooms. Is the process in control (use z = 3)?
© 1995 Corel Corp.
p Chart Hotel DataNo. No. Not
Day Rooms Ready Proportion
1 200 16 16/200 = .0802 200 7 .0353 200 21 .1054 200 17 .0855 200 25 .1256 200 19 .0957 200 16 .080
p Chart Solution16 + 7 +...+ 16
p X i
i1
k
ni
i1
k
121
14000.0864
n ni
i1
k
k
1400
7200
p zp 1 p
n 0.0864 3
0.0864 1 0.0864 200
p Chart Solution16 + 7 +...+ 16
p zp 1 p
n 0.0864 3
0.0864 1 0.0864 200
0.0864 3* 0.01984 0.0864 0.01984
0.1460, and 0.0268
p X i
i1
k
ni
i1
k
121
14000.0864
n ni
i1
k
k
1400
7200
R Chart Type of variables control chart
Interval or ratio scaled numerical data
Shows sample ranges over time Difference between smallest & largest values
in inspection sample
Monitors variability in process Example: Weigh samples of coffee &
compute ranges of samples; Plot
You’re manager of a 500-room hotel. You want to analyze the time it takes to deliver luggage to the room. For 7 days, you collect data on 5 deliveries per day. Is the process in control?
Hotel Example
Hotel Data
Day Delivery Time
1 7.30 4.20 6.10 3.455.552 4.60 8.70 7.60 4.437.623 5.98 2.92 6.20 4.205.104 7.20 5.10 5.19 6.804.215 4.00 4.50 5.50 1.894.466 10.10 8.10 6.50 5.066.947 6.77 5.08 5.90 6.909.30
R &X Chart Hotel Data
SampleDay Delivery TimeMean Range
1 7.30 4.20 6.10 3.45 5.555.32 7.30 + 4.20 + 6.10 + 3.45 + 5.55
5Sample Mean =
R &X Chart Hotel Data
SampleDay Delivery TimeMean Range
1 7.30 4.20 6.10 3.45 5.555.32 3.85
7.30 - 3.45Sample Range =
Largest Smallest
R &X Chart Hotel Data
SampleDay Delivery TimeMean Range
1 7.30 4.20 6.10 3.45 5.555.32 3.85
2 4.60 8.70 7.60 4.43 7.626.59 4.27
3 5.98 2.92 6.20 4.20 5.104.88 3.28
4 7.20 5.10 5.19 6.80 4.215.70 2.99
5 4.00 4.50 5.50 1.89 4.464.07 3.61
6 10.10 8.10 6.50 5.06 6.947.34 5.04
7 6.77 5.08 5.90 6.90 9.306.79 4.22
R Chart Control Limits
UCL D R
LCL D R
R
R
k
R
R
ii
k
4
3
1
Sample Range at Time i
# Samples
From Exhibit 6.13
Control Chart Limits
n A2 D3 D4
2 1.88 0 3.278
3 1.02 0 2.57
4 0.73 0 2.28
5 0.58 0 2.11
6 0.48 0 2.00
7 0.42 0.08 1.92
R Chart Solution
From 6.13 (n = 5)
R
R
k
UCL D R
LCL D R
ii
k
R
R
1
4
3
3 85 4 27 4 227
3 894
(2.11) (3.894) 8 232
(0)(3.894) 0
. . ..
.
X Chart Control Limits
k
RR
k
XX
RAXUCL
k
ii
k
ii
X
11
2
Sample Range at Time i
# Samples
Sample Mean at Time i
X Chart Control Limits
UCL X A R
LCL X A R
X
X
kR
R
k
X
X
ii
k
ii
k
2
2
1 1
Sample Range at Time i
# Samples
Sample Mean at Time i
From 6.13
Exhibit 6.13 Limits
n A2 D3 D4
2 1.88 0 3.278
3 1.02 0 2.57
4 0.73 0 2.28
5 0.58 0 2.11
6 0.48 0 2.00
7 0.42 0.08 1.92
R &X Chart Hotel Data
SampleDay Delivery TimeMean Range
1 7.30 4.20 6.10 3.45 5.555.32 3.85
2 4.60 8.70 7.60 4.43 7.626.59 4.27
3 5.98 2.92 6.20 4.20 5.104.88 3.28
4 7.20 5.10 5.19 6.80 4.215.70 2.99
5 4.00 4.50 5.50 1.89 4.464.07 3.61
6 10.10 8.10 6.50 5.06 6.947.34 5.04
7 6.77 5.08 5.90 6.90 9.306.79 4.22
X Chart Control Limits
X
X
k
R
R
k
ii
k
ii
k
1
1
5 32 6 59 6 797
5 813
3 85 4 27 4 227
3 894
. . ..
. . ..
X Chart Control Limits
From 6.13 (n = 5)
X
X
k
R
R
k
UCL X A R
ii
k
ii
k
X
1
1
2
5 32 6 59 6 797
5 813
3 85 4 27 4 227
3 894
5 813 0 58 * 3 894 8 060
. . ..
. . ..
. . . .
X Chart Solution
From 6.13 (n = 5)
X
X
k
R
R
k
UCL X A R
LCL X A R
ii
k
ii
k
X
X
1
1
2
2
5 32 6 59 6 797
5 813
3 85 4 27 4 227
3 894
5 813 (0 58)
5 813 (0 58)(3.894) = 3.566
. . ..
. . ..
. .
. .
(3.894) = 8.060
Thinking ChallengeYou’re manager of a 500-room hotel. The hotel owner tells you that it takes too long to deliver luggage to the room (even if the process may be in control). What do you do?
© 1995 Corel Corp.
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