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Chapter 6
Homework
Problems 6, 8, 10, 21
Problem 6
Sample Sample Mean Range1 12.1 0.72 11.8 0.43 12.3 0.64 11.5 0.45 11.6 0.9
Sum 59.3 3.0Mean 11.9 0.6
9.115
3.55
5
6.115.113.128.111.12
5,6
k
XX
kn
X-bar Chart
1.12
)6.0(48.09.11
6.11
)6.0(48.09.11
2
2
RAXUCL
RAXLCL
X-bar Chart
X-bar Chart
X-bar Chart
11
11.5
12
12.5
1 2 3 4 5
LCL
UCL
CL
Mean
X-bar Chart
• A special cause occurred on the 3rd and 4th samples.
• Therefore, the X-bar chart is not in control.• The process mean is unstable and,
consequently, not predictable.• Consequently, it is impossible to get a
valid estimate of the mean.• The operators are responsible for finding
and correcting or institutionalizing special causes.
6.0
5
3
5
9.04.06.04.07.0
k
RR
R Chart
2.1
)6.0(2
0
)6.0(0
UCL
LCL
2
0
6
4
3
D
D
n
R Chart
R Chart
0
0.5
1
1.5
1 2 3 4 5
LCL
UCL
R-bar
Range
R Chart
R Chart
• The R chart is in control
• The process variance is therefore stable and predictable.
• The variance can be estimated as
24.03
6)6.0)(48.0(
3ˆ 2
nRA
Fraction Defective
• Assume that USL = 12.5 ounces and LSL = 11.5 ounces.
• If the mean was in control and centered on the target of 12 ounces, the fraction defective would be
0.0376.4812]-2[.5 defectiveFraction
4812.)08.2(
08.224.
125.12ˆ
Area
XUSLz
Fraction Defective
Sample n X p=X/n1 20 1 0.052 20 3 0.153 20 2 0.14 20 1 0.055 20 4 0.26 20 1 0.057 20 2 0.18 20 0 09 20 3 0.1510 20 1 0.05
Sum 0.9p-bar 0.09
Problem 8 (a)
10
20
k
n
09.010
90.0
k
pp
n
pppLCL
n
pppUCL
)1(3
)1(3
0102.0.20
)09.1(09.309.
282.020
)09.1(09.309.
Problem 8 (a)
p Chart
0
0.05
0.1
0.15
0.2
0.25
0.3
1 2 3 4 5 6 7 8 9 10
Sample
p
p=X/n
LCL
UCL
p-bar
Problem 8 (a)
Problem 8 (a)
• All the sample fraction defectives fall within the control limits and form a random pattern.
• The process appears in control.
• We can therefore estimate the process fraction defective.
• Our best estimate is the p-bar of .09.
Problem 8 (a)
• Thus, 9% of the tires produced are defective.
• Since the process is stable, management action is required to improve the process by reducing the fraction defective.
• On Day 8, no defective tires were found.• Since this point is on the LCL, it should be
investigate for a special cause, which may have a favorable impact on the fraction defective.
Day n X p=X/n LCL UCL p-bar11 20 6 0.3 0 0.282 0.0912 20 3 0.15 0 0.282 0.0913 20 3 0.15 0 0.282 0.0914 20 4 0.2 0 0.282 0.09
Problem 8 (b)
The sample fraction defective on Day 11 falls above the UCL. The process fraction defective isTherefore out of control. The underlying special cause has an unfavorable affect on the processbecause it shifted the process fraction defective upward.
Week c c-bar1 4 4.42 5 4.43 6 4.44 6 4.45 3 4.46 2 4.47 6 4.48 7 4.49 3 4.410 4 4.411 3 4.412 4 4.4
Sum c 53c-bar 4.4
Problem 10
4.4
12
53
k
cc
72.10
4.434.4
3
0or 89.1
4.434.4
3
ccUCL
ccLCL
c-Chart – Control Limits
c-Chart
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10 11 12
Week
c, n
um
be
r o
f d
efe
cts
c
LCL
UCL
c-bar
Interpretation of Chart
• The process is in control.
• Each billing statement contains on average 4.4 errors, which is unacceptably high.
• Since the process is in control, management action is required to improve the process by reducing the average number of errors.
39.1
)12.0(6
5.95.10
ˆ6
LSLUSL
Cp
Problem 21
Since the capability index is greater than 1, the process is capable.
17.4
)39.1(3
3
Cpz
Problem 21
Process fraction defective = .00003, or 3 outof every 100,000 bottles will be out of spec.
Problem 21
The index assumes that the process is on target. However, the process is not on target. Therefore,we should compute CT.
z was computed using the NORMSDIST in Excel.
.
=2*NORMSDIST(G26)
-4.17 0.000030526
G
.00003
0-4.17 z
.00003
4.17
NORMSDIST(G26) = Area under curve to left of z
Problem 21
USLLSL
71.0
)108.9()12.0(6
5.95.10
Target)(ˆ6
2
2
X
LSLUSLCT
Problem 21
Since the capability index is less than 1, the processis not capable. If process is center on target, the capability index would increase to 1.39, and the process would be capable.