Ch03 SPC and Process Capability
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Transcript of Ch03 SPC and Process Capability
Lecture Outline
• Basics of Statistical Process Control• Control Charts• Control Charts for Attributes• Control Charts for Variables• Control Chart Patterns• SPC with Excel and OM Tools• Process Capability
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Statistical Process Control (SPC)
• Statistical Process Control• monitoring production process
to detect and prevent poor quality
• Sample• subset of items produced to
use for inspection
• Control Charts• process is within statistical
control limits
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Process Variability
• Random• inherent in a process• depends on equipment
and machinery, engineering, operator, and system of measurement
• natural occurrences
• Non-Random• special causes• identifiable and
correctable• include equipment out of
adjustment, defective materials, changes in parts or materials, broken machinery or equipment, operator fatigue or poor work methods, or errors due to lack of training
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SPC in Quality Management
• SPC uses• Is the process in control?• Identify problems in order to make
improvements• Contribute to the TQM goal of continuous
improvement
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Quality Measures:Attributes and Variables
• Attribute• A characteristic which is evaluated with a
discrete response• good/bad; yes/no; correct/incorrect
• Variable measure• A characteristic that is continuous and can be
measured• Weight, length, voltage, volume
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SPC Applied to Services
• Nature of defects is different in services• Service defect is a failure to meet customer
requirements• Monitor time and customer satisfaction
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SPC Applied to Services
• Hospitals• timeliness & quickness of care, staff responses to requests,
accuracy of lab tests, cleanliness, courtesy, accuracy of paperwork, speed of admittance & checkouts
• Grocery stores• waiting time to check out, frequency of out-of-stock items, quality
of food items, cleanliness, customer complaints, checkout register errors
• Airlines• flight delays, lost luggage & luggage handling, waiting time at
ticket counters & check-in, agent & flight attendant courtesy, accurate flight information, cabin cleanliness & maintenance
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SPC Applied to Services
• Fast-food restaurants• waiting time for service, customer complaints, cleanliness, food
quality, order accuracy, employee courtesy
• Catalogue-order companies• order accuracy, operator knowledge & courtesy, packaging,
delivery time, phone order waiting time
• Insurance companies• billing accuracy, timeliness of claims processing, agent
availability & response time
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Where to Use Control Charts
• Process • Has a tendency to go out of control
• Is particularly harmful and costly if it goes out of control
• Examples• At beginning of process because of waste to begin
production process with bad supplies• Before a costly or irreversible point, after which product is
difficult to rework or correct• Before and after assembly or painting operations that
might cover defects• Before the outgoing final product or service is delivered
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Control Charts
• A graph that monitors process quality• Control limits
• upper and lower bands of a control chart
• Attributes chart• p-chart• c-chart
• Variables chart• mean (x bar – chart)• range (R-chart)
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Process Control Chart
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1 2 3 4 5 6 7 8 9 10Sample number
Uppercontrol
limit
Processaverage
Lowercontrol
limit
Out of control
Normal Distribution
• Probabilities for Z= 2.00 and Z = 3.00
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=0 1 2 3-1-2-3
95%
99.74%
A Process Is in Control If …
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1. … no sample points outside limits
2. … most points near process average
3. … about equal number of points above and below centerline
4. … points appear randomly distributed
Control Charts for Attributes
• p-chart• uses portion defective in a sample
• c-chart• uses number of defects (non-conformities) in a
sample
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p-Chart
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UCL = p + zp
LCL = p - zp
z =number of standard deviations from process average
p =sample proportion defective; estimates process mean
p = standard deviation of sample proportionp =
p(1 - p)
nn
Construction of p-Chart
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20 samples of 100 pairs of jeans
NUMBER OF PROPORTIONSAMPLE # DEFECTIVES DEFECTIVE
1 6 .06
2 0 .00
3 4 .04
: : :
: : :
20 18 .18
200
Construction of p-Chart
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UCL = p + z = 0.10 + 3p(1 - p)
n
0.10(1 - 0.10)
100
UCL = 0.190
LCL = 0.010
LCL = p - z = 0.10 - 3p(1 - p)
n
0.10(1 - 0.10)
100
= 200 / 20(100) = 0.10total defectives
total sample observationsp =
Construction of p-Chart
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0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
Pro
por
tion
defe
ctiv
e
Sample number2 4 6 8 10 12 14 16 18 20
UCL = 0.190
LCL = 0.010
p = 0.10
p-Chart in Excel
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Click on “Insert” then “Charts”
to construct control chart
I4 + 3*SQRT(I4*(1-I4)/100)
I4 - 3*SQRT(I4*(1-I4)/100)
Column values copiedfrom I5 and I6
c-Chart
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UCL = c + zc
LCL = c - zc
where
c = number of defects per sample
c = c
c-Chart
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Number of defects in 15 sample rooms
1 122 83 16
: :: :15 15 190
SAMPLE
c = = 12.6719015
UCL = c + zc
= 12.67 + 3 12.67= 23.35
LCL = c - zc
= 12.67 - 3 12.67= 1.99
NUMBER OF
DEFECTS
c-Chart
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3
6
9
12
15
18
21
24
Num
ber
of
defe
cts
Sample number
2 4 6 8 10 12 14 16
UCL = 23.35
LCL = 1.99
c = 12.67
Control Charts for Variables
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Range chart ( R-Chart ) Plot sample range (variability)
Mean chart ( x -Chart ) Plot sample averages
3-25
x-bar Chart: Known
UCL = x + z x LCL = x - z x
-
-
==
Where
= process standard deviation
x = standard deviation of sample means =/k = number of samples (subgroups)n = sample size (number of observations)
x1 + x2 + ... + xk
kX = =
- - -
n
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Observations(Slip-Ring Diameter, cm) n
Sample k 1 2 3 4 5 -
x-bar Chart Example: Known
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x
We know σ = .08
x-bar Chart Example: Known
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= 5.01 - 3(.08 / )10
= 4.93
_____10
50.09 = 5.01X =
=
LCL = x - z x = -UCL = x + z x
= -= 5.01 + 3(.08 / )10
= 5.09
x-bar Chart Example: Unknown
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_UCL = x + A2R LCL = x - A2R
= =_
where
x = average of the sample means
R = average range value
=_
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Control Chart Factors
n A2 D3 D42 1.880 0.000 3.2673 1.023 0.000 2.5754 0.729 0.000 2.2825 0.577 0.000 2.1146 0.483 0.000 2.0047 0.419 0.076 1.9248 0.373 0.136 1.8649 0.337 0.184 1.81610 0.308 0.223 1.77711 0.285 0.256 1.74412 0.266 0.283 1.71713 0.249 0.307 1.69314 0.235 0.328 1.67215 0.223 0.347 1.65316 0.212 0.363 1.63717 0.203 0.378 1.62218 0.194 0.391 1.60919 0.187 0.404 1.59620 0.180 0.415 1.58521 0.173 0.425 1.57522 0.167 0.435 1.56523 0.162 0.443 1.55724 0.157 0.452 1.54825 0.153 0.459 1.541
Factors for R-chartSample
Size Factor for X-chart
x-bar Chart Example: Unknown
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OBSERVATIONS (SLIP- RING DIAMETER, CM)
SAMPLE k 1 2 3 4 5 x R
1 5.02 5.01 4.94 4.99 4.96 4.98 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 5.14 5.10 4.99 5.08 5.09 5.08 0.15
10 5.01 4.98 5.08 5.07 4.99 5.03 0.10
50.09 1.15Totals
x-bar Chart Example: Unknown
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∑ Rk
1.1510R = = = 0.115
_ ____ ____
_UCL = x + A2R
=
= 50.0910
_____x = = = 5.01 cmx
k___
= 5.01 + (0.58)(0.115) = 5.08
_LCL = x - A2R
== 5.01 - (0.58)(0.115) = 4.94
x- bar Chart
Example
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UCL = 5.08
LCL = 4.94
Mea
n
Sample number
|1
|2
|3
|4
|5
|6
|7
|8
|9
|10
5.10 –
5.08 –
5.06 –
5.04 –
5.02 –
5.00 –
4.98 –
4.96 –
4.94 –
4.92 –
x = 5.01=
R- Chart
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UCL = D4R LCL = D3R
R = Rk
WhereR = range of each samplek = number of samples (sub groups)
R-Chart Example
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OBSERVATIONS (SLIP- RING DIAMETER, CM)
SAMPLE k 1 2 3 4 5 x R
1 5.02 5.01 4.94 4.99 4.96 4.98 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 5.14 5.10 4.99 5.08 5.09 5.08 0.15
10 5.01 4.98 5.08 5.07 4.99 5.03 0.10
50.09 1.15Totals
R-Chart Example
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Retrieve chart factors D3 and D4
UCL = D4R = 2.11(0.115) = 0.243
LCL = D3R = 0(0.115) = 0_
_
R-Chart Example
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UCL = 0.243
LCL = 0
Ra
nge
Sample number
R = 0.115
|1
|2
|3
|4
|5
|6
|7
|8
|9
|10
0.28 –
0.24 –
0.20 –
0.16 –
0.12 –
0.08 –
0.04 –
0 –
Using x- bar and R-Charts Together
• Process average and process variability must be in control
• Samples can have very narrow ranges, but sample averages might be beyond control limits
• Or, sample averages may be in control, but ranges might be out of control
• An R-chart might show a distinct downward trend, suggesting some nonrandom cause is reducing variation
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Control Chart Patterns
• Run• sequence of sample values that display same
characteristic
• Pattern test• determines if observations within limits of a control
chart display a nonrandom pattern
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Control Chart Patterns
• To identify a pattern look for:• 8 consecutive points on one side of the center line• 8 consecutive points up or down• 14 points alternating up or down• 2 out of 3 consecutive points in zone A (on one side
of center line)• 4 out of 5 consecutive points in zone A or B (on one
side of center line)
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Control Chart Patterns
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UCL
LCL
Sample observationsconsistently above thecenter line
LCL
UCL
Sample observationsconsistently below thecenter line
Control Chart Patterns
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LCL
UCL
Sample observationsconsistently increasing
UCL
LCL
Sample observationsconsistently decreasing
Zones for Pattern Tests
3-43
UCL
LCL
Zone A
Zone B
Zone C
Zone C
Zone B
Zone A
Process average
3 sigma = x + A2R=
3 sigma = x - A2R=
2 sigma = x + (A2R)= 23
2 sigma = x - (A2R)= 23
1 sigma = x + (A2R)= 13
1 sigma = x - (A2R)= 13
x=
Sample number
|1
|2
|3
|4
|5
|6
|7
|8
|9
|10
|11
|12
|13
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Performing a Pattern Test
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1 4.98 B — B
2 5.00 B U C
3 4.95 B D A
4 4.96 B D A
5 4.99 B U C
6 5.01 — U C
7 5.02 A U C
8 5.05 A U B
9 5.08 A U A
10 5.03 A D B
SAMPLE x ABOVE/BELOW UP/DOWN ZONE
Sample Size Determination
• Attribute charts require larger sample sizes• 50 to 100 parts in a sample
• Variable charts require smaller samples• 2 to 10 parts in a sample
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Process Capability
• Compare natural variability to design variability • Natural variability
• What we measure with control charts• Process mean = 8.80 oz, Std dev. = 0.12 oz
• Tolerances• Design specifications reflecting product
requirements• Net weight = 9.0 oz 0.5 oz • Tolerances are 0.5 oz
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Process Capability Ratio
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Cp =tolerance range
process range
upper spec limit - lower spec limit
6=
Process Capability Index
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Cpk = minimum
x - lower specification limit
3
=
upper specification limit - x
3
=
,
Computing Cpk
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Net weight specification = 9.0 oz 0.5 ozProcess mean = 8.80 ozProcess standard deviation = 0.12 oz
Cpk = minimum
= minimum , = 0.83
x - lower specification limit
3
=
upper specification limit - x
3
=
,
8.80 - 8.50
3(0.12)
9.50 - 8.80
3(0.12)
Process Capability With Excel
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=(D6-D7)/(6*D8)
See formula bar
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