performance analysis for process improvement · conclusions process performance analysis provides...
TRANSCRIPT
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Performance Analysis forProcess Improvement
Michael V. Petrovich
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Warning:This material may cause a
paradigm shift.Should you not wish yourparadigm shifted you maywant to leave at this time.
Introduction
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Presentation Outline
◆ Introduction
◆ Sampling
◆ Process Performance Measures
◆ Process Performance Analysis
◆ Example
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Quality Goals
◆ The elimination of nonconformance
◆ The minimization of variation aroundappropriate targets
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Quality Goal Questions
◆ How well are these quality goals beingrealized?
◆ What must be done to improve?
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LSL USL
(USL - LSL)
NaturalTolerance
Traditional Approach
◆ Establish statistical control of a process
◆ Determine the underlying processdistribution
◆ Conduct a Process Capability Analysis
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Scenario #1
◆ A Plastic Lid Manufacturer– 40 molding machines
– 54 Tools per molder
– Tools are replaced every few weeks
– Tool-to-Tool Differences found
– Differences from Raw Material batches
– Variation from maintenance cycles, startupperiods, and operator adjustments
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Scenario #2
◆ A Metal Crown Company– Eight Presses
– Each Press Contains 22 dies
– Differences exist die-to-die and press-to-press
– The process exhibits tool wear
– Slight fluctuations are observed with lot-to-lotchanges in steel
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Scenario #3
◆ An Aluminum Can Manufacturer– Two Lines in One plant
– Each Line contains 16 stations
– Station-to-station differences are found
– This process also undergoes tool wear
– Perfect through-time stability (or control) is notobserved in many of the stations
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Conclusions
◆ Traditional capability methods areinadequate in these situations
◆ Without statistical control, capabilityassessment cannot be made
◆ The number of process streams yields anightmare to mathematically model
◆ Practitioners still need a means ofassessing their process
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Performance Ppk
Pp
Ppm
Capability Cpk
Cp
Cpm
Performance Measures
◆ In recent years “Process PerformanceMeasures” have gained popularity
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Process Performance MeasuresDiffer from Capability Measures◆ Statistical control not required for analysis
◆ Common and special causes may both bepresent
◆ Do not consider distributional shape
◆ Measure past performance only
◆ Do not predict the future
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Process PerformanceMeasures
◆ Assess total observed variation producedduring a particular time period
◆ Can be used as validation measures forprocess improvement
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PPA
Process PerformanceAnalysis
◆ Uses Process Performance Measures,descriptive measures, and graphicaltechniques to study processes
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With Process PerformanceAnalysis
◆ Assess processes before statistical control isachieved
◆ Determine sources of process loss
◆ Make comparisons among characteristics,products, plants, and suppliers
◆ Determine control and improvement priorities
◆ Assess results of process improvement efforts
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Presentation Outline
◆Introduction
◆Sampling
◆Process Performance Measures
◆Process Performance Analysis
◆Example
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Sampling
◆ Measure total outgoing variation as sent tothe customer
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Total Outgoing Variation
◆ Variation inherent to the process
◆ Variation from sources such as:– differences from lot-to-lot,
– tool-to-tool,
– line-to-line,
– shift-to-shift,
– setup-to-setup,
– day-to-day,
– and throughout maintenance cycles.
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EOL
Sampling
◆ Generally done at end-of-line
◆ May be done upstream if characteristicformation is complete and will not change
◆ Should be done after sorting
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Stratification
◆ Used for process diagnosis
◆ Stratifications may include:● Lines
● Tools
● Stations
● Cavities
● or Other
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time
Sample Frequency
◆ Samples taken periodically within definedtime periods
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Sampling Amount
◆ Long periods are preferred
◆ Minimum one month, preferably, threemonths worth of data
◆ 1,000 is a desired minimum
◆ 10,000 or more may provide betteranalysis
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Measurement SystemAnalysis
◆ Measurement system analysis should beconducted prior to data collection
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Presentation Outline
◆ Introduction
◆ Sampling
◆ Process Performance Measures
◆ Process Performance Analysis
◆ Example
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Process PerformanceMeasures
◆ Ppm
◆ Ppk
◆ Pp
◆ Pp(process stream)
◆ Cp(potential)
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Ppm =USL - LSL
6 Σ (X-T )2
n-1
=
6 s2 + nn-1
¯(X-T )2
USL - LSL
Ppm
◆ Assessing overall performance
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Ppm will improve as:
◆ Process brought into better control
◆ Differences in tooling, setup, operators,machines, and material are reduced
◆ Process brought on target
◆ Inherent process variation reduced
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Ppk = min (USL - X, X - LSL)3s
Ppk
◆ Assessing overall performance withouttargets
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Pp = ( USL - LSL )6 s
Pp
◆ Assessing performance as if process wason target
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| X - T |
USL - LSL
_
% Off-Target = x 100%
% Off-Target
◆ A further diagnostic measure
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Pp (process stream) = ( USL - LSL )6 s within stream
Pp (process stream)
◆ Assessing performance without processstream differences
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| X max stream - X min stream |
USL - LSL
_% Process Stream Difference =
x 100%
_
% Process Stream Difference
◆ A further diagnostic measure
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Cp(potential) = ( USL - LSL )6 σpotential
^
Cp (potential)
◆ Assessing Potential Performance
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^σpotential
Potential Standard Deviation
◆ Short-term capability studies
◆ Estimated using average or mediandispersion statistics
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Nonconforming Proportion (ppm) =
1,000,000 x Total NonconformingTotal Items
A further check
◆ Observed Nonconforming Rates
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PRODUCT OK Not OK
Attributes Data
◆ Similar sampling may be done forattributes
◆ Nonconforming rate used to evaluateattribute performance
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Presentation Outline
◆ Introduction
◆ Sampling
◆ Process Performance Measures
◆ Process Performance Analysis
◆ Example
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Ppm < Pp < Pp(process stream) < Cp(potential)
Stacked Bar Charts
◆ The following Relation holds:
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Stacked Bar Charts
Ppm
Pp - Ppm
Pp(process stream) - Pp
Cp(potential) - Pp(process Stream)
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0
0.5
1.0
1.5
2.0
Per
form
ance
Cp(potential)
Pp(process stream)
Pp
Ppm
Process Performance Analysis
Stacked Bar Charts
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Per
form
ance
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Target Stream Target &Stream
Time(Control)
Cannot Compare Areas of theStacked Components
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τ² = σ²potential + σ²off-target +σ²process stream + σ²time
Variance Components
◆ Total variance about target, τ², can bedecomposed
◆ May be done with simple formulas
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Target Loss (49.00%)
Process Stream (22.00%)
Time (Control) (10.00%)
Process (19.00%)
Percent Variance Contributions
Variance Components
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Presentation Outline
◆ Introduction
◆ Sampling
◆ Process Performance Measures
◆ Process Performance Analysis
◆ Example
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Case StudyAluminum Lids - Height
◆ Aluminum Lids are produced on a Presswith 22 Stations
◆ Each day, Lids from each Station aresampled and Lid Height measured
◆ Each sample contains a single Lid fromeach station
◆ Data collected over Three-month period
◆ Specifications are 98 ± 5
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Fre
qu
ency
0
50
100
150
200
250
300
350
400
92.50 93.25 94.00 94.75 95.50 96.25 97.00 97.75 98.50 99.25 100.00 100.75 101.50 102.25 103.00
Height
LSL Target USL
Histogram of Combined Data
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Bo x & W h iske r P lo ts
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
103
102
101
100
99
98
97
96
95
94
93
Box Plot of Each Station
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Run Chart of Combined DataIn
divi
dual
Val
ues
92.0
93.0
94.0
95.0
96.0
97.0
98.0
99.0
100.0
101.0
102.0
103.0
104.0
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131
Point Number
Sig
ma
0.0
0.5
1.0
1.5
2.0
LSL=93.00
Target=98.00
USL=103.00
height
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Control Chart from Station #1In
divi
dual
Val
ues
96.0
96.5
97.0
97.5
98.0
98.5
99.0
99.5
100.0
100.5
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101105109113117121125129133
Point Number
Mov
ing
Ran
ge
0.0
0.5
1.0
1.5
2.0
UCL=99.159
CL=98.368
LCL=97.577
UCL=0.972
CL=0.297
LCL=0.000
Height Station 1
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Control Chart from Station #5In
divi
dual
Val
ues
99.4
99.6
99.8
100.0
100.2
100.4
100.6
100.8
101.0
101.2
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133
Point Number
Mov
ing
Ran
ge
0.0
0.5
1.0
1.5
UCL=101.053
CL=100.356
LCL=99.659
UCL=0.856
CL=0.262
LCL=0.000
Height Station 5
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Run Chart Station #1 with Specs
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133
Point Number
Val
ues
92.0
93.0
94.0
95.0
96.0
97.0
98.0
99.0
100.0
101.0
102.0
103.0
104.0
LSL=93.00
Target=98.00
USL=103.00
Height Station 1
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Run Chart Station #5 with Specs
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131
Point Number
Val
ues
92.0
93.0
94.0
95.0
96.0
97.0
98.0
99.0
100.0
101.0
102.0
103.0
104.0
LSL=93.00
Target=98.00
USL=103.00
Height Station 5
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Station n mean s s(MMR) 1 134 98.37 0.63 0.26 5 134 100.36 0.29 0.23 15 134 100.27 0.68 0.23 20 134 100.80 0.71 0.24 22 134 101.06 0.49 0.27
SQRT(MSW) (Std Dev Within) = 0.58 AVG Std(MMR) (Std Dev from Median MR) = 0.23
Descriptive Statistics
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n = 2948 Mean = 100.48 Std. Dev = 0.98 Low = 96.26 Q1 = 100.06 Median = 100.59 Q3 = 101.14 High = 102.94
Descriptive Statistics
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%Off Target = 24.82%Max Station Mean = 102.21Min Station Mean = 98.37%Process Stream Loss = 38.42%
Ppk = 0.858Ppm = 0.625Pp = 1.703Pp(Stream)= 2.862Cp(pot) = 7.212
n = 2948Total Out = 0 (0 ppm)
Performance Measures
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Cp(potential)Pp(s tream)PpPpm
Proce ss Pe rform a nce Ana lysis
Height1
Pe
rfo
rma
nc
e
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Process PerformanceAnalysis
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OpportunityPpm
Process Performance AnalysisH
eig
ht
Tar
get
Str
eam
Tar
get
+S
trea
m
Tim
e(C
on
tro
l)
Per
form
ance
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Process PerformanceAnalysis
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Process Stream (8.71%)Process (0.70%)
Time(Control) (4.07%)
Target Loss (86.52%)
Process Performance AnalysisVariance Components
Variance Components
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Conclusions for Example
◆ Biggest opportunity: getting the individualtools on target
◆ Improving through-time stability (control)will have minimal effect in this example
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PpmPpPp(s tream)Cp(potentia l)
Proc e s s Pe rform a nc e Ana lys is
1 2 3 4 5
Pe
rfo
rma
nc
e
11
10
9
8
7
6
5
4
3
2
1
0
Summary Results ofSelected Cases
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PotentialTarget Los sProc es s StreamTime(c ontrol)
Proce s s Pe rform a nc e Ana lys isVaria nc e Com ponents
1 2 3 4 5
Va
ria
nc
e %
100
90
80
70
60
50
40
30
20
10
0
Summary Results ofSelected Cases
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Conclusions
◆ Process Performance Analysis provides an effectivemeans of describing performance of complex processesnot in a state of control
◆ PPA is an effective means of showing effects of processcontrol and improvement efforts
◆ PPA helps the practitioner determine how they aredoing, what processes need work and which one do not
◆ PPA gives you another way of looking at your processes
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The End