Six Sigma in Measurement Systems Evaluating the Hidden Factory (1)

5/28/2018 Six Sigma in Measurement Systems Evaluating the Hidden Factory (1)

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slide 1

Six Sigma in Measurement Systems:Evaluating the Hidden Factory

Scrap

Rework

Hidden Factory

NOTOK

OperationInputs Inspect First TimeCorrect

OK

Time, cost, people

Bill RodebaughDirector, Six Sigma

GRACE


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slide 2

Objectives

The Hidden Factory Concept

What is a Hidden Factory? What is a Measurement Systems Role in the Hidden

Factory?

Review Key Measurement System metrics including

%GR&R and P/T ratio Case Study at W. R. GRACE

Measurement Study Set-up and Minitab Analysis

Linkage to Process

Benefits of an Improved Measurement System How to Improve Measurement Systems in an

Organization


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slide 3

The Hidden Factory -- Process/Production

Scrap

Rework

Hidden Factory

NOTOK

OperationInputs Inspect First Time

Correct

OK

Time, cost, people

What Comprises the Hidden Factory in a Process/Production Area?

Reprocessed and Scrap materials -- First time out of spec, not reworkable

Over-processed materials -- Run higher than target with higherthan needed utilities or reagents

Over-analyzed materials -- High Capability, but multiple in-processsamples are run, improper SPC leading to over-control


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slide 4

The Hidden Factory -- Measurement Systems

Waste

Re-test

Hidden Factory

NOTOK

Lab WorkSample

InputsInspect Production

OK

Time, cost, people

What Comprises the Hidden Factory in a Laboratory Setting?

Incapable Measurement Systems -- purchased, but are unusabledue to high repeatability variation and poor discrimination

Repetitive Analysis -- Test that runs with repeats to improve knownvariation or to unsuccessfully deal with overwhelming sampling issues

Laboratory Noise Issues -- Lab Tech to Lab Tech Variation, Shift toShift Variation, Machine to Machine Variation, Lab to Lab Variation


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slide 5

The Hidden Factory Linkage

Production Environments generally rely upon in-

process sampling for adjustment As Processes attain Six Sigma performance they begin

to rely less on sampling and more upon leveraging thefew influential X variables

The few influential X variables are determined largelythrough multi-vari studies and Design ofExperimentation (DOE)

Good multi-vari and DOE results are based uponacceptable measurement analysis


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slide 6

Objectives



Factory?




Linkage to Process


Organization


7/30slide 7

Possible Sources of Process Variation

We will look at repeatability and reproducibility as primary

contributors to measurement error

Stability Linearity

Long-term

Process Variation

Short-term

Process Variation

Variation

w/i sample

Actual Process Variation

Repeatability Calibration

Variation due

to gage

Variation due

to operators

Measurement Variation

Observed Process Variation

SystemtMeasuremen

2

ocesslAc tua

2

ocessObserved

2 PrPr

it yproducib i l 2

ypeatabilit 2

SystemtMeasuremen2

ReRe


8/30slide 8

11010090807060504030

15

10

5

0

Observed

Frequen

cy

LSL USL

Actualprocess variation -

Nomeasurement error

Observed process

variation -

Withmeasurement error

11010090807060504030

15

10

5

0

Process

Frequency

LSL USL

How Does Measurement Error Appear?


9/30slide 9

Measurement System Terminology

Discrimination - Smallest detectable increment between two measured values

Accuracy related terms

True value- Theoretically correct value

Bias- Difference between the average value of all measurements of a sample and thetrue value for that sample

Precision related terms

Repeatability- Variability inherent in the measurement system under constantconditions

Reproducibility- Variability among measurements made under different conditions(e.g. different operators, measuring devices, etc.)

Stability - distribution of measurements that remains constant and predictable over time forboth the mean and standard deviation

Linearity - A measure of any change in accuracy or precision over the range of instrumentcapability


10/30slide 10

Measurement Capability Index - P/T

Precision to Tolerance Ratio

Addresses whatpercent of the tolerance is taken up bymeasurement error

Includes both repeatability and reproducibility

Operator x Unit x Trial experiment Best case: 10% Acceptable: 30%

Usually expressed

as percentP TTolerance

MS/

. *

515

Note: 5.15 standard deviat ion s acco unt s for 99% of Measurement System (MS) variat ion.

The use of 5.15 is an industr y stand ard.


11/30slide 11

Measurement Capability Index - % GR&R

Addresses whatpercent of the Observed Process Variation is

taken up by measurement error %R&R is the best estimate of the effect of measurement

systems on the validity of process improvement studies (DOE)

Includes both repeatability and reproducibility

As a target, look for %R&R < 30%

Usually expressed

as percent

100xRRVariationocessObserved

MS

Pr

&%


12/30slide 12

Objectives



Factory?




Linkage to Process


Organization


13/30slide 13

Case Study Background

Internal Raw Material, A1, is necessary for Final Product production Expensive Raw Material to produceproduced at 4 locations Worldwide

Cost savings can be derived directly from improved product quality, CpKs Internal specifications indirectly linked to financial targets for production costs are used to

calculate CpKs

If CTQ1 of A1 is too low, then more A1 material is added to achieve overall quality higherquality means less quantity is neededthis is the project objective

High Impact Six Sigma project was chartered to improve an important quality variable,CTQ1

The measurement of CTQ1 was originally not questioned, but the team decided to studythe effectiveness of this measurement The %GR&R, P/T ratio, and Bias were studied

Each of the Worldwide locations were involved in the study

Initial project improvements have somewhat equalized performance across sites. Smalllevel improvements are masked by the measurement effectiveness of CTQ1


14/30slide 14

CTQ1 MSA Study Design (Crossed)

Site 1 Lab

6 analyses/site/sample2 samples taken from each site2*4 Samples should be representativeEach site analyzes other sites sample.Each plant does 48 analyses6*8*4=196 analyses

Site 1 Sample 1 Site 1 Sample 2

Op 1 Op 2 Op 3

T1 T2

Site 2 Lab Site 3 Lab Site 4 Lab

Site 2 Sample 1..


15/30slide 15

CTQ1 MSA Study Results (Minitab Output)

0

750

800

850

900 CB1 CB2 CB3 LC1 LC2 LC3 V1 V2 V3 W1 W2 W3

Xbar Chart by Operator

SampleMean

Mean=821.3

UCL=851.5

LCL=791.1

0

0

50


R Chart by Operator

SampleRange

R=16.05

UCL=52.45

LCL=0

1 2 3 4 5 6 7 8

800

850

900

Sample

Operator*Sample Interaction

Average

CB1 CB2 CB3 LC1 LC2 LC3 V1 V2 V3 W1 W2 W3

740

790

840

890

Oper

Response By Operator

1 2 3 4 5 6 7 8

740

790

840

890

Sample

Response By Sample

%Contribution%Study Var

%Tolerance

Gage R&R Repeat Reprod Part-to-Part

0

20

40

60

80

100

120

Components of Variation

Percent


16/30slide 16

CTQ1 MSA Study Results (Minitab Session)Source DF SS MS F P

Sample 7 14221 2031.62 5.0079 0.00010

Operator 11 53474 4861.27 11.9829 0.00000

Operator*Sample 77 31238 405.68 1.4907 0.03177

Repeatability 96 26125 272.14

Total 191 125058

%Contribution

Source VarComp (of VarComp)

Total Gage R&R 617.39 90.11

Repeatability 272.14 39.72

Reproducibility 345.25 50.39

Operator 278.47 40.65

Operator*Sample 66.77 9.75

Part-To-Part 67.75 9.89

Sample, Operator,

& Interaction are

Significant


17/30slide 17

CTQ1 MSA Study Results

Site %GRR

P/T

Ratio R-bar

Equal Variances

within Groups

Mean

Differences(Tukey Comp.)

All94.3

(78.6100)*116 16.05 No (0.004) Only 1,2 No Diff.

Site 1 38.9(30.047.6)

29 7.22 Yes (0.739) All Pairs No Diff.

Site 291.0

(70.7100)96 17.92 Yes (0.735) Only 1,2 Diff.

Site 380.0

(60.894.8) 79 20.37 Yes (0.158) All Pairs No Diff.

Site 498.0

(64.8100)120 18.67 Yes (0.346) Only 2,3 No Diff.

*Conf Int not calculated with Minitab, Based upon R&R Std Dev


18/30slide 18


890

840

790

740

Site 1 Site 2 Site 3 Site 4

Dotplot o f Al l Samples over Al l Sites


19/30slide 19

CTQ1 MSA Study Results (Minitab Session)

Analysis of Variance for Site

Source DF SS MS F P

Site 3 37514 12505 26.86 0.000

Error 188 87518 466

Total 191 125032

Individual 95% CIs For Mean

Based on Pooled StDev

Level N Mean StDev -+---------+---------+---------+-----

Site 1 48 824.57 15.38 (---*---)

Site 2 48 819.42 22.11 (---*---)

Site 3 48 800.98 20.75 (---*---)

Site 4 48 840.13 26.58 (---*---)

-+---------+---------+---------+-----

Pooled StDev = 21.58 795 810 825 840

Site and Operator are closely related


20/30slide 20


X-bar R o f Al l Samples fo r Al l Sites

750

800

850


Xbar Chart by Operator

SampleMean

Mean=821.3

UCL=851.5

LCL=791.1

0

0

50


SampleRange

R=16.05

UCL=52.45

LCL=0

Most of the

samples are

seen as noise

Discrimination

Index is 0,

however can

probably see

differences of 5


21/30slide 21


Mean differences are seen in X-bar area

Most of the samples are seen as noise

800

850

900 W1 W2 W3

Xbar Chart by WO OP

SampleMean

Mean=840.1

UCL=875.2

LCL=805.0

0

0

10

20

30

40

50

60

70 W1 W2 W3

SampleRange

R=18.67

UCL=60.99

LCL=0

X-bar R o f Al l Samples for Site 4

CTQ1 MSA St d R lt P Li k


22/30slide 22

CTQ1 MSA Study Results Process LinkageSite 2 Example

780

790

800

810

820

830

840850

860 LC1 LC2 LC3

SampleMean

Mean=819.4

UCL=853.1

LCL=785.7

400300200100Subgroup 0

1000

900

800

700

IndividualValue 1

1

6

1

6

1

6

222 4

1

4

1

2

5

11 1

6

11

222

26662

2

66222

2

55

Mean=832.5

UCL=899.2

LCL=765.8

2002 Historical

Process

Results withMean = 832.5

MSA Study

Results with

Mean = 819.4

Selected Samples are Representative



23/30slide 23


0

50

100 LC1 LC2 LC3

SampleRange

R=17.92

UCL=58.54

LCL=0

150

100

50

0MovingRange 1

22

1

22222

2

1

1

1111

1

11

1

222

1

22

R=25.08

UCL=81.95

LCL=0

2002 HistoricalProcess

Results with

Range = 25.08

Calc for pt to pt

MSA Study Resultswith Range = 17.92,

Calc for Subgroup

When comparing the MSA with process operation, a large

percentage of pt-to-pt variation is MS error (70%) --- a

back check of proper test sample selection



24/30slide 24


Use Power and Sample Size Calculator with and without impact

of MS variation. Lack of clarity in process improvement work,

results in missed opportunity for improvement and continued

use of non-optimal parameters

Key issue for Process Improvement Efforts is When will we seechange? Initial Improvements to A1 process were made

Control Plan Improvements to A1 process were initiated

Site 2 Baseline Values were higher than other sites

Small step changes in mean and reduction in variation will achieve goal How can Site 2 see small, real change with a Measurement System with

70+% GR&R?

CTQ1 MSA Stud R sults Pr c ss Link


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slide 25


Simulated Reduction of Pt to Pt variation by 70% decreases

time to observe savings by over 9X.

2-Sample t Test

Alpha = 0.05 Sigma = 22.23

Sample Target Actual

Difference Size Power Power

2 2117 0.9000 0.9000

4 530 0.9000 0.9002

6 236 0.9000 0.9002

8 133 0.9000 0.9001

10 86 0.9000 0.9020

12 60 0.9000 0.9023

14 44 0.9000 0.9007

16 34 0.9000 0.9018

18 27 0.9000 0.9017

20 22 0.9000 0.9016

2-Sample t Test

Alpha = 0.05 Sigma = 6.67

Sample Target Actual

Difference Size Power Power

2 192 0.9000 0.9011

4 49 0.9000 0.9036

6 22 0.9000 0.9015

8 13 0.9000 0.9074

10 9 0.9000 0.9188

12 7 0.9000 0.9361

14 5 0.9000 0.9156

16 4 0.9000 0.9091

18 4 0.9000 0.9555

20 3 0.9000 0.9095


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slide 26


Benefits of An Improved MS

Realized Savings for a Process Improvement Effort For A1, an increase of 1 number of CTQ1 is approximately $1 per ton

Change of 10 numbers, 1000 Tons produced in 1 month (832842)

$1 * 10 * 1000 = $10,000

More trust in all laboratory numbers for CTQ1 Ability to make process changes earlier with R-bar at 6.67

Previously, it would be pointless to make any process changes within the 22 pointrange. Would you really see the change?

As the Six Sigma team pushes the CTQ1 value higher, DOEs and othertools will have greater benefit


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slide 27

Objectives



Factory?




Linkage to Process


Organization


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slide 28

Measurement Improvement in the Organization

Initial efforts for MS improvement are driven on a BB/GB project basis

Six Sigma Black Belts and Green Belts Perform MSAs during Project Work Lab Managers and Technicians are Part of Six Sigma Teams

Measurement Systems are Improved as Six Sigma Projects are Completed

Intermediate efforts have general Operations training for lab personnel,mostly laboratory management Lab efficiency and machine set-up projects are started

The %GR&R concept has not reached the technician level

Current efforts enhance technician level knowledge and dramaticallyincrease the number of MS projects

MS Task Force initiated (3 BBs lead effort) Develop Six Sigma Analytical GB training

All MS projects are chartered and reviewed; All students have a project

Division-wide database of all MS results is implemented


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slide 29

Measurement Improvement in the Organization

Develop common methodology for Analytical GB training

Six Sigma Step Action Typical Six Sigma Tools Used

Define Target measurementsystem for study

Identify KPOVs

Project Charter

Measure Identify KPIVs Evaluate KPOV

performance

Soft tools: Process Map, Cause & EffectMatrix, FMEA

Stat tools: Minitab Graphics, SPC,Capability Analysis

Analyze Measurement SystemAnalysis

Gage R&R, ANOVA, Variance Components,Regression, Graphical Interpretation

Improve Reduce Reproducibility Reduce Repeatability Reduce Operator or

Instrument Bias

Soft tools: Fishbone Diagram, FocusedFMEAStat tools: D-Study, t-Tests andRegression, Design of Experiments

Control Final Report Control Plan for KPIVs SPC, Reaction Plans, Control Plans, ISOsynergy, Mistake Proofing


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Final Thoughts

The Hidden Factory is explored throughout all Six Sigma programs

One area of the Hidden Factory in Production Environments isMeasurement Systems

Simply utilizing Operations Black Belts and Green Belts to improveMeasurement Systems on a project by project basis is not the long term

answer The GRACE Six Sigma organization is driving Measurement System

Improvement through: Tailored training to Analytical Resources

Similar Six Sigma review and project protocol

Communication to the entire organization regarding Measurement Systemperformance

As in the case study, attaching business/cost implications to poorly performingmeasurement systems

Documents

Six Sigma in Measurement Systems Evaluating the Hidden Factory (1)