outside in thinking

8/12/2019 Outside in Thinking

1/76

Black Belt Advanced

Tools/Refresher Training

Introduction to Outside/In Thinking

g G Industrial Systems


2/76


3/76

g GE Ind us t r ial Sys tems

April 1999- Adapted by Industrial Systems from Corporate Roadmap to Customer Impact course 3.

Outs ide -In Th ink ing


4/76



y1 yn

y4

y3

y2

x1x2xn

Phase 1Learning

1997

Phase 2Focus

x6x9xn

Big Y

1998

Phase 3Cluster

Big Y

xn xm

Cluster Cluster

Phase 4Correlation

1999

Y unit measure

xn xm

Y=f(x

)

From

Inside-Out

In t roduc t ion

To

Outside-In

Likely

Outcome

Learn too ls , improveys that may not impact

customer. Random,sporad ic resu l ts

Drives imp act fo rselected Y, redund ancy,

lack o f focus . May no timpact custom er Y

Drives impact for selectedY, coord inated projects

p reven ts redundancy, b igimp act on Y. May not be thecustom er Y.

Processes fo r p ro jectsare identified based on correlation

with customer Y. Successfu lp ro jects d r ive a direct impact tocustomer


5/76



Our Succ ess Star t s and End sWith The Cus to m er...

In t roduc t ion

1. Measure the same as the customer does

2. Determine your capability as the customer sees it

3. Understand the variance in the output signal

4. Find the in-process keys to impact the customer

Does o u r Y Measu rem en t Ref lec

This?


6/76



Desired Outcom es

1) Do we have the true Outside-In View?

- Will improving this Y provide direct impact at the customer?

- Will this Y measurement drive the right behaviors in Our Box? We wi l l get wh at we measure!

As the mental picture takes shape, 2 key questions to ask :

2) Do we all agree?

- The Y m easurement es tabl ishes the Miss ion fo r our Team.The Whole Team needs to o wn the Who le Problem.


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Capture the un i t & scope that real ly im pacts the cus tom er

From the Cus tomer perspective, define the transactional Uni t & Scope

at the smallest, single, product-unit or service-unit the customer needs.

Outside-In

OUTSIDE - IN

Examples ord er l ine : OTD, Ind . System : Entire System , qu ote : respon se t ime

Capture the expectat ion of the custo m er for this CTQ

From the Cus tomer perspective, define the transactional Measure

that the customer uses to gage performance on this CTQ

Examples days ear ly / la te vs reques t , weeks vs con tract , min utes

1)

2)


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Prin cip les Of Var ianc e Based Th ink ing


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Obviously We Have a Performance Problem When It Comes To Serving

The Customer...Where Do We Begin?Overall Output Signal for ALL PRODUCTS/ PLANTS

# o f

P r o

d u c

t s P

r o d u

c e

d

150

100

50

+1 Day

0 day

Mean

-2 Days Notice...On average wedo a great job

But... We fail a significant proportion of the time


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We next need to stratify the OVERALL signal by likely stratification

varibles (product line, different HP applications, )

Probably different PROCESS MAPS- and DIFFERENT CENTRAL TENDENCIES

# o

f P

r o d

u c t s P

r o d

u c e

d

150

100

50

+1 Day

0 day

Mean

-2 Days

Stratify

Switch Boards

Motor Controls

Limit Amp


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Switch Boards

Motor Controls

Limit Amp

Now Select a STRATA to work on

Motor Control was selected as itexplains more of the upper tailof the overall output signal.


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Motor Controls

Within Motor Controls start looking for Segmentation Variables

Different customer groups Week # within the QTR Different HP application

Transporation Methods Dist. Channels

Likely the Same PROCESS MAP and CENTRAL TENDENCIES-BUT DIFFERENT VARIANCES


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By Customer Type May Explain theRadical Differences in Variation

Industrial Commercial Utility (Different levels of variation)

The Goal Is To Stay OUTSIDE YOUR BOX As Long As PossibleTo Ensure Linkage To The Customer Y

Motor Controls

Within Motor Controls start looking for Segmentation Variables

Similar Central Tendency


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Could These Really Be Two Different Processes? (Or at least one process

behaving two different ways? With Stability And Without?)

CAUTION: THE UNSTAB LE PROCESS MAY NOT BE NORMA LLY DIST. OR DISTINGUISHABL EAS A SE PARATE DISTRIBUTION

150

100

50

Outliers werepart of a biggerDistribution

Deviations For Unstable-Unpredictable Process-ProcessNot Well Behaved

0 Day

Mean (USL)(LSL)

Deviations For Well Behaved -Predictable

Process


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150

100

50


Distribution of Time For Unstable-Unpredictable Process-ProcessNot Well Behaved

Distribution of Time s For Well Behaved -PredictableProcess

If This is Truly The Case, Average-Based Measurements Will Mis-Lead You:

1.) The average doesnt reflect the central tendency of either distribution

2.) On Average you meeting the customer need, but in actuality, your failing asubstantial percentage of the time

0 Days

Mean


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After Segmentation...Apply the Six Sigma Methodology to find

and Fix the Xs responsible for the Unacceptable Levels of Variation

At this point we are strictly dealing with Labels -- NOT Xs We MUST Identify the REAL Xs

M P G

Age40

Label= Age of Driver

M P G

True X = Driving Style

Aggressive Conservative


17/76



The Flow...

Define Big Y Stratify Segment Drill Down ImproveArrive on time, alive 9 Businesses 6 Customer TypesRequest Met/ 20 Locations 30 Product Types Delivered

DMAICStay Outside The Box

Y = y (X 1, X 2, X 3, X 4...X n)


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150

100

50


Distribution of Time For Unstable-Unpredictable Process-ProcessNot Well Behaved

Distribution of Time s For Well Behaved -PredictableProcess

Given Our Knowledge Of Process Behaviors From Six Sigma Class, We Know:

1.) The Unstable distribution has very special (assignable causes) associated with it. We need to find and eliminate them using appropriate tools

2.) The stable distribution usually consists of Xs behaving predictably,

therefore creating consistent output.3.) In order to reduce variation, we should focus on the identifiable and

manageable Xs first -In other words the UNSTABLE DISTRIBUTION

4.) We need measurements that reflect variability in performance which are

covered in module 2


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A verag e Vs . VarianceB ased Measu rem en ts


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17426158

79325711842484958628658467686104295945694767

5666552543

53

Jan

Feb

Mar

OrderFulf i l lment-Bui ld

to Order Motors(days)

Average

CUSTOMERS VIEW

0 25 50 75 100 125

Min = 17Max = 118

GEs VIEW

0 25 50 75 100 125

53

CA PTURE WHAT THE CUSTOMER SEES

- THE ENTIRE DISTRIB UTION OF Y VA L UES

Last years average

17426158

79325711842484958628658467686104295945694767

5666552543

53


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Measurements Drive Behavior :

Jan

Feb

Mar

Average

CUSTOMERS VIEW

0 25 50 75 100 125

Min = 17Max = 118

GEs VIEW

0 25 50 75 100 125

53

Likely Learning/Behavior

Likely Learning/Behavior

I n s

i d e

G E

C u s

t o m e r

S i t e

GE has a rang e of 101 Days We MUST plan for wo rst case I f on ly they could reduce

variation! Do they know what we REALLY

care about as a cus tomer?

We beat last years num ber The custom er must real ly be

reaping the benef i t o f our w ork

Maybe we can use this datato enhance our re lat ionship We should keep th is k ind of

act iv i ty up Why doesnt the customer tell

us of the great job we are do ing?

Last years average

Why Dont They Match?

17426158793257118424849586286584676861042959456947

675666552543

53


22/76



Typ es o f Pro cess Measu rem en ts :

A ttr ib u te Vs. Variab le


23/76



Attr ibu te Measurem ents :

Using An At t r ibu te Based Measurem entIs L ike Trying To Co ntrol Your SteeringBy Count ing The Number o f Times YouHit The Guard Rail

Variables Measurem ents:

Using Variables MeasurementsWill Give You Direction andDeviation From Your IntendedPath


24/76



Attr ibu te Measurements : Variables Measurem ents:

Using an a t t r ibu te Measurementis a lso l ike t ry ing to d iagnos e aau tomob i le p rob lem w i th theCHECK ENGINE l igh t

Service Engine Now

Unless youre lucky, you really dont know where to start looking for the root cause. YouAlso need to worry about false positives,

or silent positives

Using a Variables Measurement g ivesyou ins igh t in to the exac t locat ionand v ar iat ion of each o f the cr i t icalparam eters m easured.

Using a variables gage, youllimmediately know where to lookbased on atypical behaviorof the gage


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The Real Po w er o f Variable Data...

150

100

50

30 Days

Term= 30 days

Days to Collect Receivables

This distribution paid on time, a few were lateprobably due to the delivery system (Physical Mail)

This distribution NEVER intended topay on time

Variables data allows you to see the differences in behavior across differentXs (the late distribution may be high credit risk, or a certain customer type)


26/76



150

100

50

Term= 30 days

Days to Collect Receivables

This distribution paid on time, a few were lateprobably due to the delivery system (Physical Mail)

This distribution NEVER intended topay on time

30 Days

Failure=Late PaymentAcceptable=On timeor early

Attribute data only creates two categories, , . All failuresare viewed the same, all acceptable events are viewed the same.

Attr ib ute Data Takes A way Cri t ical Inform at ion:

ALL FAILURES ARE VIEWED EQUALLYALL SUCCESSES ARE VIEWED EQUALLY


27/76



Outs ide - In

The GOOD Days early/ late per orderline

against requested day.

Hrs availability per locomotive.

Actual cycle time versuscontract cycle time

Quotation response time inminutes

Nielsen rating vs predictedrating per program

... and the BAD

% misshipments per week.

Number of locomotive failuresper quarter.

% cycles completed in-time

Responses made per hour

Top 5 Nielsen ratings per week

Note : Select time period over which the data is to be collected.Select sample size for this period.


28/76



Variables data is ALWAYS preferred

In Fact...REQUIRED FOR STABLE OPs


29/76



Th e B read th Of Th eMeasurement


30/76



1) GE Fully Met Its Contractural Obligations (AB)

2) Customers view determined by their process performance (AC)

Defining th e Breadth of You r Y Measurem ent

Customer

Process

GE Process

CA B

GEs View of ItsContribution

CustomerView of GEsContribution

If we cut ou r process cy cle t imewo uld the cus tom er fee l it ?


31/76



CustomerProcess

GE Process

CA B

GEs View of ItsContribution

Customer View ofGEs Contribution

I f we cut our proc ess cycle t imewould the cus tomer feel i t?

Industrial Systems Example...

If A Customer Ordered an Industrial Motor, A Drive Package, and

Labor To Install/Start Up. What Would Be The AppropriateMeasurement Breadth? The Motor, The Drive Package, The Labor,Or The Entire Start Up?


32/76



Customer Delight

GE process level

A little more of the customers view

C U S T O M E R

S A

T I S F A C T I O N L E V E L

What Iam

Brings a Lot More Satisfaction

....but Sensitive to Variance

Plateau Landscape

Custo mer Sat is fact ion


33/76



Customer Delight

GE process level

C U S T O M E R

S A


What Iam We need to capture far more of the customers view

to Get the Same Level of Satisfaction

Mountain Landscape



34/76



GE process level

C U S T O M E R

S A


What Iam


Flat

No Need to include more of the customers process

Customer Delight

Desert Landscape

d l


35/76




Do you u nders tand the Cus tom er Landsc ape?

Whats the potential to impact the Customer?

Plateau Landscape

MountainLandscape

Desert Landscape

InterpretationSmall change in breadth, much more

customers process

Requires a larger change in breadthto pick up customer process

No matter how broad you measure, youdo not influence the customer success

GEI d i lS


36/76



Does Yo u r MEA SUREMENT Of

The Ou tp u t Sign al (Y) MatchTh e Custo m ers View ?

GEI d i lS


37/76



The Measurement System will transmit variation to our data.

+ Actual(Part)2

=

Output Variability(Actual variability)

Meas.System2

MeasurementVariability

Total Variability(Observed variability)

ProcessInputs OutputsMeasurement

ProcessInputs Outputs

Observations Measurements Data

Documents

(Example)

Good EnoughTo Monitor Process

MeasurementVariation W ILLDrive Decis ion Errors

GEI d i lS


38/76



The Measurement System Can Shift the Centering of Our Data

ProcessInputs Outputs MeasurementProcessInputs Outputs

Observations Measurements Data

Actual(Part) Meas.System+ =

Avg

Avg Avg

Ex:Your Weight

Ex:Bathroom Scale

Adjust Down By 2 lbs

Ex:What You See

To Fix Calibrat ion ,You Must Have Operat ionalDefin i t ions

GEI d t i lS t


39/76



Avg

Avg

Avg

Ex:

Your Weight

Ex:Bathroom Scale

Adjust Down By 2 lbs

Ex:What You See

To Fix Calibrat ion ,You Must HaveOperat ional

Defin i t ions

Industrial Systems Example:

Urgent Order = Pad By 1 WeekCritical Order = Pad By 2 WeeksVery Critical Order = Pad by 3 Weeks

What About Reproducibility Between Sales People?

GEI d t i lS t


40/76



Actual Delivery- Tuesday

Measured Delivery- Thursday

Max Range of Measurement Error

Delivered on -tim e - Cus tom er i s happyMeasu red as bein g late - We take act ion to co rrect

- 1 Day + 1 Day (Customer Specs)

On-Time

CYCLE TIME vs Requ est

GEI d t i lS t


41/76




On-Time

CYCLE TIME vs Request

Actual Delivery- Tuesday

Measured Delivery

- Thursday

Max Range of Measurement Error

Deliv ered early - Cus tom er i s unhappy Measured as on -t im e - Great Jo b!

GEI d t i lS t


42/76




On-Time

CYCLE TIME vs Request

Measured Delivery- Tuesday 10:00am

MRME

Actual Delivery- Tuesday 14:00am

Close Enough to Understand Sources of Variation In Your Process

GEInd us t r ialSys tems


43/76



Your Evaluation of DeliveryMeasurement

On TimeDelivery

TheTruth

On TimeDelivery

Late Delivery

Type IError

a -Risk)

Type II Error

b -Risk)

Correct

Correct

Late Delivery

Consequences: Your customer observed a late delivery

and you IGNORE IT

Consequences:

You waste resourceslooking

for a non-existentFailure



44/76



X: Actual Delivery

Target: Promise Datex

x

x

x

x

xx

x

x

xx

x x

xxx xxxx xx

xxxxxxxx xx

xx

x

x

xx

x

x

x

x

Consis ten t lyMeasur ing w ithA Bias

Solution : CalibrateYour Measurement With The Custom er(Hint: Operational Definition)

Incons is ten t andBiased Measurements

Solution : Calibrate First (see above)Find source of var ia t ion through MSA

Inconsis ten t Measurements

Solut ion:Find source of var ia t ion through MSA

Together Accuracy & Variation Issues Prevent You From Measuring Your Process



45/76



Measurment System Analysis Issuesfor

Variables Data (Continuous Data)

(How can I tell whether I have too much

rounding in my data?)

Sc ale o f Sc ru t iny



46/76



Two Big Scale Of Scrutiny (Measurement Units) Issues:

1.) Prevent you from seeing the REAL variation in the process (Big Y), and willmake it difficult(if not impossible) to find the X-Y relationships.

2.) Mask smaller but potentially important process changes

3.) Will not allow you adequate reaction time to prevent process failures. You will know only slightly before (or sometimes after) the customer knowsyouve failed.

Inadequate Scale of Scrutiny WILL:



47/76



Scale of Scrut iny

Scale of Scrutiny

10 Tablets

Acetam inophen con tent

Target 2000 mgActual 2005 mg

Almost no variance



48/76



Scale of Scrutiny

1 Tablet


Target 200 mgActual 201 mg

Still no variance

Scale of Scrut iny



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Scale of Scrutiny

Tablet


Expected 100 mgActual 156 mg

Large Variance- Could be dangerous!

Scale of Scrut iny

g GEInd us t r ialSys tems


50/76



Scale of Scrutiny

The Sm aller the Scale of Scr ut in y

The Larg er the % Varianc e

Decide the Scale of Scrutiny that the Customer uses

What is your internal scale of scrutiny to control the output?

Scale of Scrut iny



51/76



Inadequate Measurement Units

Based on Evaluating the Measurement Process by Wheeler & Lyday, 1984

Will Eliminate The TRUE Variation FromShowingUp in Your Measurement of The Big Y

Therefore Prevent You From Reducing It



52/76



What do these charts tell us about our process?

0.136

0.138

0.140

0.142

0.144

5 10 15 20 25

Subgroup #

Avg=0.1403

LCL=0.1375

UCL=0.1431

0.000

0.005

0.010

0.015

0.020

5 10 15 20 25

Subgroup#

Avg=0.0048

LCL=0.0000

UCL=0.0102

M e a n o

f T h i c k n e s s

R a n g e o f

T h i c k n e s s

X-barChart

RangeChart




53/76



Inadequate Measurements Units

Measurement units which are too large toproperly reflect the variation present.A type of inadequate discrimination due to

excessive round -off of measurements oran inappropriately designed measurementsystem

Definition:




54/76



About Inadequate Measurement Units:

One of the simplest measurement system problemsProblem is fairly widespread, but impact is rarelyrecognized.Easily detected by ordinary control charts for processor product measurements.No special studies are necessaryNo known standards are needed.

Example: The data in the following table are thethickness measurements of a plastic plate. The dataare recorded in inches, but the smallest measurementunit is one / one-thousandth of an inch (0.001 in.).




55/76



Sub-group

Part 1 Part 2 Part 3 Part 4 Part 5 X-bar (Avg)

Range Sub-group


Range

1 0.140 0.143 0.137 0.134 0.135 0.1378 0.009 15 0.144 0.142 0.143 0.135 0.144 0.1416 0.0092 0.138 0.143 0.143 0.145 0.146 0.1430 0.008 16 0.133 0.132 0.144 0.145 0.141 0.1390 0.013

3 0.139 0.133 0.147 0.148 0.149 0.1432 0.016 17 0.137 0.137 0.142 0.143 0.141 0.1400 0.0064 0.143 0.141 0.137 0.138 0.140 0.1398 0.006 18 0.137 0.142 0.142 0.145 0.143 0.1418 0.0085 0.142 0.142 0.145 0.135 0.136 0.1400 0.010 19 0.142 0.142 0.143 0.140 0.135 0.1404 0.0086 0.136 0.144 0.143 0.136 0.137 0.1392 0.008 20 0.136 0.142 0.140 0.139 0.137 0.1388 0.0067 0.142 0.147 0.137 0.142 0.138 0.1412 0.010 21 0.142 0.144 0.140 0.138 0.143 0.1414 0.0068 0.143 0.137 0.145 0.137 0.138 0.1400 0.008 22 0.139 0.146 0.143 0.140 0.139 0.1414 0.0079 0.141 0.142 0.147 0.140 0.140 0.1420 0.007 23 0.140 0.145 0.142 0.139 0.137 0.1406 0.008

10 0.142 0.137 0.134 0.140 0.132 0.1370 0.010 24 0.134 0.147 0.143 0.141 0.142 0.1414 0.01311 0.137 0.147 0.142 0.137 0.135 0.1396 0.012 25 0.138 0.145 0.141 0.137 0.141 0.1404 0.00812 0.137 0.146 0.142 0.142 0.146 0.1426 0.009 26 0.140 0.145 0.143 0.144 0.138 0.1420 0.00713 0.142 0.142 0.139 0.141 0.142 0.1412 0.003 27 0.145 0.145 0.137 0.138 0.140 0.1410 0.00814 0.137 0.145 0.144 0.137 0.140 0.1406 0.008 . . . . . . . .

0.134

0.136

0.138

0.140

0.142

0.144

0.146

5 10 15 20 25Subgroup#

Avg=0.1406

LCL=0.1357

UCL=0.1456

0.000

0.005

0.010

0.015

0.020

5 10 15 20 25Subgroup#

Avg=0.0086

LCL=0.0000

UCL=0.0181

M e a n o

f T h i c k n e s s

R a n g e o

f T h i c k n e s s

Smallest Measurement Unit = 0.001 inch Y = Plate Thickness

Neither the X-bar Chart norRange Chart show anyindications of lack of control.

The underlying physical

process appears quite stableand predictable.

Derived from Evaluating The Measurment Process by Wheeler and Lyday

(1989)

Based on Evaluating the Measurement Process by Wheeler & Lyday,1984



56/76



Sub-group


Range Sub-group


Range

1 0.14 0.14 0.14 0.13 0.14 0.138 0.01 15 0.14 0.14 0.14 0.14 0.14 0.140 0.002 0.14 0.14 0.14 0.14 0.15 0.142 0.01 16 0.13 0.13 0.14 0.14 0.14 0.136 0.01

3 0.14 0.13 0.15 0.15 0.15 0.144 0.02 17 0.14 0.14 0.14 0.14 0.14 0.140 0.004 0.14 0.14 0.14 0.14 0.14 0.140 0.00 18 0.14 0.14 0.14 0.14 0.14 0.140 0.005 0.14 0.14 0.14 0.14 0.14 0.140 0.00 19 0.14 0.14 0.14 0.14 0.14 0.140 0.006 0.14 0.14 0.14 0.14 0.14 0.140 0.00 20 0.14 0.14 0.14 0.14 0.14 0.140 0.007 0.14 0.15 0.14 0.14 0.14 0.142 0.01 21 0.14 0.14 0.14 0.14 0.14 0.140 0.008 0.14 0.14 0.14 0.14 0.14 0.140 0.00 22 0.14 0.15 0.14 0.14 0.14 0.142 0.019 0.14 0.14 0.15 0.14 0.14 0.142 0.01 23 0.14 0.14 0.14 0.14 0.14 0.140 0.00

10 0.14 0.14 0.13 0.14 0.13 0.136 0.01 24 0.13 0.15 0.14 0.14 0.14 0.140 0.0211 0.14 0.15 0.14 0.14 0.14 0.142 0.01 25 0.14 0.14 0.14 0.14 0.14 0.140 0.0012 0.14 0.15 0.14 0.14 0.15 0.144 0.01 26 0.14 0.14 0.14 0.14 0.14 0.140 0.0013 0.14 0.14 0.14 0.14 0.14 0.140 0.00 27 0.14 0.14 0.14 0.14 0.14 0.140 0.0014 0.14 0.14 0.14 0.14 0.14 0.140 0.00 . . . . . . . .

Smallest Measurement Unit = 0.01 inch Y = Plate Thickness

0.136

0.138

0.140

0.142

0.144

5 10 15 20 25Subgroup#

Avg=0.1403

LCL=0.1375

UCL=0.1431

0.000

0.005

0.0100.015

0.020

5 10 15 20 25Subgroup#

Avg=0.0048LCL=0.0000

UCL=0.0102

M e a n o

f T h i c k n e s s

R a n g e o

f T h i c k n e s s

The data in this table werederived from the previousdata table by rounding offeach value to the nearestone/one-hundredth of an

inch (0.01 in.). Values ending in 5 wererounded to the nearest evenmultiple of 0.01. Then, the subgroupaverages and ranges wererecalculated .

Derived from Evaluating The Measurment Process by Wheeler and Lyday (1989)Based on Evaluating the Measurement Process by Wheeler & Lyday, 1984



57/76



Consequences of Inadequate Measurement UnitsBoth the X-bar Chart and

Range Chart for the roundeddata show points outside thecontrol limits, although theunderlying physical processis quite stable!Information lost in the round-off caused the followingdistortions:

Deflated Avg RangeDeflated estimate of within-subgroup Std.Dev.Other statistics involving thiswithin-subgroup variation

estimate are suspect.Range Chart limits too tightX-bar Chart limits too tightGreater discreteness for both theaverage and range values,spreading-out the plotted points.

0.136

0.138

0.140

0.142

0.144

5 10 15 20 25

Subgroup#

Avg=0.1403

LCL=0.1375

UCL=0.1431

0.000

0.005

0.010

0.015

0.020

5 10 15 20 25

Subgroup#

Avg=0.0048

LCL=0.0000

UCL=0.0102

M e a n o

f T h i c k n e s s

R a n g e o

f T h i c k n e s s




58/76



In the lower RangeChart, we can easily

see that there are onlytwo possible values forthe range within thecontrol limits.This suggests that ourmeasurement units aretoo large to properlyreflect the within-subgroup variation.Information aboutdispersion is lost in

the round -off. Other statisticsinvolving this within-subgroup variationestimate are suspect.

0.000

0.005

0.010

0.015

0.020

5 10 15 20 25Subgroup #

Avg=0.0086

LCL=0.0000

UCL=0.0181

0.000

0.005

0.010

0.015

0.020

5 10 15 20 25

Subgroup #

Avg=0.0048LCL=0.0000

UCL=0.0102

Range Chart for Thickness Measurements to 0.001 in.

Range Chart for Thickness Measurements to 0.01 in.Based on Evaluating the Measurement Process by Wheeler & Lyday, 1984

Visual Detection of Inadequate Measurement Units



59/76



Rules for Detecting Inadequate Measurement UnitsThe measurement unit borders on being too large when

there are only 5 possible values within the control limits onthe Range Chart.4 values within the limits will be evidence of Inadequate MeasurementUnits1, 2, or 3 possible values will result in substantial distortion.

The only exception to this occurs when the Subgroup Sizefor the Range Chart is n = 2.

3 possible values within the limits will be evidence of InadequateMeasurement Units.1 or 2 possible values will result in substantial distortion

Also, beware if more than 25% of the ranges are zero.

In other words, inadequate discrimination due tomeasurement units which are too large begins to affectstatistical analyses when the Measurement Unit is greaterthan the Standard Deviation of the process (behavior) weintend to study.

Derived from Evaluating The Measurment Process by Wheeler and Lyday (1989)Based on Evaluating the Measurement Process by Wheeler & Lyday, 1984



60/76

g y


What Can Be Done About Inadequate Measurement Units?Measure and report to as many decimal places as themeasurement device permits.

Sometimes the measurement unit will be too large simply because thesomeone truncated to a certain level in order to avoid (they believe)reporting noise.This may actually be cutting off part of the signal! Recording one extradigit will usually be enough to eliminate this source of inadequatediscrimination.

Seek a measurement device that can measure smaller units. If there is nothing else can be done right away, you may haveto live with it, for now. Document that the problem exists.Priorities may need to involve other considerations:

Is this a study of the total process variation or a special study of a sub-component of the process variation (where we might be trying to detectsmaller differences, shifts, or variation)?What is engineering tolerance? Process Capability?Cost and difficulty in replacing device?


g GE Ind us t r ial Sys tems C i h M


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Captu r ing th e Measurement

Outside-In Process Scope Broad enough customer-process view( Repair time - TAT - Wing To Wing )

Execution Scope Have you captured the whole customer expectation( request vs promise/negotiated vs standard )

Scale of Scrutiny Unit What is the Granularity the customer looks at?e.g. Forecast Order vs Specific order vsSpecific order-line vs Items in order-line

Measure At what level does the customer see differences?e.g. for Time: weeks, days, hrs, or minutes

GR & R Make sure that what youre measuring is real

Skills for 6 Leaders - You Must Be Able to Do This

CHECK LIST FOR MEASURING THE Y



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Hints on Measu rem ent er rors : 1) Its always a bigger deal than you think -ALWAYS!

2) Scale of scrutiny is key - if 1 day is important then measure in hours

3) Aim at 10% of customer window as max allowable measurement error

(probability of mis-classification, or % contribution to variation)

Remember the Measurement System Issues:

Accuracy & Precision

Scale of Scrutiny/Inadequate Measurement Units

Excessive Variablility

Operational Definitions that Match the Customers



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If Yo u Have A n In adequ ate

Measu rem ent Sys tem ...

STOP!

YOU MUST FIX THE MEA SUREMENTSYSTEMS FIRST

A l l You r A ct iv i ty i s a t r i sk o fb ein g B ENIGN

(For Method s to Redu ce Measurem ent Errors-See Chpt . 9 in The Black B elt orGreen Belt Material Or See A BB or MB B.



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Breakout:

M easurement Systems Validation



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The Scenario:

The business has decided to initiate a variation reduction projectfor RQ (requests kept) for small AC drives sold throughdistributors.

You begin by validating the measurement system

Select the largest distributor, also has the most disagreement and performance problems with Data is collected at both the distributor site as well as in your

factories Given your Six Sigma training on MSA, plus the new insight

obtained through module 1-Variation Based Thinking Course,determine of the following measurements are acceptable to usefor analysis



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File: VBT_MSA.mtw

Variables:Delivery ID: Delivery Sequence NumberDist_Dev_FR_REQ: Distributor Meas. of Deviation from RequestGE_Dev_FR_REQ : GEs internal measure of Deviation from Request

Breakout Questions: Do the numbers agree? If not: Is there a difference in the variation (precision),

or the mean (accuracy)? If a difference exists, what could be causing such a difference? Is this data good enough to begin an analysis? What would be your next step?



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INSTRUCTOR PAGE:

Since these data are measurements of the same delivery events, andthe data are paired, we can quickly check for disagreement:

Step 1: Do the overall distributions match?

Stat>Basic Stats>Descriptive Stats



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INSTRUCTOR PAGE:



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GEs Measurements Distributors Measurements

Centering is about the same 1

37.525.012.50.0-12.5-25.0

95% Confidence Interval for M u

321

95% Confidence Interval for Median

Variable: GE_Devia

1.0000

11.5437

0.5408

Maximum3rd QuartileMedian1st QuartileMinimum

NKurtosisSkewnessVarianceStDevMean

P-Value: A-Squared:

3.0000

13.0704

2.6952

40.0000 10.0000 2.0000 -6.0000

-29.0000

5007.52E-029.56E-02

150.29312.2594 1.6180

0.2950.437


95% Confidence Interval for Sigma

95% Confidence Interval for Mu

Anderson-Darling Normality Test

Descriptive Statistics

1383-2-7-12


210


Variable: Dist_Dev

0.0000

5.4405

0.4803



P-Value: A-Squared:

2.0000

6.1600

1.4957

17.0000 5.0000 1.0000 -3.0000

-14.0000

500-3.5E-011.85E-0233.38265.777770.98800

0.0031.229






1: To test for difference use a t-test or ANOVA 2: Could validate with CI of SIGMA or Homogeneity of Variance test

Distributor measurements have about 2Xs the variability 2

Variable N Mean Median Tr Mean StDev SE MeanGE_Dev_F 500 0.988 1.000 0.991 5.778 0.258Dist_Dev 500 1.618 2.000 1.520 12.259 0.548

Variable Min Max Q1 Q3GE_Dev_F -14.000 17.000 -3.000 5.000Dist_Dev -29.000 40.000 -6.000 10.000



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From our simple analysis, a couple of things are evident:

On average we are serving the distributor well Our measurement systems agree-ON AVERAGE There is significantly more variation in GEs measurements of the

delivery events (same events-Why?). Differences: More variation showing up in difference between Q3 and Q1 Confidence intervals of the Standard Deviations Graphically (Interrocular test)

What could cause this kind of diffence? Operation definitions (How do we define the request date?) Variation in how the date is logged? Data handling/integrity errors? The feedback loop (how the date gets back to GE)

INSTRUCTOR PAGE:



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Since we have paired data, we could get much more insight intowhere the variation might be coming from (paired t-test, Controlcharts for the delta (GE-Distributor date), Multi- vari by Xs to findsources of variation. BUT:

Usually this kind of problem is easy to fix-

Make sure you have defined measurement points Look for hand off errors (red flag conditions)

Poor data handling procedures Calibration with the customers measurement Sometimes youll have to get inside the process to find the person, type of job, or period (Xs) that are causing the issue, but generally it is system wide.

INSTRUCTOR PAGE:



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The Scenario:

The business has decided to initiate a variation reduction projectfor RQ (requests kept) for OEMs for small motors.

You begin by validating the measurement system Data is collected at both the OEM site as well as in your

factories Given your Six Sigma training on MSA, plus the new insightobtained through modeule 1-Variation Based Thinking Course,determine of the following measurements are accepatble to usefor analysis



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File: VBT_MSA.mtw

Variables:Delivery ID: Delivery Sequence NumberOEM : OEM Meas. of Deviation from RequestGE: GEs internal measure of Deviation from Request

Breakout Questions: Do the numbers agree? If not: Is there a difference in the variation (precision),

or the mean (accuracy)?

Is there a difference in the shape of the distributions? Are they normal? If a difference exists, what could be causing such a difference? Is this data good enough to begin an analysis? What would be your next step?



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12.510.58.56.54.52.50.5

95% Confidence Interval for M u

1.00.50.0


Variable: OEM

0.0000

1.5528

0.8451



P-Value: A-Squared:

0.0000

1.7581

1.1349

13.0000 1.0000 0.0000 0.0000 0.0000

50012.00242.873902.719341.649040.99000

0.00057.446






5.54.02.51.0-0.5-2.0-3.5


-2.35-2.45-2.55-2.65-2.75-2.85-2.95-3.05


Variable: GE

-3.00000

1.60442

-2.67371



P-Value: A-Squared:

-3.00000

1.81661

-2.37429

6.00000-2.00000-3.00000-4.00000-4.00000

5006.538232.313962.90323

1.70389-2.52400

0.00040.648






Variable N Mean Median Tr Mean StDev SE MeanOEM 500 0.9900 0.0000 0.7578 1.6490 0.0737GE 500 -2.5240 -3.0000 -2.7556 1.7039 0.0762

Variable Min Max Q1 Q3OEM 0.0000 13.0000 0.0000 1.0000GE -4.0000 6.0000 -4.0000 -2.0000

Measureable difference in mean, but compariable Std. Dev.s



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Since we have paired data, we could get much more insight intowhere the variation might be coming from (paired t-test, Controlcharts for the delta (GE-Distributor date), Multi- vari by Xs to findsources of variation. BUT:

Usually this kind of problem is easy to fix-

There seems to be almost a two day difference in our measurement Vs.the OEMs

You should begin by comparing measurement point Make sure the Start/Stop points are the same Poor handling procedures Calibration with the customers measurement

INSTRUCTOR PAGE:



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Ag enda-Modu le 1

Unders tandin g Outs ide-In Thinking and Variat ion Reduct ion -Sect ion 1 Outside In Determining the REAL customer Y What is Variance Based Thinking

Identifying Customer CTQs & Measurements -Big Y thou gh t process-Sect ion2

The Customer view Problems with Existing Measurements Average Vs. Variance Based Metrics Breadth of Measurements-Customer Impact Adopting the customers measurement of your success

Measurement Systems for Variance Based Ys -Sect ion 3 Traditional problems with Measurement Systems Correlating your process signal to the customer Y The impact of a bad measurement system Attribute Vs. Variable Measurement Scale of Scrutiny/Inadequate Measurement Units