Black Belt Refresher

1© 2001 ConceptFlow

Black Belt Refresher


SPACER

• Safety• Purpose• Agenda• Code of Conduct• Expectations• Roles


Safety

• Drills• Issues• • • • • • Emotional


Purpose

• Education: Review of the methodology and tools learned in Black Belt training

• Practical Exercises: Provide a refresher for use and interpretation of the tools


Agenda

Roadmap of the Phases

The 12 Step Process

Supporting tools


Administrivia

• Breaks• Lunch• Pagers, Blackberrys & Cell Phones• Facilities• • •


Code of Conduct

• Stay focused• Openness and trust • One person speaks at a time• No whining• Breaks as needed• Start and stop on time• Enjoy learning• Your turn....•


Your Expectations?


Roles

• Black Belts • Facilitator• Others


The Breakthrough Strategy


Module Objectives

By the end of this module, the participant will:• Receive a refresher on the 12 Steps of the Breakthrough

Strategy• Understand how the 12 Steps of the Breakthrough

Strategy provide the backbone for six sigma DMAIC projects

• Know the linkage of six sigma tools to the Breakthrough Strategy

• Understand the linkages of tools to each other


Goals Of Six Sigma

Center Process on TargetReduce Variation

Client target

Meet Client Target and Specifications

Client target

Eliminate DefectsReduce Variation

Client target

Defects Defects

GOAL

USLLSL

LSLLSL USL USL


The Breakthrough Strategy

Characterization

Optimization

Recognize PhaseDefine PhaseMeasure Phase Analyze Phase

Improve PhaseControl PhaseIntegrate PhaseSustain Phase

1 Select CTQ characteristic2 Define performance standards3 Validate measurement system

4 Establish product capability5 Define performance objectives6 Identify variation sources

7 Screen potential causes8 Discover variable relationship9 Establish operating tolerances

10 Validate measurement system11 Determine process capability12 Implement process controls

Purpose Phase Step


Benefits of Following the 12 Steps

• Logical and proven method for achieving success• “Would you want to give a baseline DPMO (step 4) if you did not

trust your data (step 3)?”

• “Would you put 6 months into fixing a client’s problem and then not

bother to control it to assure ongoing value (step 12)?”

• Provides a roadmap for BB activities

• Prevents “jumping to conclusions” and “firefighting”

• Forces data to be evaluated before used

• Provides a framework upon for analytical and statistical

tools


Practical Problem Statistical Problem

Statistical SolutionPractical Solution

Overall Problem Solving Approach

Measure Analyze

ImproveControl

y f x x x k ( , , . . . , )1 2


Optimized Process

10-15 Xs

8-10 KPIVs

4-8 Key KPIVs

Dynamics Of Execution Strategy - The Funnel Effect

3-6 Key KPIVs

30-50 Inputs (X)Define Phase

Measure Phase

Analyze Phase

Improve Phase

Control Phase


Road Maps

© 2001 ConceptFlow

Define the Project, the Customers and the ‘Big ‘Y’?

Breakthrough Strategy (DMAIC)

Focus

Vital Few x i

y

y

y

y

y

x1, x2, ... xn

x1, x2, ... xn

Vital Few x i

Vital Few x i

Vital Few x i

Vital Few x i

Ph

as

e

M easure

A nalyze

I mprove

C ontrol

Select Product or Process Key Characteristic(s); e.g..., ’Little’ y

Define Performance Standards For y

Validate Measurement System for y

Establish Process Capability of Creating y

Define Improvement Objectives For y

Identify Variation Sources In y

Screen Potential Causes For Change In y & Identify Vital Few xi

Discover Variable Relationships Between Vital Few xi

Establish Operating Tolerances On Vital Few xi

Validate Measurement System For xi

Determine Ability To Control Vital Few xi

Implement Process Control System On Vital Few xi

D efine Y

Step

0

1

2

3

4

5

6

7

8

9

10

11

12

Colors reflect training week


Define / Measure

Wk 11.

Select CTQ Characteristics

2.Define

Performance Standards

Phase Gate

Reviews

Customer(s)Customer Requirements (VOC)

Big Picture Big "Y"CT Trees - CTC - CTQ - CTDLittle "y"

Project Metrics for Little "y" or y'sPerformance StandardsGap Analysis

Availability of ResourcesBB/Team Training NeedsBarriersScheduleBudget

Metric DefinitionScopeCTSsHigh-Level Process Map (SIPOC)Process Knowledge

Process OwnerProject TeamChampion ApprovalBBMBB ApprovalEstablish Business Support (Viability)Project TrackingDocumentation

Create Project

Definition

3.Validate

Measurement System

KPOVs (y's)Validate Performance DataCapable Measurement System on y'sData Collection / Sampling Plan

Process MapOperational Definition(s)Measurement System EvaluationCalibration

Process Owner/ StakeholdersBusiness ObjectivesGap AnalysisScope (initial)Communication w/ BB, MBB, Champion

Identify the 'y' or 'y's' as the focus of your project (KPOV)Problem StatementGoals, ObjectivesCharterProject Schedule, Delivery Milestones, Resources (Project Plan)Team Members, Process OwnerBusiness Impact


Define/Measure Tool Linkage

S I P O C

Requirements Requirements

Suppliers Inputs Outputs ClientsInput Boundary Output Boundary

Process

Complete Travel

Authorization

Contact Agency

Traveler Information Verification

Gather Customer Billing Info

Auth. Code?

Authorized TravelAuth. Code

ŸŸ

Authorization Form, S

DomesticInternationalTravelerApprover

Website, STravel Budget CCost Estimate SComputer Systems,NLocation Variation,N

Ÿ

ŸŸŸŸ

ŸŸŸŸ

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Contact Service, SPhone/Web

Travel Information,STraveler/Designee, N Agency Resources

day time, SNafter hours, SN

Computer Systems, NMeasurement Systems, STime In Queue

Time in VRUCall VolumePeak Hours

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ŸŸŸ

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ŸŸ

ŸŸŸŸ

Agency Request Entered

Ÿ Correct Traveler Information

Ÿ

Traveler Info, SCompany Info, SRestrictions if applicable, SPreferences, S (seat, class)

Agent NTraveler NComputer Systems, NMeasurement Systems, S

ŸŸŸŸŸŸŸŸ

Client name, SLocation, SAuth. Code, SOperator, SAgent NTraveler NComputer Systems, N

Measurement Systems, N

ŸŸŸŸŸŸŸŸ

Correct Traveler Billing

Info Into System

Ÿ

Y

NNeed For

Travel

Rationale, NViable Alternatives, N

Traveler NDestination Customer,N

ŸŸŸŸ

Complete Travel Request

Form

Request Form, SCharge No., STraveler NLocation Variation, NComputer Systems, NOther Personnel?Form Procedure

STravel Information N

ŸŸŸŸ

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InformationCorrect

Updated ProfileŸTravel RequestŸ

X Y

Detailed Process Map

Critical to Satisfaction

CostCTC

QualityCTQ Delivery

CTD

CTQCTQ CTQ CTD

CTD CTDCTC

CTC CTC

CT TreeVoice Of the Client


Measurement Systems Analysis

Resolution?

Accuracy/Bias?

Linearity?

Stability?

Precision ?

OK

OK

OK

OK

Bob Sue Tom

60

70

80

90

100

Appraiser

Perce

nt

Appraiser vs Standard

[ , ] 95. % CIP rcent

AttributeMSA

ObservationalMSA

Data type?

Gage name:Date of study:Reported by:Tolerance:Misc:

00.30.40.50.60.70.80.91.01.1 1 2 3

Xbar Chart by Operator

Sam

ple

Mea

n

Mean=0.8075UCL=0.8796

LCL=0.7354

0

0.00

0.05

0.10

0.15 1 2 3

R Chart by Operator

Sam

ple

Ran

ge

R=0.03833

UCL=0.1252

LCL=0

1 2 3 4 5 6 7 8 9 10

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Part

OperatorOperator*Part Interaction

Ave

rage

1 2

3

1 2 3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Operator

By Operator

1 2 3 4 5 6 7 8 9 10

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Part

By Part

%Contribution

%Study Var %Tolerance

Gage R&R Repeat Reprod Part-to-Part

0

100

200

Components of Variation

Per

cent

Gage R&R (ANOVA) for Response

Variable MSA

All Measurement

Systems


Analyze

4.Establish Process

Capability

5.Define

Performance Objectives

(final)

Phase Gate

Review

Define Rational SubgroupsActive data collectionValid DataVOC (specs)

Histogram on yBaseline Control Chart or Metric Graph over timeBaseline CapabilityVoice of ProcessZst, Cpk, DPMO, COPQ, RTYPareto types of defects

Revised Project DefinitionProcess MetricsQuantified Project ObjectivesDefect definitionValidated Financial Goals

Baseline PerformanceBenchmarking / Competitive Analysis

BBMBBProcess OwnerChampionFin. AnalystValidate Business SupportProject TrackingMaj. StakeholderDocumentation

Availability of ResourcesBB/Team Training NeedsBarriersScheduleBudget

6.Identify

Variation Sources

(Subjective)

6.Identify

Variation Sources

(Statistical)

"Low hanging Fruit"Identify Major Sources of VariationPotential KPIVsData collection on KPIV's

Active and/or Passive Data Collection / Sampling PlansGraphical TechniquesMulti-VarANOVAVariance ComponentsHypothesis TestingCorrelationRegression

Identify Potential Critical Xs and Improvement OpportunitiesRanked importance of KPIV's

Wk 2

Current Control Plan Detail Process Map BrainstormingFishbone/CE FMEAMistakeProofingSPC (Ys & Xs) SOP's Laws of Nature Process Knowledge


What Is a Control Chart?

Statistical control limits at +/- 3 standard deviations from mean

Region ofnon random

variation

Region ofrandom variation

10050Subgroup 0

70

60

50

40

30

Ind

ivid

ual

Val

ue

Mean=50.06

UCL=66.49

LCL=33.63

20

10

0

Mov

ing

Ran

ge

1

R=6.179

UCL=20.19

LCL=0

R = 13.

LCL= 0

I and MR Chart

Patterns within the “Region of random variation” can also indicate non-randomness


Control Chart Selection Process

Characteristic Selected

Variable Data?

Proportions Counts

Individuals Chart (I-MR)

Subgroup size > 9

Ability to calculate s for each subgroup

Average Chart (Xbar-R)

Average Chart (Xbar-s)

No

Yes

No

Yes

YesNo

Constant sample size

np or p Chart

p Chart

No

Yes

No

Yes

Constant sample size

c or u Chart

Yes

u ChartNo

Yes NoVariable Data Discrete or Attribute Data

RationalSubgroup


Process Capability

Eliminate out of

control situation

Yes

No

Variable Data Discrete or Attribute Data

Data normal?

Transform data

No

ProcessStable?

Descriptive StatisticsHistogramNormality Plot

Control Chart

DataType?

CapabilityAnalysis(Normal)

Box-Cox Transformation

SummaryStatistics

Cp, CpkDPMO/ppmZ

Attribute Type?

SummaryStatistics

Convert to ppm

CalculateSigma

CapabilityAnalysis

(Binomial)

CapabilityAnalysis(Poisson)

Alternate

StartCharacteristic

Selected

Binomial Poisson

Process Z DPU

Z Table


95.44%

68.26%

99.73%

43210-1-2-3-4

…………………….60-75%

…………………….90-98%

…………………….99-100%

The Standard Normal Curve

The standard normal curve is a special case of the normal distribution where the mean = 0 and the standard deviation = 1

Theoretical Empirical

95% of the population is within approximately ± 2 standard deviations or Z-scores of the mean


Z Transformations

X

Z

Point of Interest

Mean

Z = 3

1 2 3


Transforming a Data Distribution to a Standard Normal Curve

Area under curve can be used to estimate the probability of an “event” occurring

60 70 80 9 100 110 120

Days

Z-Scale

-3 -2 -1 0 2 3

1

-7 -6 -5 -4 4 5 6 7

95% of the observations will fall within ± 2 standard deviations

0


Z-Score Relationship to Six Sigma® Philosophy

A six sigma process would have six standard deviations between the mean and both spec limits for a short term capability study

60 70 80 90 100 110 120

Days

Z-Scale

-3 -2 -1 0 2 3

1

1st

-7 -6 -5 -4 4 5 6 7

What is the minimum number

of Z’s between the spec limits and the mean?

6st

LSL USL


Long-Term Versus Short-Term Sigma

Acceptable

USL

ZLT

ZST

ZLT = ZST – 1.5

1.5

ZLT estimates PPM or

DPMO over the long-term

ZST is used to rate

performance over the short-

term


DPMO = =

Total Defects x 1,000,000

Total Units x Total Opportunities Per UnitDPMO =

Defects Per Million Opportunities (DPMO)

Defects x 1,000,000 D x 1,000,000

Total Opportunities TOP

DPMO = DPO x 1,000,000

Number of Defects

Total Units= DPU =

D

U


Hidden Factory

Final Yield, FY = U/S = Units Passed/Units Submitted

Verify

Rework

Scrap

ProductS U

Operation

Hidden Factory:

Defects and Rework

Classic Yield Ignores Role of “Hidden Factory”


Throughput Yield Exercise

• Calculate the TPY for Process Steps 2 and 3• Calculate the FY for each step and compare

Step 3100

Units70 Units

10 units lost as scrap

Step 1

10 Rework 10 Rework 10 Rework



Step 2


Rating of Importance to

Client

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Process Inputs Total

1

2

3

45

6

7

Cause and Effect Matrix

Process Outputs

Complete Travel

Authorization

Contact Agency



Auth. Code?


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Form



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InformationCorrect


X Y


Scoping

KPIV Identification

Voice Of the Client

Delivery Defects

PEOPLE

ENVIRONMENT

MATERIALS

METHODS

MEASUREMENTS

MACHINES

Maintenance

Servers

Resources/Shift

Ticket Types

Times

Training

Freq of Updates

Experience Level

Postal Service

Co. Profiles

Traveler Profiles

Terminals

Call Volume

S

elf

systems

internet

Zabar Volume

Process

Queue

Hol

ida

yTim

e of

D

ay

Phone Service

T-1

Line

s

Credit card

addressem

ail

Computer Prog

PO Damage

PO DamageCall Routings

Carrier Updates

Time Zones

Comp Downtime

Team Ratings

C&E Matrix Construction


C&E Matrix Scoring

Rating of Importance to

Client

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Process Inputs Total

1

2

3

45

6

7

Cause and Effect Matrix

Process Outputs

0

1000

2000

3000

4000

5000

6000

7000

Defect

Cou

nt

C&E Matrix, Pareto of Customer Requirements As They Relate To The Process

Rank and display on Pareto Chart


0

1000

2000

3000

4000

5000

6000

7000

Defect

Cou

nt

C&E Matrix, Pareto of Customer Requirements As They Relate To The Process

FMEA Construction

Complete Travel

Authorization

Contact Agency



Auth. Code?


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InformationCorrect


X Y



FMEA Construction (continued)

Delivery Defects

PEOPLE

ENVIRONMENT

MATERIALS

METHODS

MEASUREMENTS

MACHINES

Maintenance

Servers

Resources/Shift

Ticket Types

Times

Training

Freq of Updates

Experience Level

Postal Service

Co. Profiles

Traveler Profiles

Terminals

Call Volume

Self

systems

internet

Zabar Volume

Process

Queue

Hol

ida

yTim

e of

D

ay

Phone Service

T-1

Line

s

Credit card

addressem

ail

Computer Prog

PO Damage

PO DamageCall Routings

Carrier Updates

Time Zones

Comp Downtime


RPN on Pareto Chart


Core Team: Date (Orig):

Date (Rev):

Process Step

Input Output

Process Spec (LSL, USL,

Target)

Cpk / Date

(Sample Size)

Measurement

System

%R&R or P/T

Current Control Method (from FMEA)

Who Where WhenReaction

Plan

8 9 10 11 12

LSL USL

Process Capability Analysis for Distance

USL

Target

LSL

Mean

Sample N

StDev (Within)

StDev (Overall)

Cp

CPU

CPL

Cpk

Cpm

Pp

PPU

PPL

Ppk

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

12.0000

*

10.0000

9.8614

125

0.328760

0.490139

1.01

2.17

-0.14

-0.14

*

0.68

1.45

-0.09

-0.09

576000.00

0.00

576000.00

663368.77

0.00

663368.77

611349.22

6.41

611355.63

Process Data

Potential (Within) Capability

Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance

Within

Overall

Initial Control Plans

Contact Agency









Correct Traveler Information

Ÿ

Traveler Info, SCompany Info, S

Restrictions if applicable, SPreferences, S (seat, class)

Agent NTraveler N

Computer Systems, NMeasurement Systems, S

ŸŸŸŸŸŸŸŸ

Client name, SLocation, SAuth. Code, SOperator, SAgent NTraveler N

Computer Systems, NMeasurement Systems, N

ŸŸŸŸŸŸŸŸ


Info Into System

Ÿ

InformationCorrect

Updated ProfileŸ


Gage name:Date of study:Reported by:Tolerance:Misc:

00.30.40.50.60.70.80.91.01.1 1 2 3

Xbar Chart by Operator

Sam

ple

Mea

n

Mean=0.8075UCL=0.8796

LCL=0.7354

0

0.00

0.05

0.10

0.15 1 2 3

R Chart by Operator

Sam

ple

Ran

ge

R=0.03833

UCL=0.1252

LCL=0

1 2 3 4 5 6 7 8 9 10

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Part

OperatorOperator*Part Interaction

Ave

rage

1 2

3

1 2 3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Operator

By Operator

1 2 3 4 5 6 7 8 9 10

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Part

By Part

%Contribution

%Study Var %Tolerance

Gage R&R Repeat Reprod Part-to-Part

0

100

200

Components of Variation

Per

cent

Gage R&R (ANOVA) for Response

MSA

FMEA


Data Collection Plan

What to Measure

Defect Definition

Type of Measure

Type of Data

Operation Definition

Data Collection

form

Sampling Plan

Service Quality Professionalism, Courteous Output/Process Discrete Continuous Ordinal scale 1-5 where 1 = did not meet expectation,

5 = exceeded expectation

Survey form tracked by job number

Internal audit by trained personnel; corporate accounts, 3 branches, 2 shifts, 10 operators within branch, 10 trials per operator at random

Service Delivery Time in queue, Time to process

Time to deliver itinerary/ticket

Delivery error:

• Agent error

• Profile error

• Mail error

Output/Process Continuous for times collected; discrete counts for defects

Queue = answering service to agent pick up

Process time = agent pick up to end call

E-ticket Itinerary: end call to message sent

Ticket mailed: end call to FedEx pickup

Time study form for auditors; time in queue and time to process

Internal time stamps on collection form by job number; time stamped for pick up

Time in queue and time to process collected by auditors above; estimated collection is One Week to determine baseline

• Build Data Collection Plan based on:• SIPOC• Process Maps• C&E Matrix• FMEA• Sampling strategy


Basic Descriptive Statistics


Graphical Analysis

18 10 19 43 59274 4.3 2.4 4.510.213.964.8

100.0 95.7 93.4 88.9 78.7 64.8

400

300

200

100

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cen

t

Cou

nt

Pareto Chart for DefectsPareto Chart Box Plots

Run Chart Scatter Plot Histogram


More Graphical Analysis

Main Effects Plot

Interaction Plot

Normal Probability

Multi-Vari Chart


Hypothesis Testing Roadmap

i

j

1 or 2 Factors?

1 or >1 Levels?

Contingency Table

HO : FA Independent FBHA: FA Dependent FB

Stat > Tables > Chi2 Test

Attribute

1 Factor

2-Proportion Test

Stat > Basic Stat >2-Proportion

2 Samples2 levels to test

1-Proportion Test

Stat > Basic Stat >1-Proportion

1 Sample1 levelto test

Is data normal?

Stat > Basic Stat >1-Sample t

Chi2 Test

Stat > BasicStat > Display Desc >Graphical Summary(if target sigma falls

between CI, then fail toreject H O )

Stat > ANOVA >Test for Equal

Variance

2-Sample t Test

Stat > Basic Stat >2-Sample t

(if sigmas are equal, usepooled std dev to compare.

If sigmas are unequalcompare means using

unpooled std dev)

1 level

2 levels

Bartlett's Test

Stat > ANOVA > Test for Equal Variance

If sigmas are NOT equal, reduce data to determine which is different

1-Way ANOVA(assumes equality of

variances)

Stat > ANOVA > 1-Way(select stacked or

unstacked data)

Test for means

*Data is Normal

HO: 1 = t

HA : 1 tt = target

Data Not Normal

1, 2 or morelevels?

1 level

Stat > BasicStat > Display Desc >Graphical Summary(if target sigma falls

between CI, then fail toreject HO )

HO : 1 = tHA : 1 t

t = target

1-Sample Wilcoxon or

1-Sample Sign

Stat > Non-parametric >and either 1-Sample

Sign or 1-SampleWilcoxon

TestMedians

2 ormorelevels

Mood’s Median Test(used with outliers)

Stat > Non-parametric >Mood's test

Kruskal-Wallis Test(assumes no outliers)

Stat > Non-parametric >Kruskal-Wallis

2 ormorelevels

H O : M 1 = M 2 = M 3 ...HA : M M j

for i j(or at least one is different)

H O : M 1 = M 2

= M 3 ...

HA : M i M for i j(or at least one is different)

HO: M 1 = M tHA: M 1

M tt = target

Mann-Whitney Test

Stat > Non-parametric >Mann-Whitney

H O: M 1 = M 2H A: M 1 M 2

HO : 1 = t

HA : 1 tt = target

HO : 1 = 2 = 3 ...HA : i j for i j

(or at least one is different)

H O: 1 = 2HA : 1 2

HO : 1 = 2

= 3 ...

HA: i j for i j

(or at least one is different)

HO: P1 = Pt

HA: P1 Ptt = target

HO: P1 = P2

HA: P1 P2

2 levels only

If P > 0.05, then fail to reject HO If P < 0.05, then reject HO

Ensure the correct sample size is taken

1, 2 or moreFactors?

Variable

2 levels or> 2 levels?

Fail to reject HO

START

2 or more Factors

1 Factor

OH : Data is normal

AH : Data is not normal

orStat > Basic Stat > Normality Test Stat > Basic Stat > Descriptive Statistics

(graphical summary)Assumption that sample size is sufficient

HO : 1 = 2HA : 1 2

Design of Experiment

Paired t Test

Stat > Basic Stat >Paired t

HO: 1 = 2

HA : 1 2

Test for Std.Dev.

Levene’s Test

Test for Equal Variance

H O : 1 = 2 = 3 ...H A : i j for i j(or at least one is different)

(If the Null Hyp is rejected,use 2 sample T test)

Test for mean or

Std.Dev.?

1, 2 or >2 levels?

More than 2 levels

Test for mean or

Std.Dev.?

Test for means

Test for Std.Dev.

F Test

Test for mean or

Std.Dev.?

Test for means

Test for Std.Dev.

Is Data Dependent?

Yes, Data is Paired

No, Data is drawn independently from 2 populations

1-Sample t Test

2 Factors

Stat > ANOVA >

Variable or

Attribute

Data?

Test for median or Std.Dev.?

Test for Std.Dev.

Chi2 Test

In general:

* basic hypothesis tests are robust to reasonable departures from normality


Process Flow Of A Hypothesis Test

DECIDE:What does the evidence suggest?Reject Ho? or Fail to reject Ho?

Calculate test statistic and/or p-value

Collect sample data

Establish significance level ()

State the “Alternate Hypothesis” (Ha)

State a “Null Hypothesis” (Ho)

Define the problem and state objectives


Goal is to Collect Variable Data

More powerful statistical techniques can be performed using variable input and response data

Inputs (X)

Outputs (Y)

Attribute Variable

Variable

AttributeProportion tests,

Chi-SquareT-tests, ANOVA, DOE, Regression

Correlation, Multiple Regression

Discriminant Analysis, Logistic

Regression


Improve

Wk 3

8.Discover Variable

Relationshipy=f(x)s=g(x)

9.Establish Operating

Tolerances

Phase Gate

Review

KPIVsIdentif y RelationshipsOptim. Input Levels - y=f (x) - s=g(x)Conf irmation Runs / PilotTo-be processAlternative Solutions

Active data collection on KPIV's & KPOV'sMultiple RegressionRSMSimulationModelingDOE (optimizing)

Identif ied RelationshipsOptimum Input LevelsConf idence IntervalsCost/Benef it Analysis

Optimal Robust Settings f or Xs with Tolerances

BBMBBProcess OwnerChampionFinancial AnalystProject TrackingDocumentation

Implementation Buy-inAvailability of ResourcesBB/Team Training NeedsBarriersScheduleBudget

7.Screen

Potential Causes

Identif y Potential Critical Xs and Improvement OpportunitiesRanked importance of KPIV's

Passive and/or active data collection on KPIV's & KPOV'sMSA on XsCorrelationRegressionDOE (screening)


IMPROVE PHASE Identifies and validates the optimum operating conditions

Improve Phase Roadmap

DESIGN OF EXPERIMENTS (DOE)

• Simulation or physical

MODELING• Discover variable relationships• What’s the best combination of the X’s (inputs) for

producing the best Y (output) to meet or exceed the CTQ• Diagnose PIV performance and establish performance

objectives

REGRESSION• Historical or active data• Multiple or simple

VALIDATE IMPROVEMENT• Pilot test improvements• Ensure that optimum inputs truly result in

desired output• Update FMEA and process map

SELECT IMPROVEMENTS• Identity alternative solutions• Determine the optimum solution• Cost benefit analysis


Correlation Coefficient

302010

100

90

80

70

60

50

40

X

Y

r = -1.0302010

90

80

70

60

50

40

30

20

X

Y

r = +1.0

302010

76

75

74

73

72

71

X

Y

r = 0.0

No correlation


Regression Plot

550 600 650

1000

1500

2000

X

Y

Sales = -4710.51 + 10.0720 Shelf Space

S = 87.2641 R-Sq = 95.7 % R-Sq(adj) = 95.3 %

Regression Plot

Xbar

Ybar


Correlation and Regression

• Correlation tells how much linear association exists between two variables

• Regression provides an equation describing the nature of relationship

Correlations: Shelf Space, Sales

Pearson correlation of Shelf Space and Sales = 0.978

p-value = 0.000

Regression Analysis: Sales versus Shelf Space

The regression equation is Sales = - 4711 + 10.1 Shelf Space


FULL FACTORIAL SIMPLE 2K• Testing many variables at 2 levels (high/low)• Screening, characterization, optimization

GENERAL FULL FACTORIAL• Tests multiple variables, at various levels• Characterization, optimization

FRACTIONAL FACTORIAL• Tests multiple variables but not all

interactions; Not all levels, less experimentation require

• Screening

IMPROVE PHASETests multiple variables at different levels to screen, characterize and optimize process output

DESIGN OF EXPERIMENTS (DOE)

• Simulation or physical

Improve Phase Roadmap


Strategy of Experimentation

Planning Phase

1.Define the problem

2.Set the objective

3.Select the response(s), Y’s

4.Select the factors(s), X’s

5.Select the factor levels

6.Select the DOE design

7.Determine sample size

8.Run the experiment

Execution Phase

9.Collect data

10.Analyze data

11.Validate results

12.Evaluate conclusions

13.Revise SOP’s

14.Train affected workforce

15.Document results

16.Consider next experiment

Manage resources; have a contingency plan; use statistical common sense


The ANOVA Results

The ANOVA Table shows which factors are significant. Regression coefficients are listed.


The Pareto of Standardized Effects

The Pareto Chart of effects shows the ranking of the variation identified by each source. Note the “line of significance”


By factor, low level to high level

Main Effects, Interactions and Cube Plots

Parallel lines indicate NO interaction

The response is plotted on the orthogonal factor axis


The Graph Results

The residual vs. fits show slight widening, but the widening here is not a concern. Looking for strong outliers and patterns

that could indicate special cause.

Looking for random distributions without a pattern.

Normality Plot and Histogram of the residuals show the residuals

to be “normal”


Reducing Risk

Define the population or process aboutwhich inferences are to be drawn

Y

Y

N

PROCEED

N

N

Define the goal of the study

Define the characteristicor property of interest

Define the environment or type of data that you have access to

Evaluate relevance of planned data for desired inferences,

i.e., evaluate whether there is a “gap”

Can werestrict population

of interest?

Can wechange accessible

data?

Risk OK?


Cost / Benefit Studies

• Verify and validate benefits outweigh costs• Consider ALL costs of implementation• Determine ALL benefits

• expense• time• customer/employee satisfaction• Safety• Others


Pilot

Pilot Purpose• Revise improvement before

implementation• Determine best

implementation method• Lower failure risks• Identify all implementation

costs/benefits• Confirm expected results• Obtain user feedback• Obtain buy-in

Pilot Execution• Involve management –

especially Process Owner• Carefully and thoroughly plan

pilot activities• Use affected personnel – get

buy-in• Use same measurement

system from “Measure” phase• Monitor results• Debrief • Adjust full scale

implementation accordingly

Don’t shortcut – always do a pilot!


Control

Wk 4

11.Determine Process

Capability(Ys and Xs)

12.Implement Process Control

Final Project Presentation/ Phase Gate

Certification &

Celebration

Pilot Plan ResultsControl Charts (x's & y 's)Improved Capability (LT)Improved Benchmark (Zst, Cpk, DPMO)Signif icance of Improvement (hypothsis test)Re-Calculate FMEA-RPN

Active data collectionCurrent Control PlanSPC on Xs + YsSOP, TrainVOCCapability Analysis

ISO Document.Ident'd KPIVSOPs, TrainMistake Proof ing / Error Proof ingAccountabilityResponsibilityFinalize Transition to Process Owner

Control Plan (x's & y 's)Validate Controlled ProcessSustained Perf ormanceMonitor Plan BB Audit PlanLink to Customer CTsTransition to Process Owner

Lessons Learned / Best Practices (Translate)Communicate SuccessCelebrate TeamDocument Results

DocumentProcess OwnerChampion BB MBB Fin. ValidationProj. Tracking EntryReplication ?

Document-Ev idenceProcess OwnerChampionBBMBBFinal Financial Validation (2 projects)Leadership coursesProj. Tracking Entry

10.Validate

Measurement System &

Improvements

Update Process Maps, CE, FMEA, MetricsPilot Plan / RunValidated Measurement System Af ter Improve (Xs & Ys)Conf irmation of Changes (stat)

Active data collectionNew Process MapMeasurement System EvaluationCalibration StudyControl ChartsValidation or Pilot Run Planning


Verb

al In

stru

ctio

ns –

Not

cons

ider

ed a

ccep

tabl

e

cont

rol f

or S

ix S

igm

a

Proj

ects

Stan

dard

Ope

ratin

g

Proc

edur

es (S

OP’

s)

Stat

istic

al P

roce

ss

Con

trol

(SPC

)Po

ka -

Yoke

(Mis

take

Pro

ofin

g)

Des

ign

For S

ix S

igm

a

Employ the Control Method that ensures the Greatest Control with the Least Amount of On-Going Effort from the Process. Typically, a combination of several are required

Proc

ess

or P

rodu

ct

Re-

desi

gn

Least Control Effectiveness Most

Least Effort to Implement Most

SOP’

s w

/Tra

inin

g &

Eval

uatio

n


CONTROL PLAN AND REACTION PLAN

LONG TERM MSA PLAN

CHANGE CONTROL

TRAINING PLAN; STANDARDIZED WORK; VISUAL WORK PLACE

STATISTICAL PROCESS CONTROL

Control Phase Roadmap

5S AND MISTAKE PROOFING

INTEGRATE WITH QUALITY SYSTEM

SUSTAIN EXPECTATIONS


Major Control Tools

Prepared by: Page: of

Approved by: Document No:

Location: Approved by: Revision Date:

Area: Approved by: Supercedes:

KPOV KPIV

Measurement Method

Who Measures

Sub ProcessSub Process

StepSpecification

Characteristic

Specification/ Requirement

USL LSL

CTQSample Size Frequency

Where Recorded

SOP Reference Decision Rule/

Corrective Action

Six Sigma Process Control Plan

Customer

Process Name:

Int/Ext

1S

2S

3S4S

5S05

10

1520


Module Objectives

By the end of this module, the participant will:• Receive a refresher on the 12 Steps of the Breakthrough

Strategy• Understand how the 12 Steps of the Breakthrough

Strategy provide the backbone for six sigma DMAIC projects

• Know the linkage of six sigma tools to the Breakthrough Strategy

• Understand the linkages of tools to each other


Exercise


Exercise: Rapid Fire Catapult Launch

OBJECTIVE:

To fire the catapult and record the distance in inches for each of the launches. The measured distance will be from the front of the launcher base to the point where the ball hits the floor. Use a data sheet to record the distances in the order in which they were obtained and who fired the catapult

Target is 60 inches; Spec limit is: LSL= 50, USL = 70

RULES

1.Every shot will be launched with the same catapult settings:a Let it set as isb. Stop angle = 130 degrees

2. Each member of the team will perform an equal number of launches (as near as possible). A “launch” consists of loading, pulling back, and releasing. Do 5 each

3.There will be a time limit of 15 seconds between successive launches. Each group is to be self policed with a penalty associated to late firings.

4. You must get plan approval before any launches

5. 5 practice shots MAXIMUM.


Deliverables CTS Statement

Process Flow Map

Cause and effect for problem “missed target”

Measurement System Analysis

Run Chart, control chart

Histogram, normal probability plot

Tally Sheet for “missed target”

Pareto, box plots

Descriptive Statistics – Mean, Median, Std. Dev., Variance

Confidence Interval for Mean and Std. Dev.

Hypothesis test for means comparing operators, tables

Capability Study

Vary one factor, hold all other constant, take 10 shots and plot correlation

Trademarks and Service Marks

Six Sigma is a federally registered trademark of Motorola, Inc.

Breakthrough Strategy is a federally registered trademark of Six Sigma Academy.

ESSENTEQ is a trademark of Six Sigma Academy.

INTELLEQ is a trademark of Six Sigma Academy.

METREQ is a trademark of Six Sigma Academy.

Weaving excellence into the fabric of business is a trademark of Six Sigma Academy.

FASTART is a trademark of Six Sigma Academy.

Breakthrough Value Services is a trademark of Six Sigma Academy.

Breakthrough Design is a trademark of Six Sigma Academy.

Breakthrough Lean is a trademark of Six Sigma Academy.

Breakthrough Six Sigma is a trademark of Six Sigma Academy.

Design with the Power of Six Sigma is a trademark of Six Sigma Academy.

Design for Six Sigma Software is a trademark of Six Sigma Academy.

Design Six Sigma for Software is a trademark of Six Sigma Academy.

Legal Lean is a trademark of Six Sigma Academy.

SSA Navigator is a trademark of Six Sigma Academy.

SigmaCALC is a trademark of Six Sigma Academy.

SigmaFlow is a trademark of Compass Partners, Inc.

SigmaTRAC is a trademark of DuPont.

MINITAB is a trademark of Minitab, Inc.

VarTran is a trademark of Taylor Enterprises.

Documents

Black Belt Refresher