2-01 six sigma roadmap

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Company ConfidentialCopyright NN, Inc. 2004

6

Defect / VariationDefect / VariationReduction RoadmapReduction Roadmap

==The Six SigmaThe Six Sig

maMethodologyMethodolog

y


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Example: Identify the process settings required to eliminate theneed to grade bearings

Y = f (x)Y = f (x)

Key Process InputVariables (Xs)

Key Process OutputVariables (Ys)

Surface Finish

CleanlinessHardness

Ovality

Waviness

Assembly Machine

Lubrication

ProcessProcess

BearingBearing

PerformancePerformance

NoiseLevel

Critical

toCustoCusto

mermer

Characterization of Process

What causes the Noise & the Variation in the Noise Level ????

Example of a Process with Variation


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Positional variation (on the same part):Variation within a partVariation within a batch or set-up

Variation within machine positionVariation in measurement system

Cyclical variation (repeating pattern):Different process settings or adjustments by

operator or shiftMaterial differences Supplier changesTool or machine wear and repair or replacementSorting practicesMaintenance schedule

Temporal variation (over time):Day to Day or Week to Week output from a

processSet-up to Set-up for machines or jigsShift to ShiftSeason to Season (due to temperature or

humidity)

Taking the total process variation apart into its components is essential to

understanding and prioritizing the elimination of significant sources and

controlling those that remain.

Different Components of Variation

A

B

1

C

2

D

now

E

later

F


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Define

Measure

Analyze

Improve

Control

Continuously

repeat andimprove!

No complacencywith success!

The Six Sigma Methodology

DMAIC

ProcessCharacterization

ProcessOptimization

OpportunityIdentification

The 5 phases of the Six Sigma Methodology


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Defect / Variation Reduction Roadmap

Define Measure Analyze Improve Control

A. Identify ProjectKPOVs & KPIVs

Deliverables:

1) IdentifyCustomer(s)

2) IdentifyProject Key ProcessOutput Variables(KPOVs)

3) Identify the

Key Process InputVariables (KPIVs)that effect theKPOVs.

B. DevelopTeam Charter

Deliverables:

1) Develop TheBusiness Case

2) Develop TheProblem and GoalStatements

3) DetermineProject Scope

4) Select Team& Define Roles

5) Set ProjectMilestones

C. DefineProcess Map

Deliverables:

1) High LevelProcess MapConnecting theCustomer to theProcess

2) Summary ofDefine Phase

Tools:

sProcess Mapping


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1. Select KPOVCharacteristics

Deliverable:

IdentifyMeasurable Key ProcessOutput Variable (KPOV)that will be Improved

Tools:

sQuality FunctionDeployment (QFD)

sProcess Mapping

sCause & Effect Diagram

sFMEA

sDiscrete vs. Contin. Data

2. Define Perfor-mance Standards

Deliverable:

Determineand ConfirmSpecification Limitsfor your Y.

3. MeasurementSystem Evaluation

Deliverables:

1)MeasurementSystem Adequate toMeasure Y

2) Summary of Measure Phase.

Tools:

sContinuous GageR&R

sTest / Retest

sAttribute Gage R&R


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5. Define Perfor-mance Objectives

Deliverable:

StatisticallyDefine the Goal of theProject

Tools:

sBenchmarking

4. EstablishProcess Capability

Deliverable:

BaselineCurrent Process

Tools:

sBasic statistics

sGraphical Analysis

sSampling

sContinuous Zst, ZltsNormality

sDiscrete Zst, Zlt

6. Identify VariationSources

Deliverable:

List ofStatistically SignificantXs, Chosen Based onAnalysis of HistoricalData

Tools:

sProcess Analysis

sGraphical Analysis

sHypothesis Testing

sRegression Analysis

sANOVA





8. Discover VariableRelationships

Deliverables:

1) Establish TransferFunction Between Yand Vital Few Xs.

2) Determine OptimalSetting for the VitalFew Xs.

3) Perform Confirmation

Runs.

Tools:

sDOE, FactorialDesigns

7. Screen PotentialCauses

Deliverable:

Determinethe Vital Few XsThat Are CausingChanges in Y.

Tools:

sScreening Design of

Experiments (DOE)

9. Establish OperatingTolerances

Deliverable:

SpecifyTolerances on theVital Few Xs.

Tools:

sSimulation





11. DetermineProcess Capability

Deliverables:

1) DeterminePost-ImprovementCapability andPerformance

2) Confirm that the ImprovementGoal has beenrealized

s

Tools:

sSee Step 4

10.Define and ValidateMeasurem. System

Deliverable:Measurement Systemis Adequate toMeasure Xs in theActual Application

Tools:

sSee Step 3

s

12.ImplementProcess Control

Deliverable:

Develop andImplement ProcessControl Plan

Tools:

sFMEA

sPoka-Yoke

s

5-SsTraining / Certification

sStat. Process Control

sStd. Work Processes



Example Process Map

KPIVs

Dimension of ballsHardness of ballsCleanliness ballsLot sizeType of Bakelite wheelCondition of wheelType of Bakelite plateCondition of plate

Type of coolantTemperature coolantDilution of coolantPollution of coolantAmount of coolantType of infeed plateCondition of infeed plateType of outfeed plate

Condition of outfeed platePressure wheel & plateRotating speed wheelElapsed time

KPOVsDiameter of balls

Distribution ofdiameterOvality of ballsWaviness of ballsSurface of ballsCleanliness of ballsM

achine

is

lapp

ingballs

Operator loads 1 lot into 2ndlapping machine & starts themachine for 1st part of cycle

Op. assures proper ball infeed

Op. checks ball diameter

Op. sets machine for 2nd part of cycle

Op. stops the machine

Op. unloads the machine

Op. measures ovality &waviness and checks surface

& cleanliness of balls

Op. measures distribution of ball dia



NVA

NVA

NVA

NVA

NVA

NVA

NVA

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These tools are utilized to characterize the process, that is, to understand

the behavior of process outputs (Y) given specific values of inputs (X).

This type of study investigates the process in its natural operating

state (no artificially induced variation, such as with Design of

Experiments).

Correlation provides a graphical analysis and mathematical tool with which

we can quantify the degree or extent of the relationship between twovariables.

The degree of linear association between two variables is quantified

by the correlation coefficient r.

Regression can be used as a means of obtaining prediction equations (not

necessarily linear).These equations can be used to predict the output variable (Y)

based on a certain input variable (X).

Correlation and Regression



Multi-Variate is a graphical tool, which through logical

sub-grouping, allows the analysis of input effects onprocess outputs.

Data is collected while the process is in its natural state.

The goal of this analysis is to narrow the field of the trivial many

inputs, to only the critical few.

Categories of sub-groups are typically as follows:

Positional: variation within a part

Cyclical: variation between parts

Temporal: variation over time

Multivariate Analysis



Hypothesis testing allows us to answer the practical question: "Is

there a real or significant difference between process #1 and process#2, or output from scenario #1 and output from scenario #2 such that

the difference is too large to be attributed to chance?

The following examples illustrate some potential uses of this test:

Is there a real difference between Machine A and Machine B?

Is there a relationship between the number of defective parts andthe manufacturing method?

Does a particular gage create more or less defects using aspecific test method?

Has the process change obtained the desired change inperformance?

Based on our sample, is the actual proportion of defective partsconsistent with the supplier's claim of 0.1%?

Hypothesis Testing



The basic idea of this procedure is to split the total variability of theresponse variable (the variability of all observations) into blocks

that relate to different processes, suppliers, shifts, or input

categories, etc.

The analysis provides graphical as well as quantitative measures ofwhether significant statistical differences exists between process

output for different input variables.

ANOVA - Analysis of Variance



In general, DOE is a controlled, active variation of a process toidentify optimum settings or procedures for improved productquality.

More specifically, it is a systematic method where a number offactors (input variables) are changed simultaneously following apredetermined pattern, in order to investigate main effects as well

as their interactions.

DOE allows for more information to be gained from less trial runsthan traditional one factor at a time experimentation methods.

Design of Experiments (DOE)


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Phase Step Description Focus Tools Deliverables

Define A Identify Project KPOVs & KPIVs Project KPOVs & KPIVs

B Develop Team Charter Approved Charter

C Define Process Map Process Mapping High Level Process Map

Measure 1 Selec t KPOV Charac teris tics Y Customer,QFD,FMEA, Process Mapping, Cause

& Effect Diagram, Discrete vs. Continuous Data

Project Y

2 Define Performance Standards Y Customer, Blueprints Performance Standard for

Project Y

3 Measurement System Analysis Y Cont inuous Gage R&R, Tes t/Retes t, At tribute R&R Data Collec tion P lan & MSE,

Data for Project Y

Analyze 4 Establish Process Capability Y Basic statistics, Graphical Analysis, Sampling,

Continuous Zst / Zlt, Normality, Discrete Zst / Zlt

Process Capability for Project Y

5 Define Performance Objectives Y Team, Benchmarking Improvement Goal for Project Y

6 Identify Variation Sources X Process Analysis, Graphical Analysis, Hypothesis

Tests, Regression Analysis, ANOVA

Prioritized List of all Xs

Improve 7 Screen Potential Causes X DOE-Screening List of Vital Few Xs

8 Discover Variable Relationships X DOE, Factorial Designs Proposed Solution

9 Establish Operating Tolerances Y, X Simulation Piloted Solution

Control 10 Define & Validate Measurement

System on Xs in Actual Application

Y, X Continuous Gage R&R, Test/Retest, Attribute R&R MSE

11 Determine Process Capability Y, X Bas ic s tat ist ics, Graphical Analysis , Sampling,

Continuous Zst / Zlt, Normality, Discrete Zst / Zlt

Process Capability Y, X

12 Implement Process Control X FMEA, Poka-Yoke, 5-S,Training / Certification,

Stat. Process Control, Std. Work Processes

Sustained Solution,

Documentation

Six Sigma Roadmap, Summary

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