Six Sigma Introduction & Application_031020

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Six Sigma Introduction

& Application John Lu

MK3300

2003/10/10



2 Six Sigma Introduction & Application

Who should attend this e-Learning

The job relate with process as

Process Improvement Manager To be Master Black Belt

Process/Product Manager Process Improvement Engineer

To be Black Belt

Process/Product Engineer

Quality Assurance Engineer To be Green Belt

PD Supervisor To be process owner

Remark: MBB, BB and GB are the titles at six sigma




Outline of Six Sigma e-Learning

0. Over view ± 0.1. What¶s six sigma

± 0.2. Defect impact for process

1. Theory

± 1.1. Build up a six sigma project ± 1.2. Operation flow v.s. DMAIC

± 1.3. Apply statistics approach in the step of six sigma project

2. Practical Operation ± 2.1. Implement timing

± 2.2. Project Team member ± 2.3. Six Sigma Configuration

3. Case Study ± 3.1. E-CAT 500DPPM project

4. Appendix




0. Over view

As a business strategy, Six Sigma uses

statistics, problem solving and problem

prevention tools to improve customer

satisfaction by removing and pre

venting

defects from processes, products,

ser vices, documentation and decisions

to a 99.999% level of perfection

It¶s the master strategy on project after

project , where the projects are selected

based on key business issues




0. Over view ~ History of Six Sigma 1/2

1987 - 1994

1992 - 1996

1995 - 1998 General Electric

Reduced in-process defect by a factor of 200 Reduced manufacturing cost by $1.4 billion Increased stockholders share value fourfold

14% growth per quarter and 520% increase of share price

Reduced new product introduction time by 16%

Company-wide savings of over $1 billion Estimated annual savings $6.6 billion by year 2000






0.1. Over view ~ What¶s Six Sigma 1/2

-6+6-1 X -2-3-4-5 +1 +2 +3 +4 +5

Spec. limit Percent Defect ppm± 1 sigma 68.27 317300

± 2 sigma 99.45 45500

± 3 sigma 99.73 2700

± 4 sigma 99.9937 63

± 5 sigma 99.999943 0.57

± 6 sigma 99.9999998 0.002

Normal Distribution CenteredLower

specification

limit

Upper

specificationlimit




0.1. Over view ~ What¶s Six Sigma 2/2

-6+6-1 X -2-3-4-5 +1 +2 +3 +4 +5

Spec. limit Percent Defect ppm± 1 sigma 30.23 697700

± 2 sigma 69.13 308700

± 3 sigma 93.32 66810

± 4 sigma 99.3790 6210

± 5 sigma 99.97670 233

± 6 sigma 99.999660 3.4

Normal Distribution Shifted 1.5Lower

specification

limit

Upper

specificationlimit




0.2. Over view ~ Defect impact for process

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A Key Process Output DataAssessment of

time sequenced data

Assessment to

the need of customer

120 130 140 150 160 170 180 190

LSL USLTarg t

r c ss apability

alysis f r Right

USL

Targ! t

LSL

M! an

Sampl! N

St" !

v (Within)

St" !

v (Ov! r all)

Cp

CPU

CPL

Cpk

Cpm

Pp

PPU

PPL

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

185.00

145.00

125.00

147.95

80

10.4613

11.1078

1.09

1.35

0.84

0.84

0.87

1.03

1.27

0.79

37500.00

0.00

37500.00

14125.01

198.84

14323.85

19408.88

425.75

19834.63

Pr oc ! ss " ata

Pot! ntial (Within) Capability

Ov! r all Capability Obs ! rv! d Perf ormance Exp. "Within" Perf ormance Exp. "Over all" Perf ormance

Within

Over allCustomer need

Frequency of

non-conformance




1. Theory

To reduce variance under particular

level, and let six sigma (6) more

narrow to meet customer specification

1. To create a close loop system and it issensitive enough to reduce the waver for company from outside

2. Using a lot of statistics technique toassist project team for

Analyzing currentAnalyzing current situationsituation

Taking improvement planTaking improvement plan

Key conceptKey concept

PurposePurpose




1.1. Build up a six sigma project

Choice a workable project

± Outer source

Customer voice, market voice,

opponent competition

± Inner/ Outer source

Help enterprise to overcome the

challenge from make a strategy or

customer tactic

± Inner source

Current operation¶s frustration,

event, issue or opportunity





Define a project¶s rule

± Base on current demandcurrent demand,

abilityability and targettarget to choicemost suitable project

± Does this project have

mean? Can we manage it?





DefineDefine

MeasureMeasure

AnalyzeAnalyze

Impr oveImpr ove

Contr olContr ol

Six Sigma improvement flow Define issue

Make sure request

Setting target

Verify issue / flow

Clear issue/ target Measure key process / Input

Develop the assumption between cause & effect

Conform some key root causes

Verify assumption

Try to solve problems Test solved program

To standardize solved program / examine result

Setting the criteria for keeping achievement

By request to modify issue





Step 1: Make sure core process and key customer DefineDefine

a. Check enterprise core process

b. Define process output & key customer

c. Work up top level¶s core process flow

Material provide

Product develop

Product and deliver

Customer Ser vice

Core

pr ocess Customer

Human

resourceFinance Basic

developmentStrategy





Step 2: Define customer requesta. Listen to customer¶s voice

b. Research achievement criteria

c. Analyze and set request¶s priority

DefineDefine

Output requestFinish good or service need to

satisfy customer satisfaction

Decision customer¶s strategy:1. 80% incoming come from 20% customer

2. Analyze and setting customer request¶s priority

Process

Input Output

=Cust r

R uest udit





Step 3: Examine current achievementa. Follow customer request to plan &

execute achievement¶s measure

b. Study the base line of variance

MeasureMeasureMeasure flow

1. Select what need to

measure

2. Study how to operate

3. Conform data source

4. Prepare sample plan

5. Implement & enhance

measure accuracy

Key point of Measurement

Transform measure ³item´ to be

observed object or action

Continuous versus Discrete

Measure

Need have reason to measure




AnalyzeAnalyze


Step 4a: Analyze & find root causea. Select improvement project

b. Using statistic tool to find factor (cause)

c. Verify the assumption

Simplify or reduce

assumption

Develop the assumption

between cause & effect

Analysis data /

flow

Analysis data /

flow

Make sure and

select

³key´ factor







Step 5: Expand and integrate six sigma systemContr olContr ol

a. Implement close loop management and

go to six sigma

b. Define process owner & manage duty

Continuous measure & keep achievement Keep innovation and new approach

To do the effective scale and chart

To establish flow response plan

Define process owner & manage duty

Using process management

Implement close loop management and go to six sigma

Using process management tool

Process marker

Customer report card




1.2. Operation flow v.s. DMAIC

Desi Productio Customer

Voice of Customer Critical of Quality

Xs Xi Xp Xo Xc

S I P O C

s i p o c

Statistical Analysis Approach

Maintain improvement

Maintain (Xs´)

Create

value

(Y)MOT

Y=f (Xi)

SPC

ANOVA

Correlation &

Regression

DOE

FMEA

Mistake proofing

QFD

DefineDefine

MeasureMeasure

AnalyzeAnalyze

Impr oveImpr ove

Contr olContr ol

Variance




1.3. Statistics approach for DMAIC

Flow chart

total processCreate C&E

Diagram

Control chart

project metrics

Estimate capability

performance of

project metrics

Create

Pareto

charts

Conduct

MSA

Identify potential

KPIV¶s & KPOV¶s

FMEA

Create

multi-

vari charts

Determine

confidence

inter vals for key

metrics

Conduct

hypothesis

tests

Determine

variance

components

Assess

correlation of

variables

Conduct

regression

analysis

Conduct

ANOVA

Select

DOE

Plan DOE

execution

Conduct DOE

Implement variability

reduction designs/

assessments

Consider response

surface methods

Determine

control plan

Implement

control charts for

KPIV¶s

Consider short run

and three-way

control char

Consider

CUSUM, EWMA

&EPC

Consider

pre-control

charts

Mistake-

proof

processes

Phase 1 Measurement phase

Phase 2 Analysis phase

Phase 3 Improvement phase

Phase 4 Control phase

Project Implementation

Source: Forrest W Breyfogle, 1999






1.3.2. Statistics approach at Analysis phase

To illustrate theTo illustrate the

relationship betweenrelationship between

inputs and outputsinputs and outputs

To judge whether To judge whether

two population aretwo population are

identicalidentical

The inter val about theThe inter val about the

true value of thetrue value of the

parameter with someparameter with some

probabilityprobability

To investigate and modelTo investigate and model

the relationship betweenthe relationship betweena response variable anda response variable and

one or more independentone or more independent

variablesvariables

Regression Analysis

Conduct ANOVAHypothesis Test

Confidence Interval

R

5S

T

S5

3

T

3 5

5

U

5

V

5

cotton

o b s e r v a t i o n

W

oxplotsof observation bycotton

X

5Y X Y

5

95 ̀

Confidence Intervals for Siga

as

b artlett's T est

Test Statistic:c .933

P-Value : c .9d c

Levene'sT est

Test Statistic: c .3e

8

P-Value : c .863

Factor Levels

f 5

gh

g

5

3h

35

Hoi ogeneity of Variance Test for Observations

ANOVA: Analysis of variance






1.3.4. Statistics approach at Control phase

-- EWMA offer an alternative thatEWMA offer an alternative that

is based on exponentialis based on exponentialsmoothing about control chartsmoothing about control chart

-- EWMA can be combined withEWMA can be combined with

EPC to give insight into whenEPC to give insight into when

a process should be adjusteda process should be adjusted

Trend and outTrend and out--of of--

control conditions arecontrol conditions are

immediately detectedimmediately detected

Checklist / SOP

EWMA & EPC

Control Chart

Spot problem areas bySpot problem areas by

frequency of location,frequency of location,

type, or causetype, or cause

-- It¶s a mechanism that either It¶s a mechanism that either

prevents a mistake fromprevents a mistake from

occurring or makes aoccurring or makes a

mistake obvious at a glance.mistake obvious at a glance.

-- We can think is as theWe can think is as the

extension of FMEAextension of FMEA

Mistake-Proofing

EWMA: Exponentially weighted average moving

EPC: Engineer process control

SOP: Standard Operating Procedure

FMEA: Failure Modes and Effects Analysis








2.1. Implement timing 3/3

VOC

(or Benchmarking)

CTQ

(or COP)

Specs

(Standards)

Y-Measure X-Measure

VOC : Voice of Customer CTQ : Critical of Quality COP : Critical of Process

From VOC effective to operator Xs successful

Y = f(X) +Y : process performance index (based on CTQ)Y : process performance index (based on CTQ)

X : causes, factors,«X : causes, factors,«

Find the key factors Xsto improve Ys apparent (Analysis)

Find a set of parameter Xs, and use control charts for monitoring Y and Xs

,to make sure Xs are under control well and product target¶s YsControl




Champion

Executive Champion

2.2. Project team member 1/2

Black Belt

Green Belt

Six

Sigma

Office

Six Sigma Team Quality Circle




2.2. Project team member 2/2

Level Position Major task / Role

Exec. Champion CEO /MD Decision making

Champion Director/GM Implementation

Master Black Belt Process Improv. Mgr. Coaching/Guiding

Black Belt Manager Project management

Green Belt Assist. Mgr./Engineer Process improvement

Source: American Society for Quality (http://www.asq.org)International Quality Federation (http://www.iqfnet.org)Six Sigma Academy (http://www.6-sigma.com)




2.3. Six Sigma Configuration

Top manage support

Champion

DMAIC

Transfer

Register

Approval

Close

6

Committee

CEO

MBB

Master Black

Belt

Focus

Projects

( 3- 6 months)

Knowledge

Base

Financial

results

6´School´

Training

MBB10 Projects

BB3GB1

Certification

Build up´

capability

Common

Language

6/DMAIC

Process

Big Improvement

More-structured

Top down

Domain+BB+GB

Link to Bonus/

Promotion

Process Owners

GB

Members

BB

Green Belt

Black Beltdiscipline

Reward

Implementation

Leader




3. Case Study ~ Define customer requirement

Purpose

Customer Complaint

DO A rate is too high

Issues can¶t be effectively sorted before

shipping

What to change? Benefits?

Raise customers satisfactory

Reduce field DO A

Get better competitiveness

Reduce MFG cost

Deliver Smoothly

Quality

Improvement




3. Case Study ~ Build up organization

Committee

Ben Cheng

Black Belt Arno Peng, Allen Lin

Penny Chen, John Lu

Process Improvement

ProjectTest Improvement

Project

QA Improvement

Project

Champion

Beer Chen

Owner: J.L. KuMember:DIP ± Chunyi ChiangSMT ± Seven ChengAOI ± Powell LinATE ± F.M. LinPD ± Cheryl Su

Owner: Y.S. Yang

Member:

TE ± Carl Chen

PD ± Fay Wang

ATE ± F.M. Lin

Owner: Arno Peng

Member:

QA ± Mark Yang

SQM ± C.M. HSiao




3. Case Study ~ Define - Core process

Load

PCB

Screen

Printer High Speed

Mounter

N2

Reflow

AOI

Inspection

Vision

Inspection

ICT

Inspection

Manual

Insertion

Wave

Solder

Touch

Up

ATE

Inspection

Function

Test

Process Check Point

Put solder

paste on

PCB

Place SMT

component on

PCB

Solder

component

and PCB

Insert DIPcomponent

on PCB

Solder component

and PCB

Inspection &repair

GR&R C pk, run chart

DOE

Enhance analysis tool

Enhance analysis tool Enhance

analysis

tool




3. Case Study ~ Measure - SPP Gauge R&R 1/2

Gauge R&R testTo judge the capability of measure system

2222 variancevariancevarianceheightof variance lityreprodcibiityrepeatabil product total !

Reduce gauge variability

Make sure that appraisers endure gauge variability

Gauge variability could come from two parts

± One is operator variability (Reproducibility)

± The other is inspection machine variability (Repeatability)






3. Case Study ~ Measure - Reflow process capability 1/2

Cpk study ( To estimate process capability

Could know process / equipment¶s capability

Eliminate or greatly reduce product variability

Process capability index including:

± Ca: The index of gap between target & mean ± Cp: The index of evaluate quality characteristic's variance

Evaluate process capability




3. Case Study ~ Measure - Reflow process capability 2/2

Histogram for each quality characteristic Run chart for monitoring

a. Engineer can monitor quality characteristic continuously

b. If data increase or decrease steadily, it could be the warning alarm

c. Understand each location¶s distribution

d. If close upper/lower process Spec., Engineer need to study it

Benefit / Advantage




3. Case Study ~ Analysis ~ Enhance analysis tool 1/2

Old Daily analysis report New Daily analysis report

AOI, VI, ICT, ATE, F/T, OOB

PS: Daily analysis work for

Process check point




3. Case Study ~ Analysis ~ Enhance analysis tool 2/2

Bar chart for monitoring D.R. Bridge chart for action effectively

Tracking chart for monitoring cause Pareto diagram for top issue




3. Case Study ~ Improvement - Wave-solder DOE 1/3

Purpose

Use DOE (Design of Experiment) to improve process

Optimize Parameter for Wave-Solder Machine




3. Case Study ~ Improvement - Wave-solder DOE 2/3

0 1 2 3 4 5

D

AC

C

AB

AD

A

B

Pareto Chart of the Effects(response is Total de, Alpha = .10)

A: Flux VolB: Conveyor C: Height oD: Pre-heat

8 0 1 0 0

8 0 9 0 3 2 5

3 3 5

12

17

22

12

17

22

12

17

22Flux Volu v e

Conveyor spe

Height of so

Pre-heat zon

28

32

80

100

80

90

Interaction Plot (data eans) for Total defect

Critical factors Factor B (Conveyor speed)

Factor A (Flux volume)

Factor AD (Flux volume,Pe-heat zone temp.)

Factor AB (Flux volume,Height of solder wave)






3. Case Study ~ Control - Line qualification 1/2




3. Case Study ~ Control - Line qualification 2/2




3. Case Study ~ Result - Yield rate improvement 1/2

95 %68 %




3. Case Study ~ Result - Yield rate improvement 2/2

68 %

42 %

67 %

85 %

74 %

50 %




3. Case Study ~ Finial Benefit - Shipping Q¶ty increasing

3

3 3 3

Shi i

'

Shipping Q¶ty

Model Q¶ty

From 3 Model o 6 Model




4. Appendix

Reference book / homepage

1. American Society for Quality (ASQ) http://www.asq.org

2. Breyfogle, F.W. (1997). Im plement Si x Sig ma. 1st ed., John Wiley &

Sons, New York, NY.

3. GE company http://www.ge.com/sixsigma/

4. International Quality Federation (IQF) http://www.iqfnet.org

5. Montogomery, D.C. (2001). I ntroduction to Statistical Quality Control .

4th ed., John Wiley & Sons, New York, NY.

6. Pande, P.S., R.P., Neuman, and Cavanagh, R. R. (2000). The Si x Sig ma

way: how GE, Motorola, and other top com panies honing their

perfor mance. 1st ed., McGraw-Hill Companies, Inc., New York, NY

7. Six Sigma Academy http://www.6-sigma.com




Thank You !E-mail address: [email protected]

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Six Sigma Introduction & Application_031020