six sigma and its implementation

SIX SIGMA AND ITS IMPLEMENTATION ON

THE PROJECT

Six Sigma

Six Sigma

6 sigma is used by individual and organizations to:•Drive and sustain improvements•Provide rigorous alignment of actions with strategy•Guide decision making with facts and data•Meet customer needs through improved products and processes•Deliver bottom-line results

Six Sigma

Sigma Level Defect.10-6

± 1σ

± 2σ

± 3σ

± 4σ

± 5σ

± 6σ

697,700

308,700

66,810

6,210

233

3.4

Six Sigma

Six Sigma Companies

The Six Sigma Evolutionary Timeline

1736: French mathematician Abraham de Moivre publishes an article introducing the normal curve.

1896: Italian sociologist Vilfredo Alfredo Pareto introduces the 80/20 rule and the Pareto distribution in Cours d’Economie Politique.

1924: Walter A. Shewhart introduces the control chart and the distinction of special vs. common cause variation as contributors to process problems.

1941: Alex Osborn, head of BBDO Advertising, fathers a widely-adopted set of rules for “brainstorming”.

1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures for Performing a Failure Mode Effects and Criticality Analysis.

1960: Kaoru Ishikawa introduces his now famous cause-and-effect diagram.

1818: Gauss uses the normal curve to explore the mathematics of error analysis for measurement, probability analysis, and hypothesis testing.

1970s: Imai develop Dr. Deming concept called 14 keys of Deming or called kaizen in Japanese.

1986: Bill Smith, a senior engineer and scientist introduces the concept of Six Sigma at Motorola

1994: Larry Bossidy launches Six Sigma at Allied Signal.

1995: Jack Welch launches Six Sigma at GE.

CustomerCompetitive Price

High Quality ProductsOn-time Delivery, etc

Company Profitability

Repeat BusinessGrowth/Expansion

Cash !!Cash !!$

Value !!Value !!

Some ProfitSome ProfitBigger ProfitBigger Profit

1

2

31

2

3

Price - Cost = ProfitPrice - Cost = Profit

Price to Sell

Price to Sell

Cost to ProduceCost to Produce

KEY BUSINESS CONCEPT OF SIX SIGMA

Six Sigma Methods Production

DesignService

Purchase

HRM

Administration

QualityDepart.

Management

M & S

IT

Where can Six Sigma be applied?

COPQ against sales revenue

COPQ (Cost of Poor Quality)

Sigma Level DPMO COPQ as sales percentile

1-sigma 691.462 (very low competitive) N/A

2-sigma 308.538 (Average Indonesia’s Industry) N/A

3-sigma 66.807 25-40% of sales

4-sigma 6.210 (Average USA’s Industry) 15-25% of sales

5-sigma 233 (Average Japan’s Industry) 5-15% of sales

6-sigma 3,4 (World Class Industry) < 1% of sales

$600$500$450$380$200

$2500

$1200

$700

$170

Cost Benefit

1996

Cost Benefit

1997

Cost Benefit

1998

Cost Benefit

1999

Cost Benefit

2000

6 Sigma Cost6 Sigma ProductivityDelighting Customers

SUCCESS STORY IN SIX SIGMASUCCESS STORY IN SIX SIGMA General ElectricGeneral Electric

$2500

$3.0B

$0.5B

$2.5B$2.5B

Dupont Chronology

Periode Description Sigma

Before six sigma Dupont Total Cost of Poor Quality= 20 -30 % of revenue

About 3 sigma

Implementing Cost of Implementing Six Sigma = $ 20 million

-

1999 Q1 Pilot Project Six Sigma on Specialty Chemicals started-Revenues $ 1.5 Billions-Target $ 80 million savings (5% dari revenues)

1999 Q2 40 Black Belts done for training and start for project

1999 Q4 Total saving $ 35 million (initial target $ 25 million) 2.3% of Revenue17.7% COPQ4 Sigma

2000 Q4 Total saving $ 100 million (initial target $ 80 million) 6.7% of Revenue13.3% COPQ5 sigma

Difficult-to-Reach FruitDesign for Six Sigma (DFSS)

Middle FruitSix Sigma tools

Lower Fruit7 Basic Tools of QC

Ground FruitLogic and Intuition

66σσ Basic Concept Basic Concept

3 sigma level company 6 sigma level company

• <25∼40% of sales is failure cost. • 5% of the sales is failure cost.

• Having 66,807 defects per million. • 3.4 defects per million.

• Depends on the detect to find

defect.

• Focusing on process not to produce

defects.

• Believes that high quality is expensive. • Realizes that high quality creates

low cost.

• Not available of systematic

approach.

• Uses know-how of measurement,

analysis, improvement & control.

• Benchmarking against competing

companies.

• Benchmarking to the best

in the world.• Believes 99% is good enough.

4 sigma Level? 1misspelled word per 30pages of newspaper.

5 sigma Level? 1misspelled word in a set of encyclopedias.

6 sigma Level? 1misseplled word in all of the books contained in a small library.

• Define CTQ’s internally.

• Believes 99% unacceptable.

66σσ Basic Concept Basic Concept

• It is important to understand the difference between accuracy and precision• Sigma is a measure of variationvariation (the data spread)

• It is a statistical measure unit displaying a process capability and the

measured sigma value is expressed by DPU(Defect Per Unit), PPM

• It is said that the process with higher sigma value is the process having smaller

defects

• The more increase the sigma value, the more decrease the quality cost and

Cycle Time

The concept of sigma

1σ

μ USL

3σT

Inflection Point

: The size or a standard deviation shows the distances between the inflection point and the mean. We could say the process has 3 sigma capability if 3 deviations are fit table between the target and the specification limit.

Understanding Basic Concept of Statistics

What does variation mean?

• Variation means that a process does not produce the same result (the “Y”)

every time.

• Some variation will exist in all processes.

• Variation directly affects customer experiences.

Customers do Customers do notnot feel averages! feel averages!

-10

-5

0

5

10

15

20

Measuring Process Performance

• Customers want their pizza delivered fast!

• Guarantee = “30 minutes or less”

• What if we measured performance and found an average delivery time of 23.5 minutes?– On-time performance is great, right?– Our customers must be happy with us, right?

The pizza delivery example. . .

How often are we delivering on time?Answer: Look at the variation!

• Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening.

s

x

30 min. or less

0 10 20 30 40 50

The pizza delivery example. . .

Reduce Variation to Improve Performance

• Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).

s

x

30 min. or less

0 10 20 30 40 50

How many standard deviations can you “fit” within customer expectations?

SIX SIGMA Basic Concept

• All work occurs in a system of interconnected processes

• Variation exists in all processes

• Understanding and reducing variation are the keys to improving customer satisfaction and reducing costs

Y = f(χ)Question 1), Which one should we focus on the Y or X?

Question 2), Is needed to test and audit Y continually if the X is good?

• Y

• Dependent Variable

• Output

• Effect

• Symptom

• Monitor

• X1 … Xn

• Independent Variable

• Input

• Cause

• Problem

• Control object

6 Sigma activity is concerned about the problem happened(in the sector of

manufacturing and non manufacturing). They could be improved by focusing

the factor which causes the problem.

Steps Activity

Measurement

4. Understanding process capability for ‘Y’

5. Clarifying measurement method of ‘Y’

6. Specific description of Target object for improving

against ‘Y’

Focus

Y

Y

Y

Analysis 7. Clarifying Target for improving ‘Y’

8. Clarifying factors which affect ‘Y’

Y

X1 .... Xn

Improvement9. Extract the vital few factors through screening

10. Understanding correlation of vital few factors

11. Process optimization and confirmation experiment

X1 .... Xn

Vital Few X1

Vital Few X1

Control12. Confirm measurement system for ‘X’

13. Selection method how to control vital few factors

14. Build up process control system & audit for vital few

Vital Few X1

Vital Few X1

Vital Few X1

6 Sigma activity with 5 steps of D-M-A-I-C, will pass through the major 14 steps. 6 Sigma activity have D-M-A-I-C process breaking down the problem through the condition analysis, finding

the potential causal factor , and improving the vital few factors After the condition identification, we have the first action about the part being improved at first, and then we

proceed continually the improvement activity at the next step.

Define1. Clarifying improvement target object.

2. Forecasting improvement effect.

3. CTQ selection for products and process. Y

The Approach of 6 Sigma Step

6 Sigma Roles6 Sigma Roles

SIX SIGMA CHALLENGES

• Six sigma less suitable for innovation.• Six sigma emphasize process and cost,

while innovation constitutes something new in which cost consuming.

• Six sigma only analyzing quantitative data, qualitative data must be converted to quantitative.

Six Sigma DMAIC Process

Develop Charter and Business Case

Map Existing Process

Collect Voice of the Customer

Specify CTQs / Requirements

Measure CTQs / Requirements

Determine Process Stability

Determine Process Capability

Calculate Baseline Sigma

Refine Problem Statement

Identify Root Causes

Quantify Root Causes

Verify Root Causes

Institutionalize Improvement

Control Deployment

Quantify Financial Results

Present Final Project Results and Lessons Learned

Close Project

Select Solution (Including Trade Studies, Cost/Benefit Analysis)

Design Solution

Pilot Solution

Implement Solution

Define

Measure

Analyze

Improve

Control

DMAIC = Define, Measure, Analyze, Improve and Control

66σσ Methodology Methodology

DefineDefine

Defining the “Project Y”

Translate the external CTQ’s into internal product requirements or “Project Y”.

Example:

Project YCTQVoice of theCustomer

The range must heat to the setting chosen

The refrigerator must stay dry

Call-takers must be available to answer calls

Answer rate(% of incoming calls

answered within 20 seconds)

Call-takers must answer 95% of all incoming calls

within 20 seconds (telephone promptness)

Calibration angleof the thermostat

A thermostat setting of 350° must result in a

350° oven cavity

Foam densityNo sweat

Process MappingProcess Mapping

Start Finish

1 2

Process Targets and SpecificationsExperimental

Input ParametersTarget Upper Spec. Lower Spec.

Y = f (X)

(SOP ) = Standard Operating Parameters( N ) = Noise Parameters( X ) = Controllable Process Parameters

Add the operating specification and process targetsFor the controllable variable input

Courtesy of Daraius Patell

Continue to ask “Why?” until you Reach the Root Cause…...

Structure TreeExample

RPM

Losses

Inductance

OD

Core length

STATOR

ASSEMBLY

ROTOR

Electromagnetic

Mechanical

Area A

Area B

Lamination

Endrings

Cause & Effect DiagramCause & Effect Diagram•The final diagram will look like a fishbone with the backbone displaying every known

variable (Measurement, Method, Machine, People, Materials, Environment).

Measurement Method Machine

People Materials Environment

33

Why-why

Diagram

Measurement Measurement Determine Process Capability for Determine Process Capability for

Project YProject YDetermining process capability for your "Project Y" allows you to do several important things.

– Establish a baseline for comparing the improvement of your product or process.

– Quantify the ability of your process to produce output that meets the performance standard.

– Determine if there is a technology or control problem.

– Understand process capabilities for the design of future processes for DFSS (Design for Six Sigma) projects.

– Compare your process with others (internally and externally) to judge relative performance.

• Define the problem in mathematical terms

• Predict probability of producing defects

Measurement Measurement

Determine Current Sigma LevelDetermine Current Sigma Level

Used to break down problem into manageable groups to identify root cause

or area of focus.

Process for creating a Structure Tree:• List your problem statement on the left hand side of the page.

• Break the problem down into causes by asking ‘Why?’ and record on tree branches. Typical categories of causes include:

Technical TransactionalManpower PeopleMachine PriceMaterial ProductMethod PromotionMeasurement Physical Distribution

• Assign a High, Medium or Low impact to each branch and select the highest impact branch.

• Continue breaking down by asking ‘Why?’ until you reach the root cause.

Structure Tree

What is a “Measurement System”?

- Everything associated with taking measurements: the people, measurement tool, material, method

and environment is known as --

Think of the “Measurement System” as a sub-process that can add additional variation to measurement data. The goal is to use a measurement process that has the smallest

amount of measurement error as possible.

ObservationsMeasurementsData

Inputs Outputs Inputs Outputs

-- The “Measurement System”.

Parts

More Frequently Asked Questions More Frequently Asked Questions About Measurement DataAbout Measurement Data

ObservedProcessVariation

Actual Process Variation

Measurement Variation

Long TermProcess Variation

σ lt

Short TermProcess Variation

σst

Within Sample

Variation

Variation due to

Measurement Equipment

Variation due to

Operators

Accuracy Linearity ReproducibilityStabilityRepeatability

Sources of Measurement System VariationSources of Measurement System Variation

The Gage R&R methods we will study in this class will provide estimates of the total measurement variation, the variation attributed to measurement equipment repeatability and the variation attributed to the appraisers.

Acceptable if less than 20%

Conditional if between 20% to 30%

Unacceptable if greater than 30%

Evaluation Criteria for Evaluation Criteria for %GR&R and %Study Variation%GR&R and %Study Variation

Beware of the risk associated with using data acquired from an unacceptable

measurement process.

Beware of the risk associated with using data acquired from an unacceptable

measurement process.

σ2gage = σ2

repeatability + σ2reproducibility

Repeatability: The variation in measurements taken by a single person or instrument on the same or replicate item and under the same conditions.Reproducibility: the variation induced when different operators, instruments, or laboratories measure the same or replicate specimen.

AnalyzeAnalyze

Hypothesis Test (for variables)

Hypothesis Test (for attributes)

CorrectDecision

CorrectDecision

Type 1Error

α

Type 2Error

β

Ho Ha

Ho

Ha

True

Accept

The ratio which isbeing “Ha” even if it’s false.

Where “β” is usually set up at 10%.

The ratio which isbeing rejected Ho even

though certain thing is true where “ α” is α error.

(usually 5%)

*Ho(Null Hypothesis) is assumed to be true. This is like the defendant being assumed to be innocent.

Ha(Alternative Hypothesis is alternatives the Null Hypothesis. Ha is the one that must be proved.

Data Types

Variable Discrete

◎ t-Test (Compares means less than 2 population)

◎ ANOVA (Compares variances more than 2 population)

◎ F-Test (Compares variances of two population)

◎ Chi Square (Compares counts and frequencies.)

Before t-Test/ANOVA, confirm thehomogeneity of variance conductingF-Test

the gap delta(δ)

The larger Means and expected gap is getting,the more different two variances of average in population.

T

• The tool depends on the data type. We use ANOVA when we have categorical input(s) and a continuous response.

Continuous Categorical

Ca

teg

ori

ca

l C

on

tin

uo

us

Dependent Variable (Y)

Ind

epe

nd

en

t V

aria

ble

(X

)

Regression

ANOVA

LogisticRegression

Chi-Square (χ2)Test

Variance Homogeneity Testing Means Testing

1.One population variance testing

2.Two population variances testing

3.Testing of population variances for more than two (Normal distribution)

4. Testing of population variances for more than two (Non-Normal distribution)

Chi Square

F

Homogeneity ofVariance▶Bartlett’s Test

Homogeneity ofVariance▶Levene’s Test

1.One population Mean testing

1) When we know σ of the population 2) When we know σ of the population

2.Two population Mean testing

1) When they know σ1 and σ2

2) When they don’t know σ1 and σ2

① σ1 = σ2

② σ1 ≠ σ2

Normal distribution( 1 - Sample Z )

T distribution( 1 - Sample t )

Normal distribution

T distribution( 2 - Sample t )

The type of Hypothesis

What is DOE ?

• DOE is more than just a statistical DOE is more than just a statistical technique. technique.

• It is the combination of effective planning, It is the combination of effective planning, discipline, subject matter knowledge and discipline, subject matter knowledge and statistical methods that make the statistical methods that make the experiment a success.experiment a success.

ImprovementImprovement

DOE Steps

1.1. Define the objective of the experiment.Define the objective of the experiment.

2.2. Select the response and input factors.Select the response and input factors.

3.3. Determine the resources required.Determine the resources required.

4.4. Select suitable experiment design Select suitable experiment design matrix and analysis strategy. matrix and analysis strategy.

5.5. Perform the experiment and record Perform the experiment and record data.data.

6.6. Analyse the data, draw conclusions, Analyse the data, draw conclusions, and perform confirmation runs.and perform confirmation runs.

Good Good

planning is planning is

critical to critical to

success!success!

Basic Concepts in DOEBasic Concepts in DOEPressurePressure

SpeedSpeedProcessProcess

Quality Characteristic (Y)Quality Characteristic (Y)

++--

Pr

118Ave +

58Ave -12++901046--701034-+905210+-7051

YPr

x SpdSpdSpdPrRun

79 Y = 8

2 0 6

y = 8 + Pr + 3 x ( Pr Spd)y = 8 + Pr + 3 x ( Pr Spd)^̂

ProcessProcess

Process Process ModelModel

DO

ED

OE

FMEAFMEAFailure Modes and Effects Analysis is a systematic method for identifying, analyzing, prioritizing, and documenting potential failure modes and their effects on a system, product, or process.

SAMPLES

A B C D E

· Controllable factors

- Assignable causes

- Adjustable

- Special

· Uncontrollable factors

- Common causes

- Noise

- Inherent causes

• SPC has been traditionally used to monitor and control the output of processes.

In this application, we are measuring the dimensions of finished parts or

characteristics of finished assemblies.

• Six Sigma Quality focuses on moving control upstream to the leverage input characteristic for Y. If we can

measure and control the vital few X’s, control of Y should be assured.

INPUT

Controller α/2

α/2

X

Lower Control Limit

Upper Control Limit

ProcessCapability

Desired Output

OUTPUTPROCESS

The Logic of SPC(Statistical Process Control)?

L M N O P

10050Subgroup 0

0.5

0.0

-0.5

Sam

ple

Mea

n

Mean=0.001188

UCL=0.4384

LCL=-0.4360

1.0

0.5

0.0

Sa

mp

le R

ang

e

11 1

R=0.2325

UCL=0.7596

LCL=0

Xbar/R Chart for Sealing Angle Line #2

Control Control

Types of Control Charts Types of Control Charts

Variables Charts for monitoring continuous X’s

• Average & Range X bar & R n ＜ 10 typically 3~5

• Averages & Std Deviation

X bar & σ

n ≥ 10

• Median & Range X & R n ＜ 10 typically 3~5

• Individual & Moving Range XmR n = 1

Attributes Charts for monitoring discrete X’s

• Fraction Defective P Chart typically n ≥ 50 tracks DPU/DPU

• Number Defective np Chart n ≥ 50 (constant) tracks # def

• Number of Defects

c Chart

c ＞ 5

• Number of Def/Unit

U Chart

n variable

• In order to select the appropriate control chart for monitoring your process, first

determine if your key process variables (X’s) are continuous or discrete. There are

control charts for both continuous data and discrete data.

Control Chart

12

10

8

•

••

•

•

••

•

••• ••

••

•

••

•

•

Week

Upper Control Limit = Ave + 3 x Std Dev14

13

7

6Lower Control Limit = Ave - 3 x Std Dev

Central Line = Average

Note: Control limits should be established using subgroup standard deviation

Six Sigma DMAICImplementation Project

Example

Date …………………

Department / TeamPrepared : …………..

Project title

G Manager Pres. Dir.Director

Mgr.

6σ

Ch

am

pio

n R

evie

wFin

al R

ep

ort

Contents

1. Define Step 2. Measure Step3. Analysis Step4. Improvement Step5. Control Step

-Attachment

Define Step

PJT Name

Perio

d

TeamNameDiv./Dept:

Breakthrough KPI Current World Best Target

Main Improvement Object

Team Formation (Related Department Involved)

Name Dept. Level Role

Quantitative

Qualitative

Expected

Results

How to do ?Why ?(* Selection Background)

New Idea for Target Achievement

Engineer

Ap

pro

val

Ka Part Ka Group Project Registration

Neck Point

CIAMD

4500

5000

Current Current Target Target Unit: (Nm3/day)

11%

How to do:

Target Saving cost:

OthersElec

tricO2N2LNG

0.00700.06050.12900.13090.1650 1.412.326.226.633.5

100.0 98.6 86.3 60.1 33.5

0.5

0.4

0.3

0.2

0.1

0.0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cent

Cou

nt

Energy Usage Price

Expected resultBackground

General Background

Start Finish

1 2

Process Targets and SpecificationsExperimental

Input ParametersTarget Upper Spec. Lower Spec.

Y = f (X)

(SOP ) = Standard Operating Parameters( N ) = Noise Parameters( X ) = Controllable Process Parameters

Add the operating specification and process targetsFor the controllable variable input

Process Mapping

CIAMD

SIPOC – Suppliers, Inputs, Process, Outputs, Customers

You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements.

Pro

ces

s U

nd

erst

and

ing

CIAMD

Measure Step

F(x) Machine

Man

Big Y X1 X2 X3

Brainstorming Potential X’s List

Material

Material

CIADM

Sampling

Training

Gage R&R

Result

Conduct a training……How to see what kind …….. Defect that happened in process.Purpose : find out the operator ability.

Conduct Gage R&R for 4 men to know the judgment capability (in different times & do not know the inspection result of each other) --> Repeat 2 times for each persons

% Gage R&R : 0 %

acceptable

Date:………

Collected 12 ea Sampling

Observed ProcessVariation

Measurement Process Variation

Sample M a nM a nMachineMethodMaterial

To determine if the measurement error is To determine if the measurement error is small and acceptable relative to the small and acceptable relative to the

process variation, we can process variation, we can use Gage R&R study.use Gage R&R study.

Gage R & R

CIADM

Units of Measureµµ

Center of the bar

Smooth curve interconnecting the center of each bar

Process CapabilityCurrent Condition

1 2 3 4 5 6

1.0

0.5

1.5

2.0

2.5

Z Sh

ift

Proc

ess

Con

trol

Good

Poor

Technology

GoodPoor

Block A

Block C

Block B

Block D

Four Block Diagram

Z Shift

A : Poor control, inadequate technology B : Must control the process better, technology is fineC : Process control is good, inadequate technologyD : World class

Current Target

CIADM

Factor Detail Analysis PlanSchedule

Mar 2nd Week Mar 3rd Week Mar 4th Week

X1.3

X1.1 Inspect correlation between “Y” and inspector for each group

Analysis khole / dimensionHeight, diameter, angle etc,

X1.2

X1.4

Analysis Plan

CIADM

Analyze Step

CID MA

Regression Analysis: Angle Value versus Rotate Gear

The regression equation is

Angle Value = - 0,380 + 3,74 Rotate Gear

Predictor Coef SE Coef T P

Constant -0,3796 0,1693 -2,24 0,042

Rotate G 3,744 1,153 3,25 0,006

S = 0,1510 R-Sq = 43,0% R-Sq(adj) = 38,9%

Analysis of Variance

Source DF SS MS F P

Regression 1 0,24032 0,24032 10,55 0,006

Residual Error 14 0,31905 0,02279

Total 15 0,55937

Unusual Observations

Obs Rotate G Angle Va Fit SE Fit Residual St Resid

2 0,230 0,4000 0,4815 0,1070 -0,0815 -0,77 X

7 0,130 0,5000 0,1071 0,0407 0,3929 2,70R

Regression Analysis: Angle Value versus Rotate Gear

The regression equation is

Angle Value = - 0,380 + 3,74 Rotate Gear


Constant -0,3796 0,1693 -2,24 0,042

Rotate G 3,744 1,153 3,25 0,006

S = 0,1510 R-Sq = 43,0% R-Sq(adj) = 38,9%


Source DF SS MS F P

Regression 1 0,24032 0,24032 10,55 0,006

Residual Error 14 0,31905 0,02279

Total 15 0,55937


Obs Rotate G Angle Va Fit SE Fit Residual St Resid

2 0,230 0,4000 0,4815 0,1070 -0,0815 -0,77 X

7 0,130 0,5000 0,1071 0,0407 0,3929 2,70R

-0,2 -0,1 0,0 0,1 0,2 0,3 0,4

-1

0

1

2

Nor

mal

Sco

re

Residual

Normal Probability Plot of the Residuals(response is Angle Va)

Motor

Gear RotationGear Rotation

Use regression is to express and analyze a mathematical equation of describing a relationship.

That is, it is to fit a mathematical equation of describing a relationship between the “YY” and “X’sX’s”.

One-way ANOVA: Gr.A - 3, Gr.A - 4, Gr.B - 3, Gr.B - 4, Gr.C - 3, Gr.C - 4Analysis of Variance

Source DF SS MS F P

Factor 5 0.8761 0.1752 2.59 0.030

Error 102 6.8897 0.0675

Total 107 7.7659

Individual 95% CIs For Mean

Based on Pooled StDev

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

Gr.A - 3 18 0.0572 0.3029 (-------*-------)

Gr.A - 4 18 -0.1217 0.2427 (-------*-------)

Gr.B - 3 18 0.0839 0.2231 (--------*-------)

Gr.B - 4 18 -0.1183 0.2403 (-------*-------)

Gr.C - 3 18 0.0156 0.2796 (-------*-------)

Gr.C - 4 18 0.0967 0.2626 (-------*--------)

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

Pooled StDev = 0.2599 -0.15 0.00 0.15

P-Value: 0.242A-Squared: 0.452

Anderson-Darling Normality Test

N: 18StDev: 0.302865Average: 0.0572222

0.50.0-0.5

.999

.99

.95

.80

.50

.20

.05

.01

.001

Pro

bab

ility

Gr.A - 3

Normal Probability Plot

See from sealing angle specifications there’s no problem, cause all operator adjustmentStill in range (-0.5o ~ 0.5o). But there’s a significant effect both of them seeing by characteristic variation result, each operator have a different mean adjustment.

Since p-value < 0.05;Ho (reject), Ha (accept). That is we can claim there’sa difference between the levelOf adhesive

Gr.

C -

4

Gr.

C -

3

Gr.

B -

4

Gr.

B -

3

Gr.

A -

4

Gr.

A -

3

0.5

0.0

-0.5

Boxplots of Gr.A - 3 - Gr.C - 4(means are indicated by solid circles)

UCL

LCL

ScreenManual Adjustment

Operator Adjustment

Target Line

We represent characterized variation “Y”“Y” by the total sum of square, then this method is to find

what the factor’s level which influence enormously is, comparing both of them.

CID MA

CID MA

Factor Detail Analysis Content Result ConclusionAnalysis Purpose

Selected as Vital “X”

P = 0.000


X1.1 Find most effected to “ Y “

X1.2

Find most effected to “ Y “

P = 0.000


Bottle Neck

Regression, to compare Dew Point and Purge Flow rate

X1.3

X1.4 Regression, to compare Dew Point and Out Air Temperature



ANOVA, to compare Dew Point and Heating Time

ANOVA, to compare Dew Point and Drying Time

P = 0.000

P = 0.003

Analysis Result

Improve Step

CD M AI

Design of Experiment

Full Factorial Design

Factors: 3 Base Design: 3, 8Runs: 8 Replicates: 1Blocks: 1 Center pts (total): 0

Drying TIme

Flow rate

10 hour

6 hourHeating Time

12 hour

4 hour

520 m3/hr

Level 2Level 1Factors

810 m3/hr

The Improve phase identifies a solution and confirms that the proposedSolution will meet or exceed the improvement goals of the project.StdOrder RunOrder CenterPt Blocks Flow rate Heating Time Drying Time Result

8 1 1 1 810 6 12 -90.2

5 2 1 1 600 4 12 -52.6

1 3 1 1 600 4 10 -56.4

7 4 1 1 600 6 12 -57.6

2 5 1 1 810 4 10 -89.2

3 6 1 1 600 6 10 -68.1

6 7 1 1 810 4 12 -85.3

4 8 1 1 810 6 10 91.3

Heating Time : 4 hour

Drying Time : 10 hour

Flow rate : 520 m3/hour

Optimize Condition:

CD M AI

-40-50-60-70-80

LSL USLProcess Data

Sample N 24StDev (Within) 4.29772StDev (Ov erall) 8.60376

LSL -80.00000Target *USL -40.00000Sample Mean -64.18750

Potential (Within) Capability

CCpk 1.55

Ov erall Capability

Z.Bench 1.81Z.LSL 1.84Z.USL 2.81Ppk

Z.Bench

0.61Cpm *

3.68Z.LSL 3.68Z.USL 5.63Cpk 1.23

Observ ed Performance% < LSL 0.00% > USL 0.00% Total 0.00

Exp. Within Performance% < LSL 0.01% > USL 0.00% Total 0.01

Exp. Ov erall Performance% < LSL 3.30% > USL 0.25% Total 3.55

WithinOverall

Process Capabi l i ty of Dew Point

-40-50-60-70-80

LSL USLProcess Data

Sample N 24StDev (Within) 4.29772StDev (Ov erall) 8.60376

LSL -80.00000Target *USL -40.00000Sample Mean -60.00000


CCpk 1.55

Ov erall Capability

Z.Bench 2.05Z.LSL 2.32Z.USL 2.32Ppk

Z.Bench

0.77Cpm *

4.51Z.LSL 4.65Z.USL 4.65Cpk 1.55

Observ ed Performance% < LSL 0.00% > USL 0.00% Total 0.00

Exp. Within Performance% < LSL 0.00% > USL 0.00% Total 0.00

Exp. Ov erall Performance% < LSL 1.00% > USL 1.00% Total 2.01

WithinOverall

Process Capabi l i t y of Dew Point

1 2 3 4 5 6

1.0

0.5

1.5

2.0

2.5

Z Sh

ift

Proc

ess

Con

trol

Good

Poor

Technology

GoodPoor

Block A

Block C

Block B

Block D

Four Block Diagram

Z Shift

Current Target

Improvement Result

CD M AI

4500

5000

Current Current Target Target

Result

8%

4600

Result Result

92%92%

Cost Saving: US$

Improvement Result

CD M AI

Control Step

I

ProcessInput

Controller

Controllable factors:- Miss adjust causes- Adjustable check- Pad control- Education

Group Member

Process CapabilityDesiredOutput

X

Upper Control Limit

Lower Control Limit●

●

Six Sigma Quality focuses on moving control upstream to the leverage input characteristic

for Y. If we can measure and control the vital few X’s, control of Y should be assured.

10050Subgroup 0

0.5

0.0

-0.5

Sam

ple

Mea

n

Mean=0.001188

UCL=0.4384

LCL=-0.4360

1.0

0.5

0.0

Sam

ple

Ran

ge

11 1

R=0.2325

UCL=0.7596

LCL=0

Xbar/R Chart for Sealing Angle Line #2

Output

Process Standard Change

CD M A

IC

D M A

P Chart enables us to control our process using statistical method's to signal when

process adjustments are needed.

50403020100

0.004

0.003

0.002

0.001

0.000

Sample Number

Pro

por

tion

P Chart for Stem Crack

P=0.001198

UCL=0.002466

LCL=0

15105Subgroup 0

10.2

10.1

10.0

9.9

9.8

9.7

Sam

ple

Mea

n

1

Mean=9.943

UCL=10.13

LCL=9.755

1.0

0.5

0.0

Sam

ple

Ran

ge

R=0.5594

UCL=1.016

LCL=0.1030

Xbar/R Chart for Cullet Speed

X-bar/R Chart use to control daily average for CTQ.

CTQ’s daily control data

Six Sigma ProjectExample

Date 20 MAY 2009Team : Galvanize

Prepared by : Imam Mudawam

Optimalkan pemakaian Zinc

Pada Proses Galvanize

Prod Mgr CEO.

Work Mgr

6σ

Cham

pio

n R

evie

wFi

nal

Rep

ort

Contents

1. Define Step 2. Measure Step3. Analysis Step4. Improvement Step5. Control Step

ISKDCNG

PJTName

Period

TeamName

Div./Dept: CONST./GALV.

Breakthrough

KPI Current World Best TargetMain Improvement Object

Team Formation (Related Department Involved)

Name Dept. Level Role

Quantitative

Qualitative

Expected Results



CEO

Appro

val

Work Mgr Prod Mgr

Neck Point

Optimalkan Pemakaian Zinc

Z Shift: - 1.86.

Imam M

Lukman

Banbang

Spv

Supv

Form

• Ketebalan Galvanize sesuai standard.

Amin Form Member

Leader

Leader

Member

Rp / kg

Rp / kg

Slamet Member

Fab

Galv

Galv

Galv

Qc

CIAMD

Project Registration

D

M

A

I

C

Schedule- Making Theme Reg.- Analyzing Aging Root-Caused

- Determine Potential X List- Find Current situation- Find Vital X by analyzing Potential X

-Find Improvement idea-Confirmation run

-Process Control by Monitoring Aging Amount

May – W5

Junl – W5

Jul –W5

Optimalkan Pemakaian ZincPada Proses Galvanize

Saving Zinc Rp. 295 Juta /Tahun

Galvanize

Reduce cost

Hans Ga Supv

Form

Member

Sept –W3

Sept –W5

Galvanize183.6 micr.

Galvanize130 micron

280240200160120

LSL Target USL

LSL 100Target 130USL 150Sample Mean 183.649Sample N 35StDev (Within) 18.0955StDev (Ov erall) 31.8811

Process Data

Z.LSL 2.62Z.USL -1.06Ppk -0.35Low er CL -0.49Upper CL -0.21Cpm 0.11Low er CL 0.09

Z.Bench -1.86Low er CL -2.59Z.LSL 4.62Z.USL -1.86Cpk -0.62Low er CL -0.80Upper CL -0.44

Z.Bench -1.07Low er CL -2.11

Ov erall Capability


PPM < LSL 0.00PPM > USL 885714.29PPM Total 885714.29

Observ ed PerformancePPM < LSL 1.89PPM > USL 968521.55PPM Total 968523.44

Exp. Within PerformancePPM < LSL 4348.13PPM > USL 854388.04PPM Total 858736.18

Exp. Ov erall Performance

WithinOv erall

Pr ocess Capabi l i t y of t 6 .5mm +(using 95.0% confidence)

Worksheet: Worksheet 3; 11/13/2009

ISK DC NG

General BackgroundCIAM

D

• Hasil proses Galvanize ketebalannya melebihi standar yang ditentukan dalam ASTM- A123 / A123M.

• Tujuan Proyek ini untuk dapat mengoptimalkan ketebalan lapisan zinc pada hasil proses galvanize.

Produk tebal 6.0s/d….mm

sample mean micron 183.6 84.2 83.6Percent 52.3 24.0 23.8Cum % 52.3 76.2 100.0

Ketebalan material

Mat

erial

t.1.

5s/d

3.0m

m

Mater

ial t.

3.5s

/d6.

0mm

Mate

rial t

.6.5

s/d ..

.mm

400

300

200

100

0

100

80

60

40

20

0

sam

ple

mea

n m

icro

n

Perc

ent

Pareto Char t of Ketebalan mater ial


Big Y X1 X2 X3

Brainstorming Potential X’s List CIAM

D

Degrising

Pickling 1 & 2

Material

Konsentrasi BasaCaustic Soda

Base Metal

Konsentrasi Keasaman

Optimalkan pemakaian ZincPada Proses Galvanize

Temperatur

Waktu Pencelupan

Komposisi kimia

Ketebalan

1

Hcl

Optimalkan Pemakaian ZincPada proses Galvanize

Fluxing Konsentrasi Keasaman

Big Y X1 X2 X3

1

Temperatur

Brainstorming Potential X’s List CIAM

D

Zinc Amunium Chloride

DippingAluminium Alloy

Zinc Ingot

Temperatur

Komposisi Campuran

Waktu pencelupan

In doing gage R&R we take 2 times repeat in check

Gage R&R take from 2 Inspector who check this sample of Galvanize coating thickness in 10 chek point

Decide operator who take Gage R&R

Do Gage R&R

Change Method,measurement, etc

Analyze Result Gage R&R

Next Step

NG ; Total Gage R&R > 20%

OK ; Total Gage R&R < 20%

Measure Step - GAGE R&RCIA

MD

Gage R&R

Gage R&R < 20%Acceptable

CIAM

D

Sample pengukuran ketebalan galvanize sebanyak 2 sample dengan 30 titik pengukuran , tiap sampel diambil 15 titik pengukuran Diukur secara berurutan dan secara acak oleh dua orang operator .

The result of Gage R&R total is 14.67 % the acceptance percentage is bellow 20% (< 20 % ) , meanwhile the result of measurement between < 20 %, it accepted.

Study Var %Study Var

Source StdDev (SD) (6 * SD) (%SV)Total Gage R&R 2.0064 12.0384 14.67 Repeatability 2.0064 12.0384 14.67 Reproducibility 0.0000 0.0000 0.00 oprtr 0.0000 0.0000 0.00Part-To-Part 13.5243 81.1460 98.92Total Variation 13.6724 82.0341 100.00

Part-to-PartReprodRepeatGage R&R

100

50

0

Per

cent

% Cont ribut ion% Study Var

8

4

0Sam

ple

Ran

ge

_R=2.633

UCL= 8.604

LCL= 0

A B

110

100

90Sam

ple

Mea

n

__X=96.67UCL= 101.62

LCL= 91.71

A B

321

110

100

90

part no

BA

110

100

90

oprt r

321

110

100

90

part no

Ave

rage

AB

oprt r

Gage name: Date of study :

Reported by : Tolerance: Misc:

Com ponents of Variat ion

R Chart by oprt r

Xbar Chart by oprt r

m easure by part no

m easure by oprt r

oprt r * part no I nteract ion

OPERATOR PENGUKURAN KETEBALAN GALVANIZE


CIAM

DCurrent Condition

Rata rata ketebalan Galvanize pada produk dengan ketebalan > 6.0 mm saat ini mencapai 183.6 micron , berdasarkan data dari tgl.20 Juni 09 Sampai 27 Juni 09 ,jumlah sample 35 , alat ukur menggunakan COATING THICKNESS DIGITAL merek TIME - TYPE TT 220 .Untuk mendapatkan ketebalan sesuai target yang diinginkan grafik harus bergeser kekiri , dan harus mengurangi keteba- Pelapisan Galvanize yang sesuai standard ASTM A 123/A 123M antara ( 100 s/d 150 ) micron .

300250200150100

99

95

90

80

70605040

30

20

10

5

1

t 6.5mm +

Perc

ent

Mean 183.6StDev 31.88N 35AD 1.706P-Value <0.005

Probabi l i t y Plot Ketebalan Galvanize mater ial 6 .5mm s/ d .......dstNormal - 95% CI


KETEBALAN GALVANIZE

280240200160120

LSL Target USL

LSL 100Target 130USL 150Sample Mean 183.649Sample N 35StDev (Within) 18.0955StDev (Ov erall) 31.8811

Process Data

Z.LSL 2.62Z.USL -1.06Ppk -0.35Low er CL -0.49Upper CL -0.21Cpm 0.11Low er CL 0.09

Z.Bench -1.86Low er CL -2.59Z.LSL 4.62Z.USL -1.86Cpk -0.62Low er CL -0.80Upper CL -0.44

Z.Bench -1.07Low er CL -2.11

Ov erall Capability


PPM < LSL 0.00PPM > USL 885714.29PPM Total 885714.29

Observ ed PerformancePPM < LSL 1.89PPM > USL 968521.55PPM Total 968523.44

Exp. Within PerformancePPM < LSL 4348.13PPM > USL 854388.04PPM Total 858736.18

Exp. Ov erall Performance

WithinOv erall

Process Capabi l i t y of t 6 .5mm +(using 95.0% confidence)


Four Block Diagram CIAM

D

Nilai sigma level saat ini adalah – 1.86 σ, target yang ingin dicapai 4.5 σ

A B

C

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6

Z sh

iftP

roce

ss C

ontro

l

Good

Poor

Poor GoodZ st

Technology

Position of Current Condition was column C , it was mean :

PROCESS CONTROL IS GOOD,BUT TECHNOLOGY (METHOD) IS BAD

EXPLANATION

D

SIGMA TARGET

SIGMA TARGET

Sigma current – 1.86

CIA

MD

WAKTU PENCELUPAN

Optimalkan pemakaian ZincPada proses Galvanize

F(x) x

Analysis

TEMPERATUR ZINC

KETEBALAN GALVANIZE

Optimalkan pemakaian zinc pada proses Galvanize (Continuous)

Waktu Pencelupan

Y Factor (x) Type Tools

Analyze – Type of factor & Tools Using CIA

MD

CONTINUE REGRESION

Regression Analysis: Ketbln.Galva versus Wktu fluxing, Wktu.deeping, ...

* Wktu.deeping is (essentially) constant* Wktu.deeping has been removed from the equation.* NOTE * All values in column are identical.* Temp.deeping is (essentially) constant* Temp.deeping has been removed from the equation.

The regression equation isKetbln.Galvanize (micron ) = 110 - 1.17 Wktu fluxing

Predictor Coef SE Coef T P VIFConstant 109.667 3.658 29.98 0.000Wktu fluxing -1.1668 0.4555 -2.56 0.034 1.000

S = 4.13685 R-Sq = 45.1% R-Sq(adj) = 38.2%


Source DF SS MS F PRegression 1 112.32 112.32 6.56 0.034Residual Error 8 136.91 17.11Total 9 249.22


Wktu Ketbln.GalvanizeObs fluxing (micron ) Fit SE Fit Residual St Resid 1 3.0 114.00 106.17 2.43 7.83 2.34R

R denotes an observation with a large standardized residual.

Durbin-Watson statistic = 1.69422

Analysis Waktu Fluxing ( Regresion )

CIA

MD

Ketebalan Galvanize VS Waktu Fluxing

Nilai p-value < 0.05, maka Ho ditolak, yang menandakan variabel waktu Fluxing berpengaruh terhadap ketebalan galvanize.

Nilai p-value < 0.05, maka Ho ditolak, yang menandakan variabel waktu Fluxing berpengaruh terhadap ketebalan galvanize.

Regression Analysis: Ktbl . Galva versus Tmprt deepin, Wkt.deeping, ...

* Wkt.deeping is (essentially) constant* Wkt.deeping has been removed from the equation.

* NOTE * All values in column are identical.

* Wkt.fluxing is (essentially) constant* Wkt.fluxing has been removed from the equation.

The regression equation isKtbl . Galvanize (micron ) = - 98 + 0.443 Tmprt deeping

Predictor Coef SE Coef T P VIFConstant -98.4 157.1 -0.63 0.548Tmprt deeping 0.4428 0.3562 1.24 0.249 1.000

S = 6.47038 R-Sq = 16.2% R-Sq(adj) = 5.7%


Source DF SS MS F PRegression 1 64.70 64.70 1.55 0.249Residual Error 8 334.93 41.87Total 9 399.63


Analysis Temperatur Dipping ( Regresion )

CIA

MD

Ketebalan Galvanize VS Temperatur Zinc

Nilai p-value > 0.05, maka Ho diterima, yang menandakan variabel temperatur tidak berpengaruh terhadap ketebalan galvanize.

Nilai p-value > 0.05, maka Ho diterima, yang menandakan variabel temperatur tidak berpengaruh terhadap ketebalan galvanize.

Regression Analysis: Ktbalan Galvanize micron versus Waktu deeping

The regression equation isKtbalan Galvanize micron = 43.7 + 19.9 Waktu deeping

Predictor Coef SE Coef T P VIFConstant 43.674 5.279 8.27 0.000Waktu deeping 19.9082 0.6573 30.29 0.000 1.000

S = 5.96979 R-Sq = 99.1% R-Sq(adj) = 99.0%


Source DF SS MS F PRegression 1 32698 32698 917.49 0.000Residual Error 8 285 36Total 9 32983


Analysis Waktu Dipping ( Regresion )

CIA

MD

Waktu Dipping VS Ketebalan Galvanize

Nilai p-value < 0.05, maka Ho ditolak, yang menandakan variabel waktu dipping berpengaruh terhadap ketebalan galvanize.

Nilai p-value < 0.05, maka Ho ditolak, yang menandakan variabel waktu dipping berpengaruh terhadap ketebalan galvanize.

CIA

MD

Selected not Vital View

Item Content Result Remarks

Selected as Vital View

Galvanizing

P = 0.034

P = 0.024

P = 0.000

Waktu Fluxing

Temperatur Dipping

Waktu Dipping

Analysis Result ( Regression )

Selected not Vital View

Berdasarkan data diatas variabel waktu Dipping paling berpengaruh terhadap hasil ketebalan pada proses galvanize (99%)

CImprovementIAMD

• Berdasarkan hasil analisa, untuk produk dengan ketebalan 6 mm didapatkan hubungan linier antara lamanya waktu pencelupan dengan hasil ketebalan galvanize.

• Grafik di bawah ini dapat dijadikan acuan untuk menentukan tebal galvanize yang diinginkan.

HUBUNGAN WAKTU PENCELUPAN DAN KETEBALAN GALVANIZEPADA PRODUK DENGAN KETEBALAN 6 mm

0

1020

3040

5060

70

8090

100110

120130

140

150160

170180

190

200210

220230

240250

260

0 1 2 3 4 5 6 7 8 9 10 11

Waktu Pencelupan (menit)

Ke

teb

ala

n G

alv

an

is (

mic

ron

)

Standar 6mm

Y = 43.7 + 19.9 x

94

CImprovementIAMD

Catatan : Proses improvement masih sedang berjalan dan akan dilaporkan kemudian hari.

Session Start : 23.01.02

□○ Theme Register

□○ Team Organization

□○ Process Map

□○ Cpk Analysis(Current)

□○ Problem Description

□○ CTQ Selection

□○ Measurable Y Value

Selection

□○ 4 Block Diagram

□○ Brainstorming

□○ Logic Tree Analysis

□○ Analysis by Minitab

□○ Process Benchmarking

□○ CTQ Selection

□○ Process Map

□○ ANOVA

□○ Regression Analysis

□○ Factor Level Decision

□○ DOE

□○ Statistical Interpretation

□○ Data Gathering & Analysis

□○ Main Factor Analysis

□○ Hypothesis Test

□○ ANOVA

□○ Control Chart

□○ Rational Tolerance

selection

□○ Document Control plan

□○ Training Process Controller

□○ CTQ Process Monitoring

System set up

Session Finished :

□○ Y Identification

□○ Gauge R&R

□○ 4 Block Focus(Zst & Zshift)

□○ Problem analysis

reaffirmation

□○ Statistical skill of Y

□○ Graphical skill of Y

□○ Gap Analysis

□○ The 1st improvement of

X(Factor)

□○ Conclusion(the fixed X

factor)

□○ Test plan

□○ Control Plan

Implementation

□○ CTQ Process Monitoring

System Build-Up

□○ Double check of all the

problems

Measurement Analysis Improvement Control

Session Start : Session Start : Session Start :

Main Schedule

Session Finished : Session Finished : Session Finished :

Measure Measure Define Define Improve Improve Control Control Analyse Analyse

A period of time taken off by an employee which is neither planned or authorised

Definition Of Defect

DEFINITION

Sickness

Unauthorised absence

Lateness

DEFECT TYPES

Payment for overtime to cover absence = £800,000:00 paLoss in revenue due to lost production = £700,000:00 pa (est)Adverse morale issuesDeterioration in productivity and quality

VALUE


Absence Logic Tree

Absence

Management Style

Time With Company

Sex

=CTQ’s

Age

Guidelines

Team Leader

Section

Accidents

Morale issues

Cleanliness

Repetitive Work

Employee

Target Setting

Environment

Method

Consistent Approach

Use Of Policy

Shift Pattern


Graph shows reduction in absence, since the beginning of the project (DEC) spotlight effect has reduced absence. One problem we do have is the calculation of absence data there is a difference between the HR data collection method and the tube plant data collection method.

Sigma Level Calculation

Using The Percentage Defective (4.605%) We Calculated The DPMO Since Merger

DPMO = 46050

Using statistical tables the SIGMA LEVEL = 3.21


-2

0

2

4

6

8

Tube 4.27 4.66 5.23 4.7 5.01 5.34 3.86 3.13 2.75

HR 4.31 5.22 5.86 4.56 5.09 5.02 3.97 3.45 2.72

Variance -0.04 -0.56 -0.63 0.14 -0.08 0.32 -0.11 -0.32 0.03

Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

VOC - Harp Questionnaire

219 People Questioned Across The Tube Plant

Answered by all employees.Name Marital StatusClock Number AgeDepartment ChildrenShift HomeTime with Company Job positionTime in current job

Have you taken any unauthorised time off since 5th July 2001? YES / NODo you currently have any warnings that relate to sickness or absence? YES / NODo you understand LGPDW's absence and sickness policy? YES / NODo you understand the affect of absence on our business? (e.g. cost, pressure on colleagues)

Only to be answered by employees with no absence historyHow do you think high absence levels affects your ability to do your job?What do you think of LG. PHILIPS Displays as an employer? Are you happy working for LGPDW?

Ask employee all questions below, and mark the score they give ie strongly agree = 10, agree = 5 and strongly disagree = 0.Do you think absence is due to Management style?Do you think absence is due to accidents in work?Do you think absence is due to the current shift pattern?Do you think absence is due to your working environment?If I was late for work, I would take the rest of the shift off because it still counts as an absenceAre there any other reasons that you think contribute to absence that are not listed above?


Overall catchment area

Main catchment area

Catchment Area Of People Questioned


Others

Job rotation

P ay rise

Music

Mgt attitude

A tt Bonus

12 5 14 33 51137

4.8 2.0 5.613.120.254.4

100.0 95.2 93.3 87.7 74.6 54.4

250

200

150

100

50

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cent

Cou

nt

How would y ou tackle high absence?

Operators

Pareto’s Of Absence Data

Teambuilding

E xtra manning

gather info on

persis tan

t absentees

1 - 1Communication

A ttendance bonus

11224

10.010.020.020.040.0

100.0 90.0 80.0 60.0 40.0

10

5

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cent

Cou

nt

How would y ou tackle absence?

Section Leaders

Conclusions

Operators would like to be paid

more to come to work

Operators see a problem in the

way they are treated by Mgt

Music might help??

Conclusions

Section Leaders see the benefit

in some form of attendance

bonus, but more emphasis on

communication and data

collection


Pareto’s Of Absence Data

Others

deaths

car problems

accidents insid

e work

low morale

injury o

utside work

flexible fl

oatdays

sickness

fludomestic

1111223444

4.3 4.3 4.3 4.3 8.7 8.713.017.417.417.4

100.0 95.7 91.3 87.0 82.6 73.9 65.2 52.2 34.8 17.4

20

10

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cent

Cou

nt

Main Reasons For Absence On Your Shif t

Others

experience

absence

poor maintenance

supply

low manning

1 1 2 3 511

4.3 4.3 8.713.021.747.8

100.0 95.7 91.3 82.6 69.6 47.8

20

10

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cent

Cou

nt

Main issues af f ecting shif ts ability to meet output targets

Conclusions

No significant patterns have

emerged

Conclusions

Absence is not a major issue


Analysis Using M System

ImproveImproveDefineDefine MeasureMeasure AnalyseAnalyse ControlControl

Time With Company

Sex

Age

Employee

NEXT STEP IS TO CARRY OUT ANALYSIS BYEMPLOYEE TO ESTABLISH IF THERE AREANY STATISTICAL DIFFERENCES

DATABASE WAS CREATED LOOKING AT THE NINE MONTH PERIOD (5th July-4th April) PRIOR TO AND DURING PROJECT

empno

Emp Namedept cd

Dept Name Grade shift noDistance Details

date_hired

Year/Month gender AgeAge

GroupCount

AbsenceAbs Occ

12345 A N OTHER1 81000 Tube Manufacturing Senior Engineer DT CF1 1AB 17/03/00 02/01 Male 34 D 0 012346 A N OTHER2 81000 Tube Manufacturing Senior Engineer DT CF1 1AB 06/10/99 02/06 Male 34 D 0 012347 A N OTHER3 81000 Tube Manufacturing Team Leader DT CF1 1AB 11/08/97 04/08 Male 33 D 3 112348 A N OTHER4 81100 Screen Production Section Leader T4 CF1 1AB 18/05/98 03/11 Male 51 H 0 012349 A N OTHER5 81100 Screen Production Section Leader T2 CF1 1AB 26/04/99 02/12 Male 42 F 1 112350 A N OTHER6 81100 Screen Production Section Leader T1 CF1 1AB 10/05/99 02/11 Male 41 F 0 012351 A N OTHER7 81100 Screen Production Section Leader DT CF1 1AB 20/04/98 03/12 Male 41 F 0 012352 A N OTHER8 81100 Screen Production Section Leader T2 CF1 1AB 06/10/97 04/06 Male 39 E 1 112353 A N OTHER9 81100 Screen Production Team Leader DT CF1 1AB 12/01/98 04/03 Male 39 E 0 012354 A N OTHER10 81100 Screen Production Technician DT CF1 1AB 06/10/97 04/06 Female 35 D 0 0

Time with Company Personal DetailsEmployment Details Unauthorised Absence

Analysis By Age


Created Age Groupings

Age Group Age RangeA 16-20B 21-25C 26-30D 31-35E 36-40F 41+

Analysed Percent Absent By Age Group

Conclusions

There is a significance showing that

D = 31-35 and F = 41+ are more likely to

not to take unauthorised absence and that

B = 21-25 are more likely to take

unauthorised absence

Chi-Square Test: A, B, C, D, E, F

Expected counts are printed below observed counts

A B C D E F Total 1 188 175 183 132 91 62 831 183.80 187.26 182.08 125.12 94.06 58.68

2 25 42 28 13 18 6 132 29.20 29.74 28.92 19.88 14.94 9.32

Total 213 217 211 145 109 68 963

Chi-Sq = 0.096 + 0.802 + 0.005 + 0.378 + 0.099 + 0.188 + 0.603 + 5.050 + 0.029 + 2.378 + 0.626 + 1.183 = 11.438DF = 5,

P-Value = 0.043

19.35%

13.27%

8.97%

16.51%

8.82%

13.71%

11.74%

A B C D E F GrandTotal

Analysis By Gender


Split By Gender Analysed Percent Absent By Gender

Conclusions

There is significance showing that a

male person would more likely take

unauthorised absence

Female Male Grand Total22.28% 30.88% 29.08%

157 526 68345 235 280

Chi-Square Test: F, M


F M Total 1 188 643 831 174.31 656.69

2 14 118 132 27.69 104.31

Total 202 761 963

Chi-Sq = 1.075 + 0.285 + 6.767 + 1.796 = 9.924DF = 1,

P-Value = 0.002

15.51%

13.71%

6.93%

Female Male Grand Total

Group RangeA <1YearB 1-2 YearsC 2-3 YearsD 3-4 YearsE >4 Years

Analysis By Time Served


Analysed Percent Absent By Time Served

Conclusions

There is a significance in that

E = 4 years +

is more likely to take unauthorized

absence

Created Time Served Groupings

Chi-Square Test: A, B, C, D, E


A B C D E Total 1 117 129 151 324 110 831 113.04 126.85 154.46 314.97 121.67

2 14 18 28 41 31 132 17.96 20.15 24.54 50.03 19.33

Total 131 147 179 365 141 963

Chi-Sq = 0.138 + 0.036 + 0.078 + 0.259 + 1.120 + 0.872 + 0.229 + 0.489 + 1.630 + 7.050 = 11.902DF = 4,

P-Value = 0.018

12.24%

15.64%

11.23%

21.99%

13.71%

10.69%

A B C D E GrandTotal

Analysis By Department


Conclusions

This shows that there is a significance in departments

A = Tube Material Warehouse, B = Design/Process Engineering,

C = Sputter section.

In which all are more likely to take unauthorised absence

Percent Absent By Sub GroupCreated Department Sub-GroupsGroup dept_cd Dept Name Total Abs NeverA 83210 Tube Material Warehouse 46.15% 6 7B 81400 Design/Process Engineering 35.71% 5 9C 81320 Sputter section 32.50% 13 27D 81310 Spin section 21.21% 7 26E 84120 Tube OQC section 16.13% 5 26

81140 Maintenance 1 section81510 Maintenance Section(2/3)81230 Gunseal and Exhaust section81330 I.T.C. section81340 Outgoing section81450 CS-Reinspection section81210 Assembly Section81220 Inner Dag section

I 84110 Tube IQC section 7.69% 1 12J 81240 1st Inspection section 5.63% 4 67

81110 Screen Coating section81120 Chemical section

L 81130 Shadow Mask Section 2.50% 2 78Average 12.72% 109 748

F

G

H

14.55%

13.68%

10.29%

47

284

61

8

45

7

K 5.45% 6 104

35.71%32.50%

21.21%

16.13%14.55%13.68%10.29%

7.69%5.63% 5.45%

2.50%

46.15%

A B C D E F G H I J K L

Analysis By Shift


Improvement Suggestions

Improvement Actions/Suggestions by CTQ.

CTQ Proved By Improvement Recommendation

AgeProved By Chi Square Test

Use best fit when recruiting.

GenderProved By Chi Square Test


Length Of ServiceProved By Chi Square Test


Shift PatternProved By Chi Square Test

Build In More flexibility for day shift workers.

DepartmentProved By Chi Square Test

Compare Management Styles

MoraleProved By HARP Survey

New Incentive Scheme (Ongoing)

AccidentsProved By HARP Survey

New Health & Safety Structure In Place

EnvirionmentProved By HARP Survey

Music & Improved Rest Room Facilities

Management StyleProved By HARP Survey

Training Courses For Manager On Interpersonal Skills. Management Attitude Improvement Plan Next Slide

Aggressive Target SettingProved By HARP Survey

Unable to improve due to the nature of our business.


Improvement Suggestions - Management Attitude


AbsenceMorale

More Information

Team Building

Improved Interpersonal Skills

Treat Operators As Equal

More 1 To 1 Communication

Follow Correct Procedures

Improved Grading System

HIGH MORALE = LOW ABSENCE

Improvement Suggestions - Attendance Bonus


£100 Per Year

Decided By Incentive Scheme Working Party

Deductions?

All Authorised Non- Sickness Absence

Paid Quarterly

Cash/Vouchers/Savings

All Absence Resulting In Warnings

Total Savings £1,086,000

Verbal -25%

Written - 50%

F Written - 100%

All Deductions For 1 Year From Date Of Warning

Output

MeasureMeasureDefineDefine ImproveImprove ControlControlAnalyseAnalyse

Initial Current

σ level 3.21 3.43

PPM (Month) 46021 27300

Loss (£) £117000 £69405

£47595Est. Monthly Saving of:(Based on hours lost)

Contents:

1. Define Step 2. Measure Step3. Analysis Step4. Improvement5. Control

Process Sterilization Capability Up

6σ

Ch

am

pio

n R

evie

wFin

al R

ep

ort

Background CIAMD

Develop working efficiency and found 6 Sigma6 Sigma control for free Salmonella in sterilization process.

0 100 200 300 400 500

-0.5

0.0

0.5

1.0Individual and MR Chart

Obser.

Ind

ivid

ual V

alu

e

Mean=-0.01707

UCL=0.7674

LCL=-0.8016

0.0

0.3

0.6

0.9

Mo

v.R

ang

e

R=0.2950

UCL=0.9638

LCL=0

480 490 500

Last 25 Observations

-0.6

-0.3

0.0

0.3

Observation Number

Va

lue

s

-0.5 0.5

Capability PlotProcess Tolerance

I I I

I I I

I ISpecifications

Within

Overall

-0.5 0.0 0.5

Normal Prob Plot

-0.5 0.0 0.5

Capability Histogram

WithinStDev:Cp:Cpk:

0.2615070.640.62

OverallStDev:Pp:Ppk:

0.2886170.580.56

Process Capability Sixpack for Sealing Angle Line #2

Process Capability Current Condition

2

0.65

1.5

0.51

Target Current

Cp

Cpk

D CIAM

Executing analysis with Logic Tree for sterile product.

Potential X’ List

Big Y X1 X2 X3

Sterile Product Machine Sterile Time Fixed

Rotate

Temp Warm

Hot > 97

Steam Supply Spec (6.5 ~ 9) bar

Pineapples Bracket Stand

Methods Automatic

Material

Juice pH

Manual

Gage R&R D CIAM

0.010.05

novi mMar 3rd,2006Sealing angle Line 2

Misc:Tolerance:

Reported by:Date of study:Gage name:

0

0.5

0.0

-0.5

CBA

Xbar Chart by Operator

Sam

ple

Mea

n

Mean=0.03017UCL=0.03706LCL=0.02327

0

0.010

0.005

0.000

CBA

R Chart by OperatorS

ampl

e R

ange

R=0.003667

UCL=0.01198

LCL=0

10 9 8 7 6 5 4 3 2 1

0.40.30.20.10.0

-0.1-0.2-0.3-0.4

Part

OperatorOperator*Part Interaction

Ave

rage

A B

C

CBA

0.40.30.20.10.0

-0.1-0.2-0.3-0.4

Operator

By Operator

10 9 8 7 6 5 4 3 2 1

0.40.30.20.10.0

-0.1-0.2-0.3-0.4

Part

By Part

%Contribution

%Study Var


100

50

0

Components of Variation

Per

cent

Gage R&R (ANOVA) for Measurement

Gage R&R %Contribution

Source VarComp (of VarComp)

Total Gage R&R 0.000020 0.03

Repeatability 0.000020 0.03

Reproducibility 0.000000 0.00

Operator 0.000000 0.00

Part-To-Part 0.077747 99.97

Total Variation 0.077767 100.00

StdDev Study Var %Study Var

Source (SD) (5.15*SD) (%SV)

Total Gage R&R 0.004437 0.02285 1.59

Repeatability 0.004425 0.02279 1.59

Reproducibility 0.000323 0.00166 0.12

Operator 0.000323 0.00166 0.12

Part-To-Part 0.278832 1.43598 99.99

Total Variation 0.278867 1.43616 100.00

Two-Way ANOVA Table With InteractionSource DF SS MS F P

Part 9 4.19852 0.466502 21530.8 0.000000.00000Operator 2 0.00004 0.000022 1.0 0.38742

Operator*Part 18 0.00039 0.000022 1.2 0.33365

Repeatability 30 0.00055 0.000018

Total 59 4.19950

If significant, P-value < 0.05 indicatesthat a part is having a variation forSome measuring

Ok for “product acceptance”considering a productstolerance.

D CIAM

Measurement

Through analysis of process capability , getting sigma level 1.85 1.85 σσ

1 2 3 4 5 6

1.0

0.5

1.5

2.0

2.5

Z Sh

ift

Proc

ess

Con

trol

Good

Poor

Technology

GoodPoor

Block A

Block C

Block B

Block D

Four Block Diagram

1.85 σ 4.5 σ

Z Shift

1.00.50.0-0.5-1.0

Target USLLSL

Angle Line #2Process Capability Analysis for Sealing

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

CpkZ.LSLZ.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

83742.06

36602.01

47140.05

56399.74

24004.66

32395.08

4008.02

0.00

4008.02

0.56

1.67

1.79

1.38

0.58

0.621.851.98

1.59

0.288617

0.261507

499

-0.017074

-0.500000

0.000000

0.500000

Exp. "Overall" PerformanceExp. "Within" PerformanceObserved PerformanceOverall Capability


Process Data

Within

Overall

Process capability for Sterile Product


Target

Analysis - Regression D CIMA

Regression Analysis: Sterile product versus time and tempThe regression equation is

Sterile =0.000303 + 0.00113 time + 0.000060 temp


Constant 0.0003033 0.0002999 1.01 0.345

Time 0.00112859 0.00003939 28.65 0.000Time 0.00112859 0.00003939 28.65 0.000

Temp 0.00006005 0.00005694 1.05 0.327

S = 0.0003891 R-Sq = 100.0% R-Sq(adj) = 100.0%


Source DF SS MS F P

Regression 2 0.82500 0.41250 2.725E+06 0.000

Residual Error 7 0.00000 0.00000

Total 9 0.82500

Regression Analysis: Sterile product versus time and tempThe regression equation is

Sterile =0.000303 + 0.00113 time + 0.000060 temp


Constant 0.0003033 0.0002999 1.01 0.345

Time 0.00112859 0.00003939 28.65 0.000Time 0.00112859 0.00003939 28.65 0.000

Temp 0.00006005 0.00005694 1.05 0.327

S = 0.0003891 R-Sq = 100.0% R-Sq(adj) = 100.0%


Source DF SS MS F P

Regression 2 0.82500 0.41250 2.725E+06 0.000

Residual Error 7 0.00000 0.00000

Total 9 0.82500 0.00050.0000-0.0005

1

0

-1

No

rma

l Sco

re

Residual

Normal Probability Plot of the Residuals(response is Angle)

Use regression is to express and analyze a mathematical equation of describing a relationship. That is, it is

to fit a mathematical equation of describing a relationship between the “YY” and “X’sX’s”.

p-value < 0.05 : Significant factor

R2 and R2-adj are over 90% : which indicates a potentially good fit

Regression Analysis: Sterile product versus Steam supplyThe regression equation is

Sterile product = - 0,380 + 3,74 Steam supply


Constant -0,3796 0,1693 -2,24 0,042

Steam Supply 3,744 1,153 3,25 0,006Steam Supply 3,744 1,153 3,25 0,006

S = 0,1510 R-Sq = 43,0% R-Sq(adj) = 38,9%


Source DF SS MS F P

Regression 1 0,24032 0,24032 10,55 0,006

Residual Error 14 0,31905 0,02279

Total 15 0,55937

Regression Analysis: Sterile product versus Steam supplyThe regression equation is

Sterile product = - 0,380 + 3,74 Steam supply


Constant -0,3796 0,1693 -2,24 0,042

Steam Supply 3,744 1,153 3,25 0,006Steam Supply 3,744 1,153 3,25 0,006

S = 0,1510 R-Sq = 43,0% R-Sq(adj) = 38,9%


Source DF SS MS F P

Regression 1 0,24032 0,24032 10,55 0,006

Residual Error 14 0,31905 0,02279

Total 15 0,55937The P-value < 0.05Reject Ho ; Accept haThe P-value < 0.05Reject Ho ; Accept ha

Comparing of Sterile product and steam supply to find what the factor’ level’s which influence

enormously by represent characterized variation “Y”“Y” by the total sum of square.

Analysis – Regression D CIMA

0.30.20.10.0-0.1-0.2-0.3

4

3

2

1

0

Residual

Fre

quen

cy

Histogram of the Residuals(response is Cullet S)

Analysis – Chi-square D CIMA

Since P-Value >> 0.05; there’s no significantEffect between product sterile and factor.Since P-Value >> 0.05; there’s no significantEffect between product sterile and factor.

Conclusion:

◆ At least no one region is different, because a dependence exists. (P > 0.05)

◆ It no appears that the dependence may exist with Region 1 due to the large difference between the observed and the expected values.(must subtract the expected and observed values)

Conclusion:

◆ At least no one region is different, because a dependence exists. (P > 0.05)

◆ It no appears that the dependence may exist with Region 1 due to the large difference between the observed and the expected values.(must subtract the expected and observed values)

This Chi-Square is used to Test hypotheses about the frequency of occurrence of some event

happening with equal probability.

Chi-Square Test: matang, Stngh matang, juice Expected counts are printed below observed countsChi-Square contributions are printed below expected counts

Stngh matang matang juice Total OK 1000 995 1013 3008 1000.01 993.03 1014.96 0.000 0.004 0.004

NG 3 1 5 9

2.99 2.97 3.04 0.000 1.308 1.269

Total 1003 996 1018 3017

Chi-Sq = 2.585, DF = 2, P-Value = 0.2753 cells with expected counts less than 5.

Analysis – two sample T-test

D CIMA

Two-Sample T-Test and CI: Automatic, Chart

Two-sample T for Automatic vs Chart

N Mean StDev SE MeanAutomatic 12 14.70 1.47 0.42Manual 12 14.13 2.19 0.63

Difference = mu (Automatic) - mu (Chart)Estimate for difference: 0.56250095% CI for difference: (-1.030754, 2.155754)T-Test of difference = 0 (vs not =): T-Value = 0.74 P-Value = 0.469 DF = 19

Hypothesis tests help to determine if a difference is real, or if it could be due to chance

Dat

a

ChartAutomatic

19

18

17

16

15

14

13

12

11

Boxplot of Automat ic, Char t

There is no statistically significant differenceif the confidence interval for m1 - m2 does

include 0.0.

9

6.5

97

90107.5

Steam

Temp

Time

3

114

10

7

1215

0

Cube Plot (data means) for Salmonel la

D CM AImprovement – Response Surface Experiment

I

From the Main Effects Plot for the average of residue we conclude:

• Temp has the greatest effect on average residue

• Time has a lesser effect on average residue

• Steam supply shows little or no effect (within the test range)

on the average residue

Main Effects Plots for Main Effects Plots for Time, Temp & Steam supplyTime, Temp & Steam supply Average and Standard Deviation of Residue. Average and Standard Deviation of Residue.

• Salmonella : 0• Temp max : 97 oC• Time : 7.5 min• Steam : 6.5 bar

Best Condition:Best Condition:

Mea

n of

Sal

mon

ella

10.07.5

10.0

7.5

5.09790

9.06.5

10.0

7.5

5.0

Time Temp

St eam

Main Ef fects Plot ( data means) for Salmonel la

Improvement – Response Surface Experiment

D CM A I

Contour Plot

Interpretation: to make sterile product (no salmonella) move towards the center corner of the

Contour Plot (samonella = 00). Read off potential “Time” and “Temp” values that will provide Salmonella

< 2.

Lines of targetresponse for

“0” Salmonella

1. Get to know the condition giving lower salmonella.

2. To get the regular response, we realize what variables is important to control (temp & time)

3. Determine the level of independent variance needed for getting salmonella 00 (When temp is approximately 97oC)

Time

Tem

p

10.09.59.08.58.07.5

97

96

95

94

93

92

91

90

Hold ValuesSteam 6.5

Salmonella

4.5 - 7.07.0 - 9.59.5 - 12.0

12.0 - 14.5> 14.5

< 2.02.0 - 4.5

Contour Plot of Salmonel la vs Temp, Time

D CM A I

Generally, main effect is more important than interaction. If interaction is regarded as a important thing,

then interaction can be used as a factor of interaction and another interaction might be confounded.

96

Salm one l la

940

5

10

T em p

15

928 90910T im e

Hold ValuesSteam 6.5

Sur face Plot of Salmonel la vs Temp, Time

Improvement – Response Surface Experiment

Improvement – Result

D CM A I

1 2 3 4 5 6

1.0

0.5

1.5

2.0

2.5

Z Sh

ift

Proc

ess

Con

trol

Good

Poor

Technology

GoodPoor

Block A

Block C

Block B

Block D

Four Block Diagram

4.25 σ

Z Shift

1.85 σ

Improvement Result:

Saving Cost estimated: 2.7K U$/Year

0.500.250.00-0.25-0.50

Target USLLSL

Angle Line #2Process Capability Analysis for Sealing

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

CpkZ.LSLZ.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

63.70

46.70

17.00

41.55

30.86

10.69

0.00

0.00

0.00

1.30

4.14

3.91

3.83

1.34

1.344.254.01

3.94

0.124193

0.121123

84

0.014762

-0.500000

0.000000

0.500000



Process Data

Within

Overall

Control D M A I

C

Process

InputController

Controllable factors:- Miss adjust causes- Adjustable check- Pad control- Education

Group Member

Process CapabilityDesiredOutput

X

Upper Control Limit

Lower Control Limit●

●

Six Sigma Quality focuses on moving control upstream to the leverage input characteristic for Y.

If we can measure and control the vital few X’s, control of Y should be assured.

10050Subgroup 0

0.5

0.0

-0.5

Sam

ple

Mea

n

Mean=0.001188

UCL=0.4384

LCL=-0.4360

1.0

0.5

0.0

Sam

ple

Ran

ge

11 1

R=0.2325

UCL=0.7596

LCL=0

Xbar/R Chart for Sealing Angle Line #2Output

Process Standard Change

Contents

6σ

Ch

am

pio

n R

evie

wD

MA

IC-S

tep

Rep

ort

1. DEFINE

2. MEASURE

3. ANALYSIS

4. IMPROVEMENT

5. CONTROL

Optimizing Material DIO

‘06 6σ Project Registration

PJT Name

Period TeamName

BreakthroughMain Improvement Object(KPI) Current World Best Target


Team Formation, Related Department Involved

Name Dept. Position Main Role

NECK POINT


Expected Results

Quantitative

Qualitative

Optimizing Material D I O

1. JIT delivery system for press part2. Door to door delivery for glass3. Hub delivery system from Korea4. Raw material issue control to process5. Weekly stock taking6. Minimize NG and rework stock in process

at end of the month7. PO issued based on the latest production

plan

1. D I O is one of key performance indicators in inventory management.

2. Good level of inventory will support production line in effective and efficient way.

3. Fluctuated material D I O

Current Condition

USL : 2.9LSL : -Means : 2.58091Sample N : 14Z Bench : 1.03

- Warehouse Inventory Amount D I O ( Days Inventory

Outstanding )2.6 days 2.3 days

- Working In Process Inventory Amount

1,117

K $

US

Current

1,050

Target - Continuous material supplies to production line

- All material used efficiently in production line

- Reduced warehouse and WIP inventory amount 1NG and rework stock in the end of month

2. Fluctuate production schedule

Just In Time PurchasingVendor Managed Inventory

K 67

3.53.02.52.01.5

USLUSL

Process Capability Analysis for C2

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

Cpk

Z.LSL

Z.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

200815.40

200815.40

*

151989.40

151989.40

*

214285.71

214285.71

*

0.28

*

0.84

0.84

*

0.34

*

1.03

1.03

0.380447

0.310413

14

2.58091

*

*

2.90000



Process Data

Within

Overall

Background

DM A I C

One of the material inventory management control is DIO (Days Inventory Outstanding) that has the formula:

Inventory AmountMaterial DIO = ------------------------- X total days of current month Sales Amount

The elements of material DIO are: 1. Warehouse Inventory (Raw Material)2. Working In Process inventory (Semi Finished Goods )3. Material In Transit Inventory

This large amount and high DIO have some effects :

Risk in obsolescence, expired, lost, and defect

High inventory carrying cost

Current Material Inventory Condition :

Average Amount : $ K 1,117

D I O : 2.6 days

Optimizing inventory amount and D I O will bring material inventory management in a more efficient cost

DM A I C

X4X3X2X1Y X5

M. RATIO

Net BOMMaterial Price

Src. Performance

BOM Quantity

M. YIELD

CPT Price

Experience

Negotiation

Education

M. DIO

Material Loss

Net Req. Material

Material Cost Amt

Chemical

Others

Market Situation

Mat. Inv. Amt

Production Qty

Key Part

Sales Amt

Beginning Stock

Purchasing Qty

Ending Stock

Receive Amt

Total Days

In Transit Inv.

Warehouse Inv.

W I P Inv.

Sales Qty.

Current Month

Supply Condition

Glass

Mask

Press Part

Sub Mount

Process Part

Assy Part

Sales Target

Calculation Skill

Forecast Skill

Logic Tree

D M A I CLogic Tree

X4X3X2X1Y X5

Mat Inv Amt

In Transit Inv Delivery Sched.

Material DIO

Sales Amt

Sales Qty

Price

Warehouse Inv Experience

A

Education

Total Days

Stock

Bulb

B

Part Stock

Assy Stock

Current Month

Sales Target

Production Capa

Graphite G 72 B

D Y

CMA

Glass

M/Assy

Simulation Skill

Marketing Nego

Market Situation

W I P Inventory

YS

TCL Prod.

Comp. Supply

Sub Month

D M A I C

This project with potential X-List will be focused to control Warehouse Inventory and working In Process Inventory

X Level 2

X Level 2

D YD Y

Big YBig Y

Material DIOMaterial DIO

Assy StockAssy Stock

Part StockPart Stock

P A DP A D

GlassGlass

Sub MountSub Mount

Flat MaskFlat Mask

PhosphorPhosphor

G 72 BG 72 B

X Level 1

X Level 1

Material Inv. AmtMaterial Inv. Amt

W/house Inv.W/house Inv.

WIP Inv.WIP Inv.

X Level 3

X Level 3

X Level 4

X Level 4

AssyAssy

CMA CMA

goodgood

Brainstorming Potential X

DM

A I C

Gage R&R

StdDev Study Var %Study Var

Source (SD) (5.15*SD) (%SV)

Total Gage R&R 0.109148 0.56211 16.58 Repeatability 0.109046 0.56159 16.56

Reproducibility 0.004723 0.02432 0.72

Operator 0.004723 0.02432 0.72

Part-To-Part 0.649363 3.34422 98.62

Total Variation 0.658472 3.39113 100.00

Number of Distinct Categories = 8

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

0

4

3

2

321


Sam

ple

Mea

n

Mean=2.679UCL=2.724LCL=2.634

0

1.0

0.5

0.0

321

R Chart by Operator

Sam

ple

Ran

ge

R=0.02405UCL=0.07857LCL=0

1413121110 9 8 7 6 5 4 3 2 1

4

3

2

Part


Ave

rage

1 2

3

321

4

3

2

Operator

By Operator

1413121110 9 8 7 6 5 4 3 2 1

4

3

2

Part

By Part

%Contribution

%Study Var %Tolerance


350

300

250

200

150

100

50

0


Per

cent

Gage R&R (ANOVA) for Measure

To test validation of measurement , 3 (three) persons, twice inspection and 14 months calculation for material D I O has carried out, and the result was acceptable.

The result of Gage R&R total is 16.58, the acceptance percentage is below 20 (<20), meanwhile the result of measurement between <20, it accepted.

Gage R&R <20%

Acceptable

Gage R&R <20%

Acceptable

Gage R & R

Current Condition D MA I C

3.53.02.52.01.5

USLUSL

Process Capability Analysis for C2

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

Cpk

Z.LSL

Z.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

200815.40

200815.40

*

151989.40

151989.40

*

214285.71

214285.71

*

0.28

*

0.84

0.84

*

0.34

*

1.03

1.03

0.380447

0.310413

14

2.58091

*

*

2.90000



Process Data

Within

Overall

Z-Bench : 1.03

Block Diagram D MA I C

Z shift has been identified , the Z shift will be 0.19, regarding this issue the target of the project is 4.5 sigma

A B

C

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6

Z sh

iftP

roce

ss C

ontro

l

Good

Poor

Poor GoodZ st

Technology

Position of DIO was column C ,it was mean :

PROCESS CONTROL IS GOOD,BUT TECHNOLOGY (METHOD)

IS BAD

EXPLANATION

Z st : 1.03Z shift : Z st – Z lt : 1.03 – 0.84 : 0.19


D

D M AI C

The Pareto analysis has been done, the result shows that tube stock, Furnace, CMA, and Assy have the highest contribution to WIP Assy Amount

WIP Assy Stock Analysis

Others

CPT Dongbang

Mount Assy

CMABulb

Bare Tube

14962 7725 33504 37174 48983165826 4.9 2.510.912.115.953.8

100.0 95.1 92.6 81.8 69.7 53.8

300000

200000

100000

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cen

t

Cou

nt

Avg WIP Stock 1Q '04

Analysis

Bare TubeBare TubeBare TubeBare Tube

BulbBulbBulbBulb

CMACMACMACMA

45 % conveyor stock ($ 78 K)

F’ce StockC/V Stock

Mount AssyMount AssyMount AssyMount Assy

Safety Stock to secure supply

55 % ( $ 97 K) consists Of :- NG & rework, - Pending Lot- Remained Prod. Stock

Stock to keep production

2 shift Mount Assy Process

Pareto Chart for WIP Assy Inv.

D M AI CWIP Part Stock Analysis

OthersPhospor

Sub Mount

GlassMask

7375 10535 32501 50418179511 2.6 3.811.618.064.0

100.0 97.4 93.6 82.0 64.0

200000

100000

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cen

t

Cou

nt

Pareto Chart for Desc.

The Pareto analysis result for WIP part stock shows that Mask Stock, Glass, Sub Mount and Phosphor have the highest contribution to WIP part Amount Analysis

Flat MaskFlat MaskFlat MaskFlat Mask

GlassGlassGlassGlass

Sub MountSub MountSub MountSub Mount

86 % stock at PT YSI($ 156 K)

Stockloading

PhosphorPhosphorPhosphorPhosphor

Safety Stock for aging time

14 % stock at SM ( $ 23 K)

- Annealing- Forming & Blackening

Mount Assy Process stock

High price

- Flat Mask (RM)

Pareto Chart for WIP Part Inv.

D M AI CWarehouse Stock Analysis

The Pareto analysis result for warehouse stock shows that Stock, Graphite, Phosphor and PAD have the highest contribution to Warehouse Stock amount Analysis

D YD YD YD Y

GraphiteGraphiteGraphiteGraphite

PhosphorPhosphorPhosphorPhosphor

Inner Supply

Hardly PO revision PO issued three month before

P A DP A DP A DP A D

- Quality Problem- Comp. Request to achieve sales target

Decreasing consumptions

- Revised Production Plan

GlassPAD

Phospor

GraphiteDY

1600717572396544144347520 9.910.824.425.629.3

100.0 90.1 79.3 54.8 29.3

150000

100000

50000

0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cen

t

Cou

nt

Pareto Chart for C1

Blue Stock

So frequently

Pareto Chart for W/House Inv.

Minimum order 2304 kg

D M AI C

Not Selected as Vital View


Item High Amount Stock Remarks



Selected as Vital View W I P

Part

NG and rework Stock

Mask Stock outsourcing process

Warehouse

Assy

Analysis Result

Glass Stock

Sub Mount

Remained production stock & Pending lot

conveyor stock


Not Selected as Vital View

Y D Y

G 72 B & G 355

P A D



Phosphor


D M AI C

Bottle Neck1. High stock of NG, rework, pending lot in the end of month due to quality problem, sourcing team can not fully controlled this situation. Actually, it’s depend on process and quality performance.

Analysis Result

2. Outsourcing Mask Annealing process at Shin require more raw material stock to keep production and secure supply.

3. Frequently change production plan for CIT and Y DY quality problem cause influence high stock Y DY.

D M AI

C Improvement

Item Improvement Remarks

Mask Stock

- Identify and checking 3 days before closing

- Partial raw material delivery

Glass Stock

Stock

- Daily vendor managed inventory

- Communicate Stock to related dept and push for action

- Working closely with PCT Team to input remained stock

In the end of month

- Pending Lot

- Remained Prod. from March ‘04

Annealing Outsourced

- Optimized in out stock control

- Daily input glass to production line

- Communicate and push process to minimize stock

- Check stock condition & make any necessary action

Y D Y- Confirm I production plan - Best effort to match PCT Production Plan & actual

- Just In Time purchasing

PhosphorGraphite

- Improve Import delivery simulation skill

Weekly control

from March ‘04

Weekly control

from March ‘04

- Tightly control on ETD & ETA - Maintain actual delivery performance on SRS

from March ‘04

from March ‘04

D M AI

CImprovement Result

Result analysis after improvement actions : Z bench 2.60

2.92.72.52.32.11.91.71.5

USLUSL

Process Capability Analysis for DIO

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

Cpk

Z.LSL

Z.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

1307.83

1307.83

*

4651.78

4651.78

*

0.00

0.00

*

1.00

*

3.01

3.01

*

0.87

*

2.60

2.60

0.215663

0.249574

6

2.25093

*

*

2.90000



Process Data

Within

Overall

Z-Bench : 2.60

D M AI

C Sigma Value

After improvement action, we can compare it with previous condition.Improved Condition is better than Previous Condition.

PREVIOUS CONDITION

A B

C0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6

Z s

hif

t

Pro

cess

Co

ntr

ol

Good

Poor

Poor GoodZ st

Technology

IMPROVED CONDITION

Sigma = 1.03

D

Z st : 2.60Z shift : Z st – Z lt : 2.60 – 3.01 : -0.41

Z st : 2.60Z shift : Z st – Z lt : 2.60 – 3.01 : -0.41

A B

C

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6

Z sh

iftP

roce

ss C

ontro

l

Good

Poor

Poor GoodZ stTechnology

D



Sigma = 2.60

D M AI

C Saving Cost

1,117

K $

US

Current

1,050

Target

K 67 (6%)

TARGET

1,117

K $

US

Before

1,075

After

K 42 (4%)

RESULT

Previous Average Material Inventory Amount : $ K 1,117Current Average Material Inventory Amount : $ K 1,075 Saving Cost : $ K 42

D M A IC

Control

Below check sheets are applied to ensure and maintain the material inventory DIO stays optimized and some improvement activities stay controlled :

1. Mask daily inventory stock control at Shin

This is one of the application of vendor managed inventory (VMI)

2. Salvage glass daily input to process

3. Weekly Stock taking for warehouse and WIP (include Assy and Stock)

Desc. 31 1 2 3 4 5 6

F/MASK 5000 120000 120000 110000 100000 87000 80000

ANNEA 35447 33612 21740 19973 23373 25155 20373

FORM 2550 1961 3170 5149 2090 2095 8300

BLACK 7189 4841 7146 6972 6860 7207 2961

TOTAL 50186 160414 152056 142094 132323 121457 111634

F/MASK 5000 75000 75000 75000 75000 75000 75000

ANNEA 2933 2933 2933 2933 2933 2933 2933

FORM 0 0 0 0 0 0 0

BLACK 2790 2790 2790 2790 2790 2790 2790

TOTAL 10723 80723 80723 80723 80723 80723 80723

14"

20"

1. 2.

3.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Tt l

14 0 0 0 672 504 168 336 0 224 224 308 154 322 168 0 0 0 392 504 168 168 0 0 308 0 782 168 168 0 336 168 6242

20 0 0 0 72 266 248 310 0 0 180 72 272 0 0 0 0 72 0 0 72 208 0 0 192 416 200 64 62 0 548 64 3318

21 0 0 512 256 192 0 64 0 192 320 320 192 128 320 0 0 576 320 512 192 0 0 0 384 320 64 128 0 0 0 128 5120

0 0 512 # # 962 416 710 0 416 724 700 618 450 488 0 0 648 712 # # 432 376 0 0 884 736 # # 360 230 0 884 360 14680

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Tt l

14 200 0 0 400 400 400 0 0 400 200 200 0 400 0 0 0 200 400 200 200 400 0 0 0 200 200 200 0 0 200 400 5200

20 0 0 81 0 261 90 270 0 90 0 0 180 0 0 0 0 0 0 0 0 0 0 270 0 180 270 270 180 0 180 90 2412

21 0 0 405 243 162 81 0 0 81 405 374 162 162 243 0 0 324 324 324 243 81 0 0 405 162 0 0 243 0 81 162 4667

200 0 486 643 823 571 270 0 571 605 574 342 562 243 0 0 524 724 524 443 481 0 270 405 542 470 470 423 0 461 652 12279

FUNNEL

PA NEL

JUMLAH

JUMLAH

Tanggal

Tanggal

1 153-113V DY 14" LG STD 0 0 488 2,432 -2,432 1.68205 0.00 820.842 153-276F DY HARTONO/SANKEN/VESTEL 0 0 0 0 0.00000 0.00 0.003 3024GAFA01C MASK FLAT 21" MULTI 20,000 20,000 2,191 80,000 -60,000 1.72214 34,442.80 145.704 3040GA0001A BASE 20" 202,103 202,103 0 202,103 0 0.02246 4,539.23 0.005 3040GA0006A BASE 14" 210,000 210,000 0 210,000 0 0.03620 7,602.00 0.006 3210GBAA01A FRAME SUPPORT 14" 0 0 4,284 0 0 0.12900 0.00 552.647 3210GBEA01A FRAME SUPPORT 20" 0 0 1,920 0 0 0.58850 0.00 1,129.928 3210GBFA01A FRAME SUPPORT 21" 0 0 3,120 0 0 0.63900 0.00 1,993.689 3300GB0001A PLATE COMPENSATION 0 0 0 0 0 0.63900 0.00 0.00

10 3300GB0001B PLATE COMPENSATION 10,000 10,000 0 10,000 0 0.00428 42.80 0.0011 3300GB0002A PLATE COMPENSATION 20,000 20,000 0 20,000 0 0.00299 59.80 0.0012 3300GC0001A B-S PLATE 20" 0 0 20,000 10,000 -10,000 0.01220 0.00 244.0013 3740GA0001A LEAD PROTECT 20" 12,500 12,500 11,500 12,500 0 0.01182 147.75 135.93

No Part No Description Act.Gd Inv. Book PMS Gap U/PRICEProcess REMARKSAmount Process

Amount INV

Attachment

Inventory and DIO Monthly Control 2004

Inven

tory

(K $

)

INTR.

W/H

WIP

T/T

DESC. 1 2 3 4 5 6 7 8 9 10 11 12

15 62 84 135 82

303 333 432 469 394

706 786 522 597 509

1,024 1,181 1039 1201 984

Days 2.3 2.5 2.0 2.3 2.0

2. DIO

1024

1181

1039

1201

984

800

850

900

950

1000

1050

1100

1150

1200

1250

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

2.32.5

2.0 22.3

0

1

2

3

4

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

Target

Actual TargetActual

Attachment

Inventory and DIO Budget Control 2004

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sales Amount Target 12,060 12,176 12,569 12,256 12,290 12,322 12,432 12,879 12,874 12,197 10,740 11,551 146,346

Actual 13,784 13,668 15,892 15,388 15,423

In transit Target 45 45 45 41 41 41 40 40 40 40 40 40 42

Actual 15 62 84 135 82

W/house Target 372 372 372 365 365 365 365 365 365 325 325 325 357

Actual 303 333 432 469 394

WIP Target 695 695 695 672 672 672 672 672 672 665 665 665 676

Actual 706 786 522 597 509

Total Target 1,112 1,112 1,112 1,078 1,078 1,078 1,077 1,077 1,077 1,030 1,030 1,030 1,074

Actual 1,024 1,181 1,039 1,201 984

DIO Target 2.9 2.6 2.7 2.6 2.7 2.6 2.7 2.6 2.5 2.6 2.9 2.8 2.7

Actual 2.30 2.51 2.03 2.34 1.98

Avg/TotalDesc.2004

Attachment

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

In Transit 25 39 33 24 17 24 113 107 80 44 124 109 55 62 61

W I P 564 724 713 799 890 970 693 653 604 664 668 574 706 786 715

W/House 442 331 278 333 298 357 314 361 409 320 383 311 303 333 341

Total 1,031 1,094 1,024 1,156 1,205 1,351 1,120 1,121 1,093 1,029 1,175 994 1,064 1,181 1,117

Sales Amt 12,953 13,091 13,701 12,080 12,698 12,328 14,099 14,396 14,147 14,385 10,551 13,112 13,784 13,668 13,214

D I O 2.4675 2.3399 2.3169 2.8709 2.9412 3.2879 2.4632 2.4138 2.3186 2.2166 3.3403 2.3502 2.3936 2.5055 2.6205

MonthsavgDESC.

0

0.5

1

1.5

2

2.5

3

3.5

4

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

Material Inventory and DIO Control 2003 and 1 Q of 2004

Date Nov 24th , 2004

Process Technique GroupPrepared : Novi Muharam

Reduce LNG Usage

6σ

Ch

am

pio

n R

evie

wD

MA

IC R

ep

ort

Contents

1. Define Step 2. Measure Step3. Analysis Step4. Improvement5. Control

Background CIAM

D

LNG (Liquid Natural Gas) is source of energy for combustion process in F’ceUp to 3rd quarter LNG usage for exhaust furnace still higher, it’s about 5000 Nm3/day. This project have a purpose to decreasing LNG usage in furnace

4500

5000


LNG Usage

Unit: (Nm3/day)

11%

How to do:

LNG & Air pressure systemAdjustment to find best ratio

Both of them.

Target Saving cost:

= (5000 –4500 )Nm3/day x 0.165 U$/Nm3 x 30 x 12= 500 Nm3/day x 0.165 x 30 x 12= 29,700 U$/Years29,700 U$/Years

OthersElec

tricO2N2LNG

0.00700.06050.12900.13090.1650 1.412.326.226.633.5

100.0 98.6 86.3 60.1 33.5

0.5

0.4

0.3

0.2

0.1

0.0

100

80

60

40

20

0

Defect

CountPercentCum %

Per

cent

Cou

nt

Energy Usage Price

Energy Unit Price (U$)

G/S

Load ExhP-pipe

Tip off B.B.D

Exhaust FurnaceZone #1 ~ #44

Unload

Robot

RobotControl Panel

-F/F C/V -CartIn

Out

KeepingZone

Up SlopeZone

Down Slope

Process MappingCIAM

D

CTQ Area : - LNG & Air Usage

LNG RatioUsage

Material LNG Pressure Ratio

Air Pressure Ratio

Machine TIC Temperature

RC Fan RPM Motor

Dumper Valve

Exh Blower Pressure

Big Y X1 X2 X3

D CIAM

Brainstormed Potential X List

GaR StdDev Study Var %Study Var

Source (SD) (5,15*SD) (%SV)

Total Gage R&R 0,009704 0,04998 1,40

Repeatability 0,002582 0,01330 0,37

Reproducibility 0,009354 0,04817 1,35

Operator 0,002566 0,01321 0,37

Operator*Part 0,008995 0,04633 1,30

Part-To-Part 0,692590 3,56684 99,99

Total Variation 0,692658 3,56719 100,00

GaR StdDev Study Var %Study Var

Source (SD) (5,15*SD) (%SV)

Total Gage R&R 0,009704 0,04998 1,40

Repeatability 0,002582 0,01330 0,37

Reproducibility 0,009354 0,04817 1,35

Operator 0,002566 0,01321 0,37

Operator*Part 0,008995 0,04633 1,30

Part-To-Part 0,692590 3,56684 99,99

Total Variation 0,692658 3,56719 100,00

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

Exhaust F'Ce MeasurementOct 19th, 2005

Novi M

0-1,5

-1,0

-0,5

0,0

0,5 Eng'r Gi jo Maker 1 maker 2 PQC


Sa

mp

le M

ea

n

Mean=-0,5057UCL=-0,5032LCL=-0,5082

0

0,000

0,005

0,010 Eng'r Gi jo Maker 1 maker 2 PQC

R Chart by Operator

Sa

mp

le R

ang

eR=0,001333

UCL=0,004356

LCL=0

a b c d e f

-1,0

-0,5

0,0

0,5

Part


Ave

rag

e

Eng'r Gijo_1Gijo_2Gijo_3

PQC

Eng'r Gijo Maker 1 maker 2 PQC

-1,0

-0,5

0,0

0,5

Oper

Response By Operator

a b c d e f

-1,0

-0,5

0,0

0,5

Part

Response By Part

%Contribution %Study Var

Gage R&R Repeat Reprod Part-to-Part

0

50

100


Pe

rce

nt

Gage R&R (ANOVA) for Auto 14"

Gage R&R for Exhaust F’ce Line #1 Measurement

Gage R&RD CIA

M

Gage R&R <20%

Acceptable

Gage R&R <20%

Acceptable

1086420

USLLSL

Exhaust F'ce 14" Capa'

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

Cpk

Z.LSL

Z.USLZ.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

137981.99

90265.06

47716.94

14111.87

11722.77

2389.10

81081.08

81081.08

0.00

0.45

1.67

1.34

1.09

*

0.76

2.82

2.272.19

1.66305

0.98276

37

5.27297

2.50000

*

7.50000



Process Data

Within

Overall

Capability Process

1 2 3 4 5 6

1.0

0.5

1.5

2.0

2.5

Z Sh

ift

Proc

ess

Con

trol

Good

Poor

Z stTechnology

GoodPoor

Block A

Block C

Block B

Block D


Four Block Diagram

D CIAM

Regression Analysis: Press LNG versus TempThe regression equation is

Press LNG = 3.7 + 21.9 Temp


Constant 3.75 36.18 0.10 0.918

Temp 21.934 6.556 3.35 0.002

S = 64.96 R-Sq = 24.2% R-Sq(adj) = 22.1%


Source DF SS MS F PP

Regression 1 47242 47242 11.19 0.0020.002Residual Error 35 147702 4220

Total 36 194945

Regression Analysis: Press LNG versus TempThe regression equation is

Press LNG = 3.7 + 21.9 Temp


Constant 3.75 36.18 0.10 0.918

Temp 21.934 6.556 3.35 0.002

S = 64.96 R-Sq = 24.2% R-Sq(adj) = 22.1%




Total 36 194945

Analysis

Accept Ha

Fail to reject Ho

The p-value < 0.05, Fail to accept Ho, which demonstrate statistical significance (an equation with a “good” fit)


LNG Pressure

ManometerGauge

D CIMA



N: 37StDev: 1.65154Average: 5.27297

9.28.27.26.25.24.23.22.2

.999

.99

.95

.80

.50

.20

.05

.01

.001

Pro

bab

ility

Qty LNG


Analysis of Temp furnace with result has significant effect to Pressure LNG

Hypothesis Analysis :Ho : Temperature furnace has no significant effect to LNG PressureHa : Temperature furnace has significant effect to LNG Pressure

Hypothesis Analysis :Ho : Temperature furnace has no significant effect to LNG PressureHa : Temperature furnace has significant effect to LNG Pressure

0 100 200 300 400 500 600 700 800 900

95% Confidence Intervals for Sigmas

Bartlett's Test

Test Statistic: 3.282

P-Value : 0.858

Levene's Test

Test Statistic: 2.096

P-Value : 0.131

Factor Levels

7575757575757575757575757575757575757575757575757575

0.1 0.2 0.5 0.6 1.7 2.0 3.0 4.0 5.0 6.0 6.8 7.0 7.3 8.0 9.0 9.1 9.3 9.811.011.611.812.012.913.514.214.6

Test for Equal Variances for LNG Ratio

One-way ANOVA: Air Press, Press LNG, TempAnalysis of Variance


Factor 2 1769458 884729 118.10 0.0000.000

Error 108 809084 7492

Total 110 2578542


Based on Pooled St Dev

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

Air Pres 37 424.73 73.53 (-*--)

Press LN 37 117.00 73.04 (--*--)

Temp 2 37 297.54 108.32 (--*--)

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

Pooled StDev = 86.55 100 200 300 400

One-way ANOVA: Air Press, Press LNG, TempAnalysis of Variance


Factor 2 1769458 884729 118.10 0.0000.000

Error 108 809084 7492

Total 110 2578542


Based on Pooled St Dev

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

Air Pres 37 424.73 73.53 (-*--)

Press LN 37 117.00 73.04 (--*--)

Temp 2 37 297.54 108.32 (--*--)

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

Pooled StDev = 86.55 100 200 300 400

Tem

p

LNG

Pre

ss

Air

Pre

s

600

500

400

300

200

100

0

Boxplots of Air Press - Temp

(means are indicated by solid circles)

Analysis D CIM

A

Analysis of Temp furnace with result has significant effect to LNG & Air press

Hypothesis Analysis :Ho : Temperature furnace has no significant effect to LNG & Air PressureHa : Temperature furnace has significant effect to LNG & Air Pressure

Hypothesis Analysis :Ho : Temperature furnace has no significant effect to LNG & Air PressureHa : Temperature furnace has significant effect to LNG & Air Pressure

Regression Analysis: LNG Press versus RPM Motor The regression equation is

RPM = 1448 + 0.0344 LNG Press


Constant 1448.00 5.02 288.20 0.000

LNG press 0.03440 0.01568 2.19 0.035

S = 8.711 R-Sq = 12.1% R-Sq(adj) = 9.6%



Regression 1 365.11 365.11 4.81 0.0350.035Residual Error 35 2655.97 75.88

Total 36 3021.08

Regression Analysis: LNG Press versus RPM Motor The regression equation is

RPM = 1448 + 0.0344 LNG Press


Constant 1448.00 5.02 288.20 0.000

LNG press 0.03440 0.01568 2.19 0.035

S = 8.711 R-Sq = 12.1% R-Sq(adj) = 9.6%



Regression 1 365.11 365.11 4.81 0.0350.035Residual Error 35 2655.97 75.88

Total 36 3021.08

RC Fan Rotation

Analysis

Accept Ha

Fail to reject Ho

D CIMA



N: 37StDev: 19.1748Average: 1462.22

14901480147014601450144014301420

.999

.99

.95

.80

.50

.20

.05

.01

.001

Pro

bab

ility

RPM


Analysis of RPM motor with result has significant effect to Pressure LNG

Hypothesis Analysis :Ho : RPM motor has no significant effect to LNG PressureHa : RPM motor has significant effect to LNG Pressure

Hypothesis Analysis :Ho : RPM motor has no significant effect to LNG PressureHa : RPM motor has significant effect to LNG Pressure



Analysis

Regression Analysis: LNG Press Versus Air Blower The regression equation is

Air Blower = 475 - 0.169 LNG Press


Constant 474.97 35.14 13.52 0.000

Air Blower -0.1689 0.1111 -1.52 0.138

S = 72.23 R-Sq = 6.2% R-Sq(adj) = 3.5%




Total 36 194647

Regression Analysis: LNG Press Versus Air Blower The regression equation is

Air Blower = 475 - 0.169 LNG Press


Constant 474.97 35.14 13.52 0.000

Air Blower -0.1689 0.1111 -1.52 0.138

S = 72.23 R-Sq = 6.2% R-Sq(adj) = 3.5%




Total 36 194647

Exhaust F’ceBlower

Fail to accept Ha

Reject Ho

D CIMA

Average: 424.730StDev: 73.5314N: 37

Anderson-Darling Normality TestA-Squared: 0.200P-Value: 0.875

300 400 500 600

.001

.01

.05

.20

.50

.80

.95

.99

.999

Pro

bab

ility

Blower


Analysis of Air Blower with result has no significant effect to Pressure LNG

Hypothesis Analysis :Ho : RPM motor has significant effect to LNG PressureHa : RPM motor has no significant effect to LNG Pressure


Regression Analysis: LNG Press versus DamperThe regression equation is

Damper = 428 - 0.43 LNG Press


Constant 427.96 24.17 17.70 0.000

LNG press -0.425 2.745 -0.15 0.878

S = 74.55 R-Sq = 0.1% R-Sq(adj) = 0.0%




Total 36 194647

Regression Analysis: LNG Press versus DamperThe regression equation is

Damper = 428 - 0.43 LNG Press


Constant 427.96 24.17 17.70 0.000

LNG press -0.425 2.745 -0.15 0.878

S = 74.55 R-Sq = 0.1% R-Sq(adj) = 0.0%




Total 36 194647

Analysis

Damper

2001000-100

2

1

0

-1

-2

Nor

mal

Sco

re

Residual

Normal Probability Plot of the Residuals(response is Damper)

Fail to accept Ha

Reject Ho

D CIMA

Analysis of Damper with result has no significant effect to Pressure LNG



Analysis Resume D CIM

Select most effected factor in Regression and ANOVA with P value < 0.05Select most effected factor in Regression and ANOVA with P value < 0.05

Factor Detail Analysis Content Result ConclusionAnalysis Tool

Selected as vital fewP < 0.05

P < 0.05Selected as vital few

LNG Ratio Pressure and quantity adjustment For f’ce combustions

P < 0.05 Selected as vital few

Sample test for kind of air blower effect to gas ratio.

RPM MotorWith Gas

Heating result measurement insideFurnace.

P > 0.05

Not selected as vital few

Air & LNGPressure

Checking both of pressure Compare with temperature result

Air blowerWith Gas

Sample test for damper setting forEach position

P > 0.05Not selected as vital few

Damper With Gas

A

Regression

Regression

Regression

Regression

ANOVA

Bottle Neck D CIM

A

From furnace build until now, this furnace have never been done by Cleaning inside of f’ce

burner

Dilutionair

GasExhaustduct

To many Carbon (C)

To make efficiency, Need cleaningNeed cleaning inside of f’ce From Carbon (C) result of combustion.

Response Surface Regression: Result versus LNG Press, LNG Qty, ...The analysis was done using coded units.

Estimated Regression Coefficients for Result

Term Coef SE Coef T P

Constant 115.3 37.23 3.098 0.008

Block -25.0 17.44 -1.436 0.173

LNG Pres 108.1 18.39 5.878 0.000

LNG Qty -137.5 18.39 -7.478 0.000

AIR Pres -79.2 18.39 -4.310 0.001

RPM 3.9 18.39 0.213 0.834

LNG Pres*LNG Pres 2.4 17.20 0.142 0.889

LNG Qty*LNG Qty 78.6 17.20 4.568 0.000

AIR Pres*AIR Pres 26.9 17.20 1.566 0.140

RPM*RPM -10.3 17.20 -0.600 0.558

LNG Pres*LNG Qty -76.0 22.52 -3.375 0.005

LNG Pres*AIR Pres -51.5 22.52 -2.287 0.038

LNG Pres*RPM 3.5 22.52 0.155 0.879

LNG Qty*AIR Pres 39.2 22.52 1.743 0.103

LNG Qty*RPM -2.5 22.52 -0.111 0.913

AIR Pres*RPM -1.7 22.52 -0.078 0.93

S = 90.08 R-Sq = 91.7% R-Sq(adj) = 82.8%

Response Surface Regression: Result versus LNG Press, LNG Qty, ...The analysis was done using coded units.

Estimated Regression Coefficients for Result

Term Coef SE Coef T P

Constant 115.3 37.23 3.098 0.008

Block -25.0 17.44 -1.436 0.173

LNG Pres 108.1 18.39 5.878 0.000

LNG Qty -137.5 18.39 -7.478 0.000

AIR Pres -79.2 18.39 -4.310 0.001

RPM 3.9 18.39 0.213 0.834

LNG Pres*LNG Pres 2.4 17.20 0.142 0.889

LNG Qty*LNG Qty 78.6 17.20 4.568 0.000

AIR Pres*AIR Pres 26.9 17.20 1.566 0.140

RPM*RPM -10.3 17.20 -0.600 0.558

LNG Pres*LNG Qty -76.0 22.52 -3.375 0.005

LNG Pres*AIR Pres -51.5 22.52 -2.287 0.038

LNG Pres*RPM 3.5 22.52 0.155 0.879

LNG Qty*AIR Pres 39.2 22.52 1.743 0.103

LNG Qty*RPM -2.5 22.52 -0.111 0.913

AIR Pres*RPM -1.7 22.52 -0.078 0.93

S = 90.08 R-Sq = 91.7% R-Sq(adj) = 82.8%

Analysis of Variance for Result

Source DF Seq SS Adj SS Adj MS F P

Blocks 1 16733 16733 16733 2.06 0.173

Regression 14 1238887 1238887 88492 10.91 0.000

Linear 4 885220 885220 221305 27.27 0.000

Square 4 193821 193821 48455 5.97 0.005

Interaction 6 159846 159846 26641 3.28 0.031

Residual Error 14 113594 113594 8114

Lack-of-Fit 10 113594 113594 11359 * *

Pure Error 4 0 0 0

Analysis of Variance for Result

Source DF Seq SS Adj SS Adj MS F P

Blocks 1 16733 16733 16733 2.06 0.173

Regression 14 1238887 1238887 88492 10.91 0.000

Linear 4 885220 885220 221305 27.27 0.000

Square 4 193821 193821 48455 5.97 0.005

Interaction 6 159846 159846 26641 3.28 0.031

Residual Error 14 113594 113594 8114

Lack-of-Fit 10 113594 113594 11359 * *

Pure Error 4 0 0 0

Improvement

The Improve phase identifies a solution and confirms that the proposed solution will meet or exceed the improvement goals of the project.

D CAMI

10

-1005

0

ratio0

500

100

1000

200 3000

400

Result

500LNG Press

Surface Plot of Result

Hold values: AIR Pres: 452.5 RPM: 1459.0

Improvement

Optimum condition when:

-LNG pressure 60.5 mmH2O-Ratio of gas 4.5-Air Pressure 452.5 mmH2O-RPM Motor 1459 rpm

Optimum condition when:

-LNG pressure 60.5 mmH2O-Ratio of gas 4.5-Air Pressure 452.5 mmH2O-RPM Motor 1459 rpm

D CAMI

D CAM

Improvement

7.56.55.54.53.52.5

USLLSL

Exhaust F'ce 14" Capability

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

Z.LSL

Z.USL

Z.Bench

Cpm

Cpk

Z.LSL

Z.USL

Z.Bench

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

1.71

0.00

1.71

0.07

0.00

0.07

0.00

0.00

0.00

1.55

4.64

6.12

4.64

*

1.76

5.27

6.94

5.27

0.464622

0.409704

37

4.65757

2.50000

*

7.50000



Process Data

Within

Overall

1 2 3 4 5 6

1.0

0.5

1.5

2.0

2.5

Z Sh

ift

Proc

ess

Con

trol

Good

Poor

Z stTechnology

GoodPoor

Block A

Block C

Block B

Block D

Four Block Diagram

I

4500

5000


LNG Usage

Unit: (Nm3/day)

8%

D CAM

Improvement

I

4600

Result Result

92%92% Cost Saving:= (5000 – 4600 )Nm3/day x 0.165 U$/Nm3 x 30 x 12= 400 Nm3/day x 0.165 x 30 x 12

= 23,760 U$/years23,760 U$/years

Improvement Result

Control Plan

Control

Item Period Gauge Method Chart Type PIC

LNG PressEach Zone

Weekly DigitalManometer

Use check sheet Xbar - R PQCLeader exh

Air PressEach Zone

Weekly DigitalManometer

Use check sheet Xbar - R PQCLeader exh

LNG metercontrol

Daily Visual Use check sheet Xbar - R Leader exh

RPM Motor

Weekly Tachometer Use check sheet Xbar - R PQC

D IAMC

Process standard changes:

D IAM

Control

F’ceControl

LNG MeterDaily check

RecordData

F’ceControl

LNG MeterDaily check

RecordData

LNG & AIR PressWeekly check

Standard RecordData

Setting GasRatio

OK

NG

RecordData

Process Control

Weekly check sheet for Gas measurement

Standard:

-LNG pressure 60.5 ± 10 mmH2O-Ratio of gas 4.5-Air Pressure 452.5 ± 50 mmH2O

Standard:

-LNG pressure 60.5 ± 10 mmH2O-Ratio of gas 4.5-Air Pressure 452.5 ± 50 mmH2O

PIC: Leader Exh

PIC: PQC Exh

Before Before After After

C

PIC: Leader Exh

six sigma and its implementation

Leadership & Management