SIX SIGMA AND ITS IMPLEMENTATION ON THE PROJECT

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Six Sigma

Six Sigma6 sigma is used by individual and organizations to:Drive and sustain improvementsProvide rigorous alignment of actions with strategyGuide decision making with facts and dataMeet customer needs through improved products and processesDeliver bottom-line results

Six sigma digunakan baik secara individu maupun secara kelompok, untuk (1) pengerak dan penyokong improvement (proses peningkatan/pengembangan kualitas), (2) menyediakan arah yang tepat/akurat/teliti dari suatu kegiatan dengan sebuah strategi, (3) memandu membuat keputusan dengan fakta dan data, (4) mencapai kebutuhan pelanggan dengan meningkatkan product dan proses, dan (5) mengantarkan/mengantarkan hasil yang bottom-line (benefit, ukuran kinerja yg berhubungan dengan uang).Definisi 6 sigma mempunyai 3 elemen, ke-3 elemen tersebut tujuannya sama yaitu untuk CI .*

Six Sigma

Six Sigma

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 dEconomie 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.

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KEY BUSINESS CONCEPT OF SIX SIGMA

Six Sigma Methods

Production

Design

Service

Purchase

HRM

Administration

QualityDepart.

Management

M & S

ITWhere can Six Sigma be applied?

COPQ against sales revenue

COPQ (Cost of Poor Quality)Sigma LevelDPMOCOPQ as sales percentile1-sigma691.462 (very low competitive)N/A2-sigma308.538 (Average Indonesias Industry)N/A3-sigma66.807 25-40% of sales4-sigma6.210 (Average USAs Industry)15-25% of sales5-sigma233 (Average Japans Industry)5-15% of sales6-sigma3,4 (World Class Industry)< 1% of sales

SUCCESS STORY IN SIX SIGMA General Electric

Dupont Chronology

PeriodeDescriptionSigmaBefore six sigmaDupont Total Cost of Poor Quality= 20 -30 % of revenueAbout 3 sigmaImplementingCost of Implementing Six Sigma = $ 20 million-1999 Q1Pilot Project Six Sigma on Specialty Chemicals startedRevenues $ 1.5 BillionsTarget $ 80 million savings (5% dari revenues)

1999 Q240 Black Belts done for training and start for project1999 Q4Total saving $ 35 million (initial target $ 25 million)2.3% of Revenue17.7% COPQ4 Sigma2000 Q4Total 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 toolsLower Fruit7 Basic Tools of QCGround FruitLogic and Intuition

6 Basic Concept

3 sigma level company6 sigma level company

It is important to understand the difference between accuracy and precision Sigma is a measure of variation (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 sigma1

USL

3

TInflection 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

The Normal Curve and CapabilityLow SigmaHigh SigmaThe objective is to understand customer requirements and reduce process variation and center of target

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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 not feel averages!

Measuring Process PerformanceCustomers 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 doesnt tell the whole story. The average and the variation together show whats happening.

s01020304050The pizza delivery example. . .

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Reduce Variation to Improve Performance

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

s01020304050How many standard deviations can you fit within customer expectations?

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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()

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.

StepsActivityMeasurement4. Understanding process capability for Y5. Clarifying measurement method of Y6. Specific description of Target object for improving against YFocusYYYAnalysis7. Clarifying Target for improving Y8. Clarifying factors which affect YYX1 .... XnImprovement9. Extract the vital few factors through screening10. Understanding correlation of vital few factors11. Process optimization and confirmation experimentX1 .... XnVital Few X1 Vital Few X1 Control12. Confirm measurement system for X13. Selection method how to control vital few factors14. Build up process control system & audit for vital fewVital 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.YThe Approach of 6 Sigma Step

6 Sigma Roles

SIX SIGMA CHALLENGESSix 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 ProcessDMAIC = Define, Measure, Analyze, Improve and Control

6 Methodology

DefineDefining the Project YTranslate the external CTQs into internal product requirements or Project Y.

Example:

Process MappingStartFinish12 Process Targets and SpecificationsExperimentalInput ParametersTargetUpper Spec.Lower Spec.

Add the operating specification and process targetsFor the controllable variable input

Courtesy of Daraius PatellContinue to ask Why? until you Reach the Root Cause...Structure TreeExample

Cause & Effect DiagramThe final diagram will look like a fishbone with the backbone displaying every known

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

MeasurementMethodMachinePeopleMaterialsEnvironment

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Why-why Diagram

Measurement Determine Process Capability for Project 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 termsPredict probability of producing defects

Measurement Determine 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:

TechnicalTransactionalManpowerPeopleMachinePriceMaterialProductMethodPromotionMeasurementPhysical 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. More Frequently Asked Questions About Measurement Data

Sources of Measurement System VariationThe 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 %GR&R and %Study Variation2gage = 2repeatability + 2reproducibilityRepeatability: 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.

Analyze Hypothesis Test (for variables)Hypothesis Test (for attributes)

CorrectDecisionCorrectDecisionType 1ErrorType 2ErrorHoHaHoHa

True

Accept

The ratio which isbeing Ha even if its false.Where is usually set up at 10%.The ratio which isbeing rejected Ho eventhough certain thing is true where is 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 TypesVariableDiscrete 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 tool depends on the data type. We use ANOVA when we have categorical input(s) and a continuous response.

Continuous CategoricalCategorical ContinuousDependent Variable (Y)Independent Variable (X)RegressionANOVALogisticRegressionChi-Square (2)Test

Variance Homogeneity TestingMeans Testing1.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 SquareFHomogeneity ofVarianceBartletts TestHomogeneity ofVarianceLevenes Test1.One population Mean testing

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

2.Two population Mean testing

1) When they know 1 and 2

2) When they dont know 1 and 2 1 = 2

1 2 Normal distribution( 1 - Sample Z )T distribution( 1 - Sample t )Normal distributionT distribution( 2 - Sample t )The type of Hypothesis

What is DOE ?DOE is more than just a statistical technique.

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

Improvement

DOE StepsDefine the objective of the experiment.Select the response and input factors.Determine the resources required.Select suitable experiment design matrix and analysis strategy. Perform the experiment and record data.Analyse the data, draw conclusions, and perform confirmation runs.

Good planning is critical to success!

Basic Concepts in DOE

++--Pr

118Ave +

58Ave -

12++901046--701034-+905210+-7051YPrx SpdSpdSpdPrRun79Y = 8

206ProcessProcess ModelDOE

FMEAFailure 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.

SAMPLESA 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 Xs, control of Y should be assured.INPUT

Controller

ProcessCapabilityDesired OutputOUTPUT

PROCESSThe Logic of SPC(Statistical Process Control)?L M N O PControl

Types of Control Charts

Types of Control Charts

Variables Charts for monitoring continuous Xs

Average & Range

X bar & R n 10 typically 3~5

Averages & Std Deviation

X bar & n 10

Median & Range

X & R n 10 typically 3~5

Individual & Moving Range

XmR n = 1

Attributes Charts for monitoring discrete Xs

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 (Xs) are continuous or discrete. There are control charts for both continuous data and discrete data.

Control Chart12108WeekUpper Control Limit = Ave + 3 x Std Dev1413 76Lower Control Limit = Ave - 3 x Std DevCentral Line = Average Note: Control limits should be established using subgroup standard deviation

Six Sigma DMAICImplementation ProjectExample

Date Department / TeamPrepared : .. Project title G ManagerPres. Dir.

Director Mgr.6 Champion ReviewFinal Report

Contents1. Define Step 2. Measure Step3. Analysis Step4. Improvement Step5. Control Step

-Attachment

Define Step

PJT NamePeriod

TeamNameDiv./Dept:

Breakthrough KPICurrentWorld BestTarget

Main Improvement Object Team Formation (Related Department Involved)

NameDept.LevelRoleQuantitativeQualitative

Expected ResultsHow to do ?Why ?(* Selection Background)New Idea for Target Achievement

Engineer

ApprovalKa PartKa Group Project RegistrationNeck Point

How to do:

Target Saving cost:

Expected resultBackgroundGeneral Background

StartFinish12 Process Targets and SpecificationsExperimentalInput ParametersTargetUpper Spec.Lower Spec.

Add the operating specification and process targetsFor the controllable variable inputProcess Mapping

SIPOC Suppliers, Inputs, Process, Outputs, CustomersYou obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements.Process Understanding

Measure Step

F(x)Machine

Man

Big YX1X2X3 Brainstorming Potential Xs ListMaterialMaterialCIADM

Observed ProcessVariationMeasurementProcess VariationSampleM a n

M a nMachineMethodMaterialTo determine if the measurement error is small and acceptable relative to the process variation, we can use Gage R&R study.Gage R & RCIADM

Process CapabilityCurrent Condition

1234 561.00.51.52.02.5Z ShiftProcess ControlGoodPoorTechnologyGoodPoor

Block ABlock CBlock BBlock D

Four Block Diagram Z ShiftA : Poor control, inadequate technology B : Must control the process better, technology is fineC : Process control is good, inadequate technologyD : World classCurrentTargetCIADM

FactorDetail Analysis PlanScheduleMar 2nd WeekMar 3rd WeekMar 4th Week X1.3X1.1Inspect correlation between Y and inspector for each groupAnalysis khole / dimensionHeight, diameter, angle etc,X1.2X1.4

Analysis PlanCIADM

Analyze Step

CIDMARegression Analysis: Angle Value versus Rotate GearThe regression equation isAngle Value = - 0,380 + 3,74 Rotate GearPredictor Coef SE Coef T PConstant -0,3796 0,1693 -2,24 0,042Rotate G 3,744 1,153 3,25 0,006

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

Analysis of VarianceSource DF SS MS F PRegression 1 0,24032 0,24032 10,55 0,006Residual Error 14 0,31905 0,02279Total 15 0,55937Unusual ObservationsObs 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

Motor

Gear 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 Y and Xs.

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One-way ANOVA: Gr.A - 3, Gr.A - 4, Gr.B - 3, Gr.B - 4, Gr.C - 3, Gr.C - 4Analysis of VarianceSource DF SS MS F PFactor 5 0.8761 0.1752 2.59 0.030Error 102 6.8897 0.0675Total 107 7.7659 Individual 95% CIs For Mean Based on Pooled StDevLevel 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 0.15See from sealing angle specifications theres no problem, cause all operator adjustmentStill in range (-0.5o ~ 0.5o). But theres 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 theresa difference between the levelOf adhesive

We represent characterized variation Y by the total sum of square, then this method is to find what the factors level which influence enormously is, comparing both of them.CIDMA

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CIDMAAnalysis Result

Improve Step

CDMAIDesign of ExperimentFull Factorial Design

Factors: 3 Base Design: 3, 8Runs: 8 Replicates: 1Blocks: 1 Center pts (total): 0The Improve phase identifies a solution and confirms that the proposedSolution will meet or exceed the improvement goals of the project.StdOrderRunOrderCenterPtBlocksFlow rateHeating TimeDrying TimeResult8111810612-90.25211600412-52.61311600410-56.47411600612-57.62511810410-89.23611600610-68.16711810412-85.3481181061091.3

Heating Time : 4 hour Drying Time : 10 hour Flow rate : 520 m3/hour

Optimize Condition:CDMAI

1234 561.00.51.52.02.5Z ShiftProcess ControlGoodPoorTechnologyGoodPoor

Block ABlock CBlock BBlock D

Four Block Diagram Z ShiftCurrentTargetImprovement ResultCDMAI

45005000Current Target

Result8%

4600Result 92%Cost Saving: US$Improvement Result CDMAI

Control Step

IProcessInputController

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

Group Member Process CapabilityDesiredOutputXUpper Control LimitLower Control LimitSix Sigma Quality focuses on moving control upstream to the leverage input characteristic for Y. If we can measure and control the vital few Xs, control of Y should be assured.OutputProcess Standard ChangeCDMA

ICDMAP Chart enables us to control our process using statistical method's to signal when process adjustments are needed.

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

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Six Sigma ProjectExample

Date 20 MAY 2009Team : GalvanizePrepared by : Imam MudawamOptimalkan pemakaian ZincPada Proses Galvanize Prod Mgr CEO.Work Mgr6 Champion ReviewFinal Report

Contents1. Define Step 2. Measure Step3. Analysis Step4. Improvement Step5. Control Step

ISKDCNG

*

PJTNamePeriod

TeamName Div./Dept: CONST./GALV.

Breakthrough KPICurrentWorld BestTarget

Main Improvement Object Team Formation (Related Department Involved)

NameDept.LevelRoleQuantitativeQualitative

Expected ResultsHow to do ?Why ?(* Selection Background)New Idea for Target Achievement

CEO

ApprovalWork MgrProd MgrNeck PointOptimalkan Pemakaian ZincZ Shift: - 1.86.

Imam MLukmanBanbangSpvSupvFormKetebalan Galvanize sesuai standard.

AminFormMemberLeaderLeaderMemberRp / kgRp / kg

SlametMemberFabGalvGalvGalvQcCIAMDProject Registration

D

M

A

I

CScheduleMaking Theme Reg.Analyzing Aging Root-Caused

Determine Potential X ListFind Current situation

Find Vital X by analyzing Potential X

Find Improvement ideaConfirmation run

Process Control by Monitoring Aging Amount

May W5Junl W5Jul W5 Optimalkan Pemakaian ZincPada Proses GalvanizeSaving Zinc Rp. 295 Juta /TahunGalvanizeReduce costHansGaSupvFormMemberSept W3 Sept W5 Galvanize183.6 micr.Galvanize130 micron

ISKDCNG

*

General Background

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

*

Big YX1X2X3 Brainstorming Potential Xs ListCIAMDDegrisingPickling 1 & 2MaterialKonsentrasi BasaCaustic SodaBase MetalKonsentrasi Keasaman Optimalkan pemakaian ZincPada Proses GalvanizeTemperaturWaktu PencelupanKomposisi kimiaKetebalan 1Hcl

*

Optimalkan Pemakaian ZincPada proses GalvanizeFluxingKonsentrasi KeasamanBig YX1X2X3 1TemperaturBrainstorming Potential Xs ListCIAMD

Zinc Amunium ChlorideDippingAluminium AlloyZinc IngotTemperatur Komposisi Campuran Waktu pencelupan

In doing gage R&R we take 2 times repeat in checkGage R&R take from 2 Inspector who check this sample of Galvanize coating thickness in 10 chek point Decide operator who take Gage R&RDo Gage R&RChange Method,measurement, etcAnalyze Result Gage R&RNext StepNG ; Total Gage R&R > 20%OK ; Total Gage R&R < 20%Measure Step - GAGE R&RCIAMD

Gage R&R

Gage R&R < 20%AcceptableCIAMDSample 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. StudyVar %StudyVarSource 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

*

CIAMDCurrent ConditionRata 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 . KETEBALAN GALVANIZE

*

Four Block Diagram

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

ABC0.51.01.52.02.5123456

Z shiftProcess ControlGoodPoorPoorGoodZ stTechnologyPosition of Current Condition was column C , it was mean :PROCESS CONTROL IS GOOD,BUT TECHNOLOGY (METHOD) IS BAD

EXPLANATIONDSIGMA TARGET

SIGMA TARGETSigma current 1.86

*

CIAMDWAKTU PENCELUPANOptimalkan pemakaian ZincPada proses GalvanizeF(x)xAnalysis TEMPERATUR ZINC KETEBALAN GALVANIZE

*

Optimalkan pemakaian zinc pada proses Galvanize (Continuous)Waktu PencelupanY

Factor (x)

Type

Tools

Analyze Type of factor & Tools UsingCIAMD

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%

Analysis of Variance

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

Unusual Observations

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 = 1.69422Analysis Waktu Fluxing ( Regresion )CIAMDKetebalan Galvanize VS Waktu Fluxing

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%

Analysis of Variance

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

Durbin-Watson statistic = 1.36705Analysis Temperatur Dipping ( Regresion )CIAMDKetebalan Galvanize VS Temperatur Zinc

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%

Analysis of Variance

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

Durbin-Watson statistic = 2.41646Analysis Waktu Dipping ( Regresion )CIAMDWaktu Dipping VS Ketebalan Galvanize

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

CIAMD Selected not Vital View ItemContent Result RemarksSelected as Vital View GalvanizingP = 0.034P = 0.024P = 0.000Waktu FluxingTemperatur DippingWaktu DippingAnalysis Result ( Regression )Selected not Vital View Berdasarkan data diatas variabel waktu Dipping paling berpengaruh terhadap hasil ketebalan pada proses galvanize (99%)

CImprovementIAMDBerdasarkan 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.

Y = 43.7 + 19.9 x

Chart1

63.6

83.5

103.4

123.3

143.2

163.1

183

202.9

222.8

242.7

Standar 6mm

Waktu Pencelupan (menit)

Ketebalan Galvanis (micron)

HUBUNGAN WAKTU PENCELUPAN DAN KETEBALAN GALVANIZEPADA PRODUK DENGAN KETEBALAN 6 mm

Sheet1

Untuk Pelat 6mm

y=a+bo*xxyy'

y=43.7+19.9*x044

16496.57

284103.4

3103135.9

4123157.6

5143196.6

6163219.2

7183256.8

8203268.7

9223287.7

10243335.9

Untuk Pelat 3mm

y=a+bo*xxy

y=20.4+15.4*x020

136

251

367

482

597

6113

7128

8144

9159

10174

Sheet1

0

0

0

0

0

0

0

0

0

0

Standar 6mm

Waktu Pencelupan (menit)

Ketebalan Galvanis (micron)

HUBUNGAN WAKTU PENCELUPAN DAN KETEBALAN GALVANIZEPADA PRODUK DENGAN KETEBALAN > 6 mm

Sheet2

ANALISA PENGHEMATAN ZINC

KETEBALAN ZINC SAAT INI183.6

TARGET KETEBALAN ZINC130

PENGHEMATAN53.6

PENGHEMATAN (%)29%

Ton

PPRODUK TEBAL > 6 MM PER TAHUN4000

INTERNAL GALVANIZE ( 40% )1600

KONSUMSI ZINC = 0.048477.44

PENURUNAN PEMAKAIAN ZINC (29%)22.61

RUPIAH PENGHEMATAN (Rp. 13.050.000 / Ton)Rp 295,031,215.6948.4

0.048450

Sheet3

*CImprovementIAMDCatatan : 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 upSession 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

MeasurementAnalysisImprovementControlSession Start :Session Start :Session Start :Main ScheduleSession Finished : Session Finished : Session Finished : Measure Define Improve Control Analyse

*

A period of time taken off by an employee which is neither planned or authorised Definition Of DefectDEFINITIONSicknessUnauthorised absenceLatenessDEFECT 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 qualityVALUE Measure Define Improve Control Analyse

*

Absence Logic TreeAbsenceManagement StyleTime With CompanySex =CTQsAgeGuidelinesTeam LeaderSectionAccidentsMorale issuesCleanlinessRepetitive Work EmployeeTarget SettingEnvironmentMethodConsistent Approach Use Of PolicyShift Pattern Measure Define Improve Control Analyse

*

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 CalculationUsing The Percentage Defective (4.605%) We Calculated The DPMO Since MergerDPMO = 46050Using statistical tables the SIGMA LEVEL = 3.21 Measure Define Improve Control Analyse

Chart1

4.274.31-0.04

4.665.22-0.56

5.235.86-0.63

4.74.560.14

5.015.09-0.08

5.345.020.32

3.863.97-0.11

3.133.45-0.32

2.752.720.03

AprAprApr

MayMayMay

JunJunJun

Tube

HR

Variance

Sheet1

Jul4.274.31-0.04

Aug4.665.22-0.56

Sep5.235.86-0.63

Oct4.74.560.14

Nov5.015.09-0.08

Dec5.345.020.32

Jan3.863.97-0.11

Feb3.133.45-0.32

Mar2.752.720.03

Apr

May

Jun

Sheet1

Tube

HR

Variance

Sheet2

Sheet3

*

VOC - Harp Questionnaire

219 People Questioned Across The Tube Plant

Answered by all employees.NameMarital StatusClock NumberAgeDepartmentChildrenShiftHomeTime with CompanyJob 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? Measure Define Improve Control Analyse

*

Catchment Area Of People Questioned Measure Define Improve Control Analyse

*

Paretos Of Absence DataConclusionsOperators would like to be paid more to come to workOperators see a problem in the way they are treated by MgtMusic might help??ConclusionsSection Leaders see the benefit in some form of attendance bonus, but more emphasis oncommunication and datacollection Measure Define Improve Control Analyse

*

Paretos Of Absence DataConclusionsNo significant patterns have emergedConclusionsAbsence is not a major issue Measure Define Improve Control Analyse

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