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`Six SigmaWhat is Six Sigma? What is Six Sigma?Six Sigma has 3 meaning :: Six Sigma has 3 meaning1. Statistical Measurement · Process has 6 sigma capability. · Our products are 6 sigma-quality products. 2. Problem Solving Methodology · We reduced defects by using 60 methodology. · We have Six Sigma projects. 3. Improvement Program · We implement Six Sigma this year.1Six Sigma as a statistical measurementSix Sigma as a statistical measurement · Most processes have variation due : to· Man · Machine · Material · Method · Measurement · Environment2Six Sigma as a problem solving methodologyDEFINE CONTROL MEASUREDMAIC ProcessIMPROVEANALYZESix Sigma is a methodology to improve processes by using : 1. Systematic thinking 2. Problem Solving tools 3. Rigorous Statistical approachSix Sigma as a problem solving methodologySix Sigma is a methodology that provide business with tools to improve the capability of their business process.Kai Yang &amp; Basem EI-Haik Design for Six Sigma (McGraw Hill, 2003)Six Sigma methodology uses a specific problem-solving approach and Six Sigma tools to improve processes and products. This methodology is data-driven, with a goal of reducing unacceptable products or eventsWarren Brussee Statistics for Six Sigma Made Easy (McGraw Hill, 2004)3Six Sigma as a problem solving methodologyBreakthrough StrategyKey QuestionsDefine Phase Measure Phase Analyze Phase Improve Phase Control PhaseWhat is the problems? How big is the problem? What is the root causes? How to improve the process? How to maintain the process?Six Sigma as a problem solving methodology4Six Sigma as a problem solving methodology1. Statistically : Proven Relationships between Inputs and Outputs 2. Systematically : Control KPIV and Monitoring KPOVSix Sigma as a problem solving methodologyProject Alignment Establish solid Baseline Determine y=f (x) Optimize y=f (x) Sustain new y=f (x) D M A I CSix Sigma Breakthrough !5Six Sigma as a problem solving methodology7 QC toolsSix Sigma ToolsWhich bag would a world class golfer prefer?Statistics Multi-vari Analysis FMEA, MSA, Control Plan Design of Experiment 6 Sigma Measurement Brainstorming tools Lean Tools Etc.Six Sigma as a problem solving methodologySpecial characteristics of Six Sigma Methodology :· Use full set of statistical / problem solving tools · Use tools in systematic fashion · Can solve various problems · 6 Sigma good in solving the following problems : · Chronic problems · Problems with many Xs · Defect reduction · Cost reduction · Variance reduction and robust solution · Breakthrough improvement6Six Sigma Project ...    TFNDefineSix Sigma Team Project Champion : ·  Black Belt : ·  ·  ·  ·  IE1DefineSix Sigma Process · · · · · Define Phase **** Measure Phase Analysis Phase Improve Phase Control Phase 6  ( .. ­ .. 47)Define Project Project  Customer Satisfaction  OTP :  95%  82%  (PPC)2Define Project M/C    Break Down OTP  Start up Loss Defect R/M Delay R/M Delay Test R/M  Technical Skill·Brain Storming and Relation Diagram   Minor Stop R/M Labor Skill Set up time delayDefine ProjectY = f (x1,x2..........,xn)OTP = f (Break Down () , Start up Loss ,  , Minor Stop ,  , Set up Delay ,  , R/M delay  , R/M Delay  , Defect ,  ,  ,) Break Down = f ( ,  R/M ,  , Labor Skill , Tech Skill) Defect = f (Labor Skill , Tech Skill ,  R/M) Set up Delay = f (Technical Skill) Minor Stop = f ( M/C , ) R/M Delay  = f ( test R/M , ) Start up Loss = f (Minor Stop , Break Down)3Define Project  Matrix Data Analysis 6    / Six Sigma tools  OTP   Process InputsTotal1 2 3 4 5 6 7 8 9 10 11 12   minor stop  Set up delay  R/M delay R/M delay Defect   9 8 5 8 10 10 8 10 8 5 2 25 6 3 5 4 5 10 5 5 10 10 105 10 5 9 10 10 10 10 8 10 5 55 8 5 6 9 10 8 10 8 10 5 53 10 3 3 5 3 10 8 3 10 10 1010 5 5 3 10 10 10 10 10 6 10 610 8 10 1 10 10 1 1 10 1 1 17.95 7.8 6.45 4.4 9.05 9.15 6.25 6.25 8.35 5.45 4.25 3.854 5 6 10 2 1 7 7 3 9 11 126-Sigma Project #1(Needle Breakage)  SKPBlack Belt CandidateName :   Company :  XXX 4Define: Project Team MembersProject ChampionPlant ManagerTeam Members (Green Belt)Production Supervisor Production Chargehand Engineering Supervisor QA Supervisor Industrial EnigineerDefine: Problem Statement (Needle Breakage)        /    (QA)     SKP  0.10%5Define : ..- .. 47      2547SUM 30000.00 25000.00 20000.00 15000.00 10000.00 5000.00 0.00 ACC % 120.00 100.00 80.00 60.00 40.00 20.00 0.00     42.73%Define () : ..- .. 47 2000000.0 Weight (Kg) 1500000.0 1000000.0 500000.0 0.031.93%Percentage (%)    /         /                               Type of Defect (/Kg)120.00 100.00 80.00 60.00 40.00 20.00 0.00SKGLLOSIN SKP  1  3   23.21 /INTERTERRTERRYSKPRI BPECKYRIB30 23.21 18.86 20 13.89 10.79 10.26 10 0Percentage (%)Weight (Kg)6Define () () :  SKP   ..- .. 47     %Loss  1,725.85 Kg 1,705,181.50 Kg0.10%Define: Project Scope  SKP  : Project Objectives (Metrics) ()  80% Baseline 0.10% Goal 0.02%: Critical To Quality  PPC   : Cost Of Poor Quality ·  PPC  ·  PPC  ( %OTP) ·  (0.60 /Kg) ·  ()  7Define: Benefits· Hard Saving : ·  561.6 Kg/  146,016 / ***  : %Loss   = 0.10 ­ 0.02 = 0.08%  = 702,000 Kg /   = 561.6 Kg /   -  -  scrap = 130  / Kg  Benefit Loss = 702,000 * 130 * 0.08% = 73,008   Opportunity Loss  = Benefit Loss·Total Hard Saving 146,016 /· Soft Saving : ·  PPC   ·   ·  · Define: Time Line· · · · · Define Phase Measure Phase Analysis Phase Improve Phase Control Phase : 19 .. 47 : 17 .. 47 : 21 .. 47 : 19 .. 47 : 15 .. 47· End Project: 30 .. 478DefineProcess Mapping· High Level Process Mapping (SIPOC)Suppliers Inputs Process Outputs Customers PPC     QA        (Knitting Process) PPCDefineProcess Mapping· High Level Process Mapping (SIPOC) ()Suppliers Inputs Process Outputs Customers PPC          QA      QA  9DefineProcess Mapping· Detailed Process Mapping      4 /   No   Yes    Data Collection QA   No  Yes   QADyeing &amp; FinishingDefineProcess Mapping· Detailed Process MappingInput-  -   -  - TypeC C/S CProcess1. -    -  stop motion -   -  -  scanner / needle detectorVA/NVA  ()VA 20Output-  -  2. -  -   -    -    -  -   -    -    C S S C/S C S S C/S -  -  -  NVA NVA NVA -   -    -    -  VA -  10DefineProcess Mapping· Detailed Process Mapping (continue)-  -   -     -   -  -   -     -   -  -   -     -   C S S C/S C S S C/S C S S C/S -  -   -  -  -  1  -  NVA NVA NVA NVA NVA VA -  -  -  -  -  -  -  4 / -  stop motion  -   -  NVA NVA NVA VA -  -  -  - DefineProcess Mapping· Detailed Process Mapping (continue)-  -   -     -   C S S C/S -  -  stop motion  -  -  -  -  -  -     -  -  -     -  -  -  -  C S S C S S S S S -  -  4 Point -  -  -  -  NVA NVA NVA NVA NVA VA -  -  -  -  -  -  -  -  QA NVA NVA NVA NVA NVA NVA VA NVA -  -  -  -  -  -  -  -  QA 11Cause &amp; Effect Diagram (Brain Storming) MeasureData Collection Function Y = f (X1 , X2 , X3 , X........... , Xn)  = f ( ,  ,    ,  ,  ,  ,  ,  ,  ,  ,  , %RH ,   ,  (slub) ,  (snarl) ,   ,  ,  , / needle detector ,  needle detector , / needle detector  4 M Man (6) =  ,  ,  ,   ,  , / needle detector Method (6) =  ,  ,  ,  ,  ,  needle detector Machine (5) =  ,  ,  ,  , /needle detector  Material (2) =  (slub) ,  (snarl)12MeasureData CollectionMeasureBaseline Data ( Sigma Level) SKP  ..- .. 47 Descriptive StatisticsVariable: %LossAnderson-Darling Normality Test A-Squared: P-Value: Mean StDev Variance Skewness Kurtosis N Minimum 1st Quartile Median 3rd Quartile Maximum 0.566 0.122 0.101446 0.035308 1.25E-03 0.532003 -9.4E-01 18 0.061696 0.067421 0.095707 0.129951 0.1710200.070.090.110.130.150.1795% Confidence Interval for Mu95% Confidence Interval for Mu 0.0838880.07 0.08 0.09 0.10 0.11 0.12 0.130.119004 0.05293295% Confidence Interval for Sigma 0.026495 95% Confidence Interval for Median95% Confidence Interval for Median0.0684230.124290P-Value &gt;= 0.05   Normal Distribution13MeasureBaseline Data ( Sigma Level) Normality Test  Normal Probability Plot.999 .99 .95Probability.80 .50 .20 .05 .01 .001 0.06 0.11 0.16Anderson-Darling Normality Test A-Squared: 0.566 P-Value: 0.122%LossAverage: 0.101446 StDev: 0.0353079 N: 18P-Value &gt;= 0.05   Normal DistributionMeasureBaseline Data ( Sigma Level) (Process Capability)  Process Capability Analysis for %LossUSLWithin OverallProcess Data 0.200000 USL * Target * LSL 0.101446 Mean 18 Sample N StDev (Within) 0.0270023 StDev (Overall) 0.0358307Potential (Within) Capability * Cp 1.22 CPU * CPL Cpk Cpm Overall Capability Pp PPU PPL Ppk * 0.92 * 0.92 1.22 * 0.00 0.05 0.10 0.15 0.20 Exp. &quot;Overall&quot; Performance * PPM &lt; LSL 2974.77 PPM &gt; USL PPM Total 2974.77Observed Performance * PPM &lt; LSL 0.00 PPM &gt; USL PPM Total 0.00Exp. &quot;Within&quot; Performance * PPM &lt; LSL 131.20 PPM &gt; USL PPM Total 131.20Process Capability = 0.92  Sigma Level = 3*0.92 = 2.7614AnalyzeAnalysis Information  1  ­ 20  47 (171 ) Pareto Chart for Frequency100 150 80Percent1 2 3 4  ()   (Knot)  (Slub)  (Snarl)Count10060 4050 20 0 01 3 2 e rs OthDefectCount Percent Cum %119 69.6 69.626 15.2 84.822 12.9 97.74 2.3 100.0SKP  171   740.56 Kg  4.33 Kg/AnalyzeAnalysis Information  1  ­ 20  47 (171 )   2  , 108.18, 15% , 99.5, 13% , 487.72, 66% , 45.16, 6% 66%     80%·   ·  ·   ·  ·  ·  13%  /15AnalyzeAnalysis Information?   (Yarn Carrier)      (Incomplete Loop)     (Knitting Element)  AnalyzeAnalysis Information  (Knitting Element)   Descriptive StatisticsVariable: CleaningAnderson-Darling Normality Test A-Squared: P-Value: Mean StDev Variance Skewness Kurtosis N Minimum 1st Quartile Median 3rd Quartile Maximum 2.681222.7 2.8 2.9 3.088.3% Pareto Chart for Cleaning1002.77778 0.63965 0.409150 0.233039 -6.5E-01 171 2.00000 2.00000 3.00000 3.00000 4.00000 2.87434 17.481 0.000150 80Count23410060 4050 20 0 03 2 495% Confidence Interval for Mu95% Confidence Interval for Mu 95% Confidence Interval for Sigma 0.57828 95% Confidence Interval for Median 0.71570 95% Confidence Interval for Median 3.00000 3.00000DefectCount Percent Cum %93 54.4 54.458 33.9 88.320 11.7 100.0Percent16AnalyzeAnalysis Information       SKP  43   78  Pareto Chart for Fan100 150 80Pareto Chart for Auto Clean100 150 80PercentCount100Count60 4010060 4050 20 0 0s Ye No50 20 0 0s Ye NoDefectCount Percent Cum %DefectCount Percent Cum %152 88.9 88.919 11.1 100.0161 94.2 94.210 5.8 100.0 4 m/c  9.30% 5 m/c  11.63%AnalyzeAnalysis Information   SKP  43   78  Pareto Chart for Door100 150 80 100 150 80Pareto Chart for Guide100CountCount60 4010060 4050 20 0 0s Ye No in Ru50 20 0 0s Ye NoDefectCount Percent Cum %DefectCount Percent Cum %94 55.0 55.063 36.8 91.814 8.2 100.096 56.1 56.175 43.9 100.0 4 m/c  9.30% 18 m/c  41.86%PercentPercentPercent17AnalyzeAnalysis Information   10   SKP  43   78  Pareto Chart for Clatcher100 150 80 150 80Pareto Chart for Yarn100PercentCount100Count60 4010060 4050 20 0 0s Ye No50 20 0 0s Ye ers OthDefectCount Percent Cum %DefectCount Percent Cum %86 50.3 50.385 49.7 100.0170 99.4 99.41 0.6 100.0 19 m/c  44.19% Ne 10S  1 AnalyzeAnalysis Information   SKP  171  Pareto Chart for Start Up Chk100 150 80 150 80Pareto Chart for Fabric Chk100PercentCount100Count60 4010060 4050 20 0 0s Ye No50 20 0 0s Ye NoDefectCount Percent Cum %DefectCount Percent Cum %108 63.2 63.263 36.8 100.090 52.6 52.681 47.4 100.0 36.80% 47.40%PercentPercent18AnalyzeAnalysis Information ·  (%RH)  65% ·   · / ·    /  · /  AnalyzeAnalysis Information ()·  ·  Tightness Factor &quot;K&quot;   K &quot;&quot;   ·  ·  ·  4 /19ImproveImprovementMachine Approach·         ·  ·  Daily Check          ImproveImprovementMan Approach·             Visual Control ·   OJT   Visual Control  3  ·  4 /   ()20ImproveImprovement· DOE  24 Factorial  Cylinder  27 .. 47  1 .. 47 ImproveImprovementMain Effects Plot (data means) for score-11-11-11-118.7Fractional Factorial Fit: score versus point, clean, checkscore7.9Estimated Effects and Coefficients for score (coded units) Term Constant point clean check point*clean Effect 1.0937 3.0938 2.1562 -0.7812 Coef SE Coef T 7.1094 0.1035 68.66 0.000 0.5469 0.1035 5.28 0.000 1.5469 0.1035 14.94 0.000 1.0781 0.1035 10.41 0.000 -0.3906 0.1035 -3.77 0.003 P7.16.35.5 point pressure clean checkAnalysis of Variance for score (coded units) Source DF Main Effects 3 2-Way Interactions 1 Residual Error 11 Lack of Fit 3 Pure Error 8 Total 15 Seq SS 61.6680 2.4414 1.8867 0.2930 1.5938 65.9961 Adj SS Adj MS 61.6680 20.5560 119.85 0.000 2.4414 2.4414 14.23 0.003 1.8867 0.1715 0.2930 0.0977 0.49 0.699 1.5938 0.1992 F P1 -1Interaction Plot (data means) for score-1 1 -1 1 -1 1point9 7 5pressure1 -19 7 5clean1 -19 7 5check21ImproveNew D 1.0000 Hi Cur Lo point 1.0 [-1.0000] -1.0 clean 1.0 [-1.0] -1.0 check 1.0 [1.0] -1.0ImprovementNew D 1.0000 Hi Cur Lo point 1.0 [-1.0000] -1.0 clean 1.0 [1.0] -1.0 check 1.0 [-1.0] -1.0score Maximum y = 5.7031 d = 1.0000score Maximum y = 7.4219 d = 1.0000New D 1.0000Hi Cur Lopoint 1.0 [1.0000] -1.0clean 1.0 [-1.0] -1.0check 1.0 [-0.9215] -1.0New D 1.0000Hi Cur Lopoint 1.0 [1.0000] -1.0clean 1.0 [1.0] -1.0check 1.0 [-1.0] -1.0score Maximum y = 5.5065 d = 1.0000score Maximum y = 7.7344 d = 1.0000    ImproveMachinery Cleaning Procedure 1 .      2.   22ImproveMachinery Cleaning Procedure 3.    4.  ImproveMachinery Cleaning Procedure 5 .     6.    23ImproveMachinery Cleaning Procedure7.  1   8.   ImproveMachinery Cleaning Procedure9.   1  10.   &quot;1&quot;  24ImproveFabric Inspection Procedure     ImproveImprovement SKP  48-1  6 // 22/11/2004 23/11/2004 24/11/2004 25/11/2004 26/11/2004 27/11/2004 28/11/2004 29/11/2004 30/11/2004 01/12/2004 02/12/2004 03/12/2004 06/12/2004 07/12/2004  11,880.3 13,879.2 12,699.9 12,417.7 13,709.4 13,652.7 13,109.2 12,247.8 13,671.8 12,931.9 13,941.5 14,126.1 12,076.8 13,899.6  %  15.80 40.95 12.30 23.78 12.30 8.85 6.80 9.10 14.39 6.55 6.05 13.00 2.55 7.97 0.13 0.30 0.10 0.19 0.09 0.06 0.05 0.07 0.11 0.05 0.04 0.09 0.02 0.06// 08/12/2004 09/12/2004 10/12/2004 11/12/2004 12/12/2004 13/12/2004 14/12/2004 15/12/2004 16/12/2004 17/12/2004 18/12/2004 19/12/2004 20/12/2004 21/12/2004  12,528.5 14,593.3 14,128.7 15,950.3 14,161.0 13,203.3 13,109.4 12,857.3 11,693.5 12,151.9 11,723.1 11,714.4 10,885.6 10,993.6  %  32.42 13.47 7.78 36.87 9.07 17.90 0.00 11.51 0.00 7.04 41.82 9.45 7.58 8.04 0.26 0.09 0.06 0.23 0.06 0.14 0.00 0.09 0.00 0.06 0.36 0.08 0.07 0.07// 22/12/2004 23/12/2004 24/12/2004 25/12/2004 26/12/2004 27/12/2004 28/12/2004 04/01/2005 05/01/2005 06/01/2005 07/01/2005 08/01/2005 09/01/2005  12,445.7 11,765.4 11,925.8 11,279.4 10,695.6 11,614.6 1,885.2 7,492.8 11,804.3 12,193.1 12,310.8 13,503.6 12,664.8 %  2.70 11.95 0.00 13.15 26.10 0.00 0.00 0.00 4.05 0.00 7.30 14.80 11.85 0.02 0.10 0.00 0.12 0.24 0.00 0.00 0.00 0.03 0.00 0.06 0.11 0.09 0.09505,518.9 475.2425ImproveImprovement SKP  48-1  6 Week 48 49 50 51 52 1   91,348.4 66,919.1 97,338.2 86,452.9 79,991.1 83,469.2  %  120.78 49.09 110.13 87.72 69.52 38.00 0.13 0.07 0.11 0.10 0.09 0.05 0.09505,518.9 475.24ImproveImprovement SKP  48-1 Descriptive StatisticsVariable: %LossAnderson-Darling Normality Test A-Squared: P-Value: Mean StDev Variance Skewness Kurtosis N Minimum 1st Quartile Median 3rd Quartile Maximum 0.0616770.06 0.07 0.08 0.09 0.10 0.11 0.12 0.130.139 0.942 9.17E-02 2.86E-02 8.17E-04 -2.5E-01 -4.7E-01 6 0.050000 0.065000 0.095000 0.115000 0.130000 0.121657 0.070089 0.1228570.050.070.090.110.1395% Confidence Interval for Mu95% Confidence Interval for Mu 95% Confidence Interval for Sigma 0.017838 95% Confidence Interval for Median 0.057143 95% Confidence Interval for MedianP-Value &gt;= 0.05   Normal Distribution26ImproveImprovement Normality Test  Normal Probability Plot.999 .99 .95Probability.80 .50 .20 .05 .01 .001 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13%LossAverage: 0.0916667 StDev: 0.0285774 N: 6 Anderson-Darling Normality Test A-Squared: 0.139 P-Value: 0.942P-Value &gt;= 0.05   Normal DistributionImproveImprovement (Process Capability)  Process Capability Analysis for %LossUSL* * 0.091667 6Process Data USL Target LSL Mean Sample N 0.200000Within OverallStDev (Within) 0.0283688 StDev (Overall) 0.0300330Potential (Within) Capability Cp CPU CPL Cpk Cpm Overall Capability Pp PPU PPL Ppk * 1.20 * 1.20 * 1.27 * 1.27 *0.000.05Observed Performance0.10Exp. &quot;Within&quot; Performance * 0.00 0.00 PPM &lt; LSL PPM &gt; USL PPM Total0.150.20Exp. &quot;Overall&quot; PerformancePPM &lt; LSL PPM &gt; USL PPM Total* 67.06 67.06PPM &lt; LSL PPM &gt; USL PPM Total* 154.79 154.79Process Capability = 1.20  Sigma Level = 3*1.20 = 3.6027ImproveImprovementParameter Mean StDev N SigmaBeforeAfter0.101446 0.091667 0.035308 0.028577 18 2.76 6 3.60ImproveActual Benefit: Benefits from ImprovementHard Saving :· Expected Benefit : 146,018 / · Actual Benefit : 8,762.32 /***  : %Loss      -  -  scrap  Benefit Loss  Opportunity Loss  = = = = = = = 0.10 ­ 0.09 = 0.01% 337,012.6 Kg /  33.7 Kg /  130  / Kg 337,012.6 * 130 * 0.01% 4,381.16  Benefit Loss·Total Hard Saving 8,762.32 /28ControlControlConclusionKey Tools Used1.Brain Storming 2.Pareto Diagram 3.Process Mapping 4.Cause &amp; Effect Diagram 5.Statistical Technique (Descriptive Statistic, Process Capability Analysis) 6.Design of Experiment (DOE) 7.Control Plan29ControlConclusionImprovement Action 1. Daily Check  2.  SKP  Visual Control 3.  OJT    4.  SKP KPI ControlConclusionAchievement 1. 0.10%  0.09% 2. Sigma Level  2.76  3.60 3. (saving)  8,762.32 / Lesson Learned 1. project  scope  2. 3. project  BB &amp; GB30`

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