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• © 2003 FleetBoston Financial Page
• © 2003 FleetBoston Financial Page
• © 2003 FleetBoston Financial Page
• © 2003 FleetBoston Financial Page
• © 2003 FleetBoston Financial Page
• © 2003 FleetBoston Financial Page
• ### Six sigma

1. 1. Introduction to Six Sigma
2. 2. Topics (Session 1)♦ Understanding Six Sigma♦ History of Six Sigma♦ Six Sigma Methodologies & Tools♦ Roles & Responsibilities♦ How YOU can use Six Sigma
3. 3. Six Sigma is. . .♦ A performance goal, representing 3.4 defects for every million opportunities to make one.♦ A series of tools and methods used to improve or design products, processes, and/or services.♦ A statistical measure indicating the number of standard deviations within customer expectations.♦ A disciplined, fact-based approach to managing a business and its processes.
4. 4. What’s in a name?♦ Sigma is the Greek letter representing the standard deviation of a population of data.♦ Sigma is a measure of variation (the data spread) σ μ
5. 5. What does variation mean? 20♦ Variation means that a 15 process does not produce 10 the same result (the “Y”) 5 every time. 0 -5♦ Some variation will exist in -10 all processes.♦ Variation directly affects customer experiences. Customers do not feel averages!
6. 6. Measuring Process PerformanceThe pizza delivery example. . .♦ 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?
7. 7. How often are we delivering ontime?Answer: Look at 30 min. or lessthe variation! s 0 10 20 x 30 40 50♦ Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening.
8. 8. Reduce Variation to ImprovePerformanceHow many standard 30 min. or lessdeviations can you“fit” within scustomerexpectations? 0 10 20 x 30 40 50♦ Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).
9. 9. Managing Up the Sigma Scale Sigma % Good % Bad DPMO 1 30.9% 69.1% 691,462 2 69.1% 30.9% 308,538 3 93.3% 6.7% 66,807 4 99.38% 0.62% 6,210 5 99.977% 0.023% 233 6 99.9997% 0.00034% 3.4
10. 10. Examples of the Sigma ScaleIn a world at 3 sigma. . . In a world at 6 sigma. . .♦ There are 964 U.S. flight ♦ 1 U.S. flight is cancelled every cancellations per day. 3 weeks.♦ The police make 7 false arrests ♦ There are fewer than 4 false every 4 minutes. arrests per month.♦ In MA, 5,390 newborns are ♦ 1 newborn is dropped every 4 dropped each year. years in MA.♦ In one hour, 47,283 ♦ It would take more than international long distance calls 2 years to see the same number are accidentally disconnected. of dropped international calls.
11. 11. Topics♦ Understanding Six Sigma♦ History of Six Sigma♦ Six Sigma Methodologies & Tools♦ Roles & Responsibilities♦ How YOU can use Six Sigma
12. 12. The Six Sigma Evolutionary Timeline 1818: Gauss uses the normal curve 1924: Walter A. Shewhart introduces to explore the mathematics of error the control chart and the distinction of analysis for measurement, probability special vs. common cause variation as analysis, and hypothesis testing. contributors to process problems. 1736: French 1896: Italian sociologist Vilfredo mathematician Alfredo Pareto introduces the 80/20 Abraham de rule and the Pareto distribution in Moivre publishes Cours d’Economie Politique. an article introducing the normal curve. 1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures 1960: Kaoru Ishikawa for Performing a Failure Mode Effects introduces his now famous and Criticality Analysis. cause-and-effect diagram.1941: Alex Osborn, head of 1970s: Dr. Noriaki KanoBBDO Advertising, fathers a introduces his two-dimensionalwidely-adopted set of rules for quality model and the three“brainstorming”. types of quality. 1986: Bill Smith, a senior engineer and scientist introduces the 1995: Jack Welch concept of Six Sigma at Motorola launches Six Sigma at GE. 1994: Larry Bossidy launches Six Sigma at Allied Signal.
13. 13. Six Sigma Companies
14. 14. Six Sigma and Financial Services
15. 15. Topics♦ Understanding Six Sigma♦ History of Six Sigma♦ Six Sigma Methodologies & Tools♦ Roles & Responsibilities♦ How YOU can use Six Sigma
16. 16. DMAIC – The Improvement Methodology Define Measure Analyze Improve ControlObjective: Objective: Objective: Objective: Objective:DEFINE the MEASURE current ANALYZE the IMPROVE the CONTROL the opportunity performance root causes of process to process problems eliminate root to sustain the gains. causesKey Define Tools: Key Measure Key Analyze Key Improve Key Control• Cost of Poor Tools: Tools: Tools: Tools: Quality (COPQ) • Critical to Quality • Histograms, • Solution • Control Charts• Voice of the Requirements Boxplots, Multi- Selection Matrix • Contingency Stakeholder (CTQs) Vari Charts, etc. • To-Be Process and/or Action (VOS) • Sample Plan • Hypothesis Tests Map(s) Plan(s)• Project Charter • Capability • Regression• As-Is Process Analysis Analysis Map(s) • Failure Modes• Primary Metric and Effect (Y) Analysis (FMEA)
17. 17. Define – DMAIC ProjectWhat is the project? \$ Project Cost of Charter Poor Voice of Quality S ta k e h o ld e r s the Stakeholde r Six Sigma♦ What is the problem? The “problem” is the Output (a “Y” in a math equation Y=f(x1,x2,x3) etc).♦ What is the cost of this problem♦ Who are the stake holders / decision makers♦ Align resources and expectations
18. 18. Define – As-Is ProcessHow does our existing process work? Move-It! Courier Package Handling Process Accounts Accounts Courier Mail Clerk In-SortClerk In-SortSupervisor DistanceFeeClerk WeightFeeClerk Out-SortClerk Out-SortSupervisor ReceivableClerk Supervisor Observ e package weight (1 or 2) on back of package Look up appropriate Weight Fee and write in top middle box on package back Add Distance & Take packages Weight Fees f rom Weight Fee together and write Clerk Outbox to in top right box on A/R Clerk Inbox. package back Circle Total Fee Does EVERYONE and Draw Arrow f rom total to sender code agree how the currentAccounting Take packages Write Total Fee f rom A/R Clerk f rom package in process works? Outbox to appropriate Accounts Sender column on Superv isorInbox. Accts. Supv .’s log Take packages Draw 5-point Star f rom Accounts in upper right Superv isor corner of package Outbox to Out- f ront Sort Clerk Inbox. Define the Non Value Sort packages in order of Sender Code bef ore placing in outbox Take packages Add steps Add up Total # of Observ e senderFinalizing f rom Out-Sort Packages and and receiv er Clerk Outbox to Total Fees f rom codes and make Out-Sort log and create entry in Out-Sort Superv isorInbox. client inv oice Superv isor’s log Deliv erPackagesDelivery to customers according to N, S, E, W route Deliv er inv oiceto client Submit log to Submit log to Submit log to General Manager General Manager General Manager at conclusion of at end of round at end of round round.
19. 19. Define – Customer Requirements What are the CTQs? What motivates the customer? Voice of the Customer Key Customer Issue Critical to Quality SECONDARY RESEARCH Market Data Industryl e n yrt s udn I Benchmarking Customer t I Correspondence Customer Services s o P gn ne s L i t i PRIMARY RESEARCH t Surve Surve ys ys OTM Obser- Focus Groups vations
20. 20. Measure – Baselines and Capability What is our current level of performance? Descriptive Statistics♦ Sample some data / not all data Variable: 2003 Output♦ Current Process actuals measured against Anderson-Darling Normality Test A-Squared: 0.211 P-Value: 0.854 the Customer expectation Mean StDev 23.1692 10.2152♦ Variance 104.349 What is the chance that we will succeed Skewness Kurtosis N 0.238483 0.240771 100 0 10 20 30 40 50 at this level every time? Minimum 1st Quartile 0.2156 16.4134 Median 23.1475 3rd Quartile 29.6100 Maximum 55.2907 Pareto Chart for Txfr Defects 95% Confidence Interval for Mu 95% Confidence Interval for Mu 21.1423 25.1961 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 95% Confidence Interval for Sigma 100 100 8.9690 11.8667 95% Confidence Interval for Median 95% Confidence Interval for Median 80 19.7313 26.0572 60 Percent Count 50 40 20 0 0 t un er s Defect La te A mo Oth Count 79 17 4 Percent 79.0 17.0 4.0 Cum % 79.0 96.0 100.0
21. 21. Measure – Failures and RisksWhere does our process fail and why?Subjective opinion mapped into an “objective” risk profile number Failure Modes and Effects Analysis (FMEA) Process/ProductProcess or Prepared by: Page ____ of ____Product Name:Responsible: FMEA Date (Orig) ______________ (Rev) _____________ Process S O D R S O D R Step/Part E C E P Actions E C E P Number Potential Failure Mode Potential Failure Effects V Potential Causes C Current Controls T N Recommended Resp. Actions Taken V C T N X1 0 0 0 0 X2 0 0 0 0 X3 0 0 0 0 X4 0 0 0 0 0 0 etc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
22. 22. Analyze – Potential Root CausesWhat affects our process? Ishikawa Diagram (Fishbone) Six Sigma y = f (x1, x2, x3 . . . xn)
23. 23. Analyze – Validated Root CausesWhat are the key root causes? Pareto Chart for Txfr Defects 100 100 80 Percent 60 Count 50 40 20 0 0 nt er s E x p e r im e n t a l D e s ig n Defect La te Am ou Oth Count 79 17 4 Percent 79.0 17.0 4.0 Cum % 79.0 96.0 100.0 Data RegressionStratification Analysis Pareto Chart for Amt Defects 15 100 80 Process Simulatio Percent 60 Count 10 40 5 20 n 0 0 cy al er Defect Cu rren Cle ric Oth Count 12 3 2 Percent 70.6 17.6 11.8 Cum % 70.6 88.2 100.0 Six Sigma y = f (x1, x2, x3 . . . xn) Critical Xs
24. 24. Improve – Potential Solutions How can we address the root causes we identified? ♦ Address the causes, not the symptoms. Generate Evaluate Clarify Decisiony = f (x1, x2, x3 . . . xn) Critical Xs Divergent | Convergent
25. 25. Improve – Solution SelectionHow do we choose the best solution? Solution Selection Matrix Qualit Solution Sigma Time CBA Other Score yTime Cost Right Six Sigma Solution Wrong Nice ☺ Try Solution Implementatio Nice n Plan X doo G da B not a ne ml p m e I Idea i t
26. 26. Control – Sustainable BenefitsHow do we ”hold the gains” of our new process? ♦ Some variation is normal and OK ♦ How High and Low can an “X” go yet not materially impact the “Y” ♦ Pre-plan approach for control exceptions Process Control System (Business Process Framework)Process Owner: Direct Process Customer: Date:Process Description: CCR: Flowchart Measuring and Monitoring Measures Key Specs (Tools) Responsibility Contingency Measure &/or Remarks Where & (Who) (Quick Fix) ments Targets Frequency P1 - activity 35 duration, min. UCL=33.48 P2 - # of incomplete Individual Value loan applications 25 Mean=24.35 15 LCL=15.21 0 10 20 30 Observation Number
27. 27. DFSS – The Design MethodologyDesign for Six SigmaDefine Measure Analyze Develop Verify ♦ Uses – Design new processes, products, and/or services from scratch – Replace old processes where improvement will not suffice ♦ Differences between DFSS and DMAIC – Projects typically longer than 4-6 months – Extensive definition of Customer Requirements (CTQs) – Heavy emphasis on benchmarking and simulation; less emphasis on baselining ♦ Key Tools – Multi-Generational Planning (MGP) – Quality Function Deployment (QFD)
28. 28. Topics♦ Understanding Six Sigma♦ History of Six Sigma♦ Six Sigma Methodologies & Tools♦ Roles & Responsibilities♦ How YOU can use Six Sigma
29. 29. Champions♦ Promote awareness and execution of Six Sigma within lines of business and/or functions♦ Identify potential Six Sigma projects to be executed by Black Belts and Green Belts♦ Identify, select, and support Black Belt and Green Belt candidates♦ Participate in 2-3 days of workshop training
30. 30. Black Belts♦ Use Six Sigma methodologies and advanced tools (to execute business improvement projects♦ Are dedicated full-time (100%) to Six Sigma♦ Serve as Six Sigma knowledge leaders within Business Unit(s)♦ Undergo 5 weeks of training over 5-10 months
31. 31. Green Belts♦ Use Six Sigma DMAIC methodology and basic tools to execute improvements within their existing job function(s)♦ May lead smaller improvement projects within Business Unit(s)♦ Bring knowledge of Six Sigma concepts & tools to their respective job function(s)♦ Undergo 8-11 days of training over 3-6 months
32. 32. Other Roles♦ Subject Matter Experts – Provide specific process knowledge to Six Sigma teams – Ad hoc members of Six Sigma project teams♦ Financial Controllers – Ensure validity and reliability of financial figures used by Six Sigma project teams – Assist in development of financial components of initial business case and final cost-benefit analysis
33. 33. Topics♦ Understanding Six Sigma♦ History of Six Sigma♦ Six Sigma Methodologies & Tools♦ Roles & Responsibilities♦ How YOU can use Six Sigma
34. 34. Questions?
35. 35. Topics for Detailed Discussion♦ Problem Identification♦ Cost of Poor Quality♦ Problem Refinement♦ Process Understanding♦ Potential X to Critical X♦ Improvement
36. 36. Problem Identification“If it ain’t broke, why fix it“This is the way we’ve always done it…”
37. 37. Problem Identification • First Pass Yield • Roll Throughput Yield • Histogram • Pareto
38. 38. Problem Identification First Pass Yield (FPY): The probability that 100 Units any given unit can go Step 1 Outputs / Inputs through a system defect-free without 100 / 100 = 1 rework. 100 Scrap 10 Units Step 2 90 / 100 = .90 90 Scrap 3 Units Step 3 87 / 90 = .96 87 Scrap 2 Units Step 4 85 / 87 = .97At first glance, the yield would seem to be When in fact the FPY is (1 x .90 x .96 x .97 = .85% (85/100 but….) 838) 85
39. 39. Problem IdentificationRolled 100 Units Outputs / InputsThroughputYield (RTY): Step 1 90 / 100 = .90The yield ofindividual Re-Workprocess steps 10 Units 100 Unitsmultiplied Step 2 97 / 100 = .97together.Reflects the Re-Workhidden factory 3 Units 100 Unitsrework issuesassociated with Step 3 98 / 100 = .98a process. Re-Work 2 Units 100 Units Step 4 .90 x .97 x .98 = .855 100 Units
40. 40. Problem Identification RTY Examples - Widgets 50 Roll Throughput Yield Function 1 50/50 = 1 (50-5)/50 = .90 50 (50-10)/50 = .80 Function 2 5 (50-5)/50 = .90 50 Function 3 10 1 x .90 x .80 x .90 = .65 50 Function 4 5 Put another way, this process is operating 50 a 65% efficiency
41. 41. Problem Identification RTY Example - Loan Underwriting 50 Roll Throughput Yield Application 50/50 = 1 (50-7-2)/50 = .82 2 50 7 Fails (43-6)/43 = .86 Underwrite Underwriting (43-1-2)/43 = .93 6 43 Complete Full Paperwork 1 x .82 x .86 x .93 = .66 2 1 43 Decide not to Close borrow 42 Put another way, this process is operating a 66% efficiency
42. 42. Problem Identification Histogram – A histogram is a basic graphing tool that displays the relative frequency or occurrence of continuous data values showing which values occur most and least frequently. A histogram illustrates the shape, centering, and spread of data distribution and indicates whether there are any outliers. Histogram of Cycle Time 40 30 Frequency 20 10 0 0 100 200 300 400 500 C8
43. 43. Problem Identification Histogram – Can also help us graphically understand the data Descriptive Statistics Variable: CT Anderson-Darling Normality Test A-Squared: 6.261 P-Value: 0.000 Mean 80.1824 StDev 67.6003 Variance 4569.81 Skewness 2.31712 Kurtosis 8.26356 N 170 25 100 175 250 325 400 Minimum 1.000 1st Quartile 31.000 Median 66.000 3rd Quartile 105.000 95% Confidence Interval for Mu Maximum 444.000 95% Confidence Interval for Mu 69.947 90.417 54 64 74 84 94 95% Confidence Interval for Sigma 61.098 75.664 95% Confidence Interval for Median 95% Confidence Interval for Median 55.753 84.494
44. 44. Problem Identification Pareto – The Pareto principle states that 80% of the impact of the problem will show up in 20% of the causes. A bar chart that displays by frequency, in descending order, the most important defects. Pareto Chart for WEB 100 100 80 Percent 60 Count 50 40 20 0 0 EB ers Defect No n-W Oth eb) (W Count 96 15 Percent 86.5 13.5 Cum % 86.5 100.0
45. 45. Topics (Session 2)♦ Problem Identification♦ Cost of Poor Quality♦ Problem Refinement♦ Process Understanding♦ Potential X to Critical X♦ Improvement
46. 46. Cost of Poor QualityCOPQ - The cost involved in fulfilling the gap between the desired andactual product/service quality. It also includes the cost of lost opportunitydue to the loss of resources used in rectifying the defect. Hard Savings - Six Sigma project benefits that allow you to do the same amount of business with less employees (cost savings) or handle more business without adding people (cost avoidance). Soft Savings - Six Sigma project benefits such as reduced time to market, cost avoidance, lost profit avoidance, improved employee morale, enhanced image for the organization and other intangibles may result in additional savings to your organization, but are harder to quantify. Examples / Buckets– Roll Throughput Yield Inefficiencies (GAP between desired result and current result multiplied by direct costs AND indirect costs in the process). Cycle Time GAP (stated as a percentage between current results and desired results) multiplied by direct and indirect costs in the process. Square Footage opportunity cost, advertising costs, overhead costs, etc…
47. 47. Topics (Session 2)♦ Problem Identification♦ Cost of Poor Quality♦ Problem Refinement♦ Process Understanding♦ Potential X to Critical X♦ Improvement
48. 48. Problem Refinement Multi Level Pareto – Logically Break down initial Pareto data into sub- sets (to help refine area of focus) Pareto Chart for WEB 100 100 80 Percent 60Count 50 40 20 0 0 Pareto Chart for Type B WE ersDefect No n- Oth eb) (W 100 Count 96 15 100Percent 86.5 13.5 80Cum % 86.5 100.0 Percent 60 Count 50 40 20 0 0 g oi n nG al ime dO ers Defect An nu On eT im e an Oth eT On Count 45 35 13 16 Percent 41.3 32.1 11.9 14.7 Cum % 41.3 73.4 85.3 100.0
49. 49. Problem Refinement Problem Statement – A crisp description of what we are trying to solve. Primary Metric – An objective measurement of what we are attempting to solve (the “y” in the y = f(x1, x2, x3….) calculation). Secondary Metric – An objective measurement that ensures that a Six Sigma Project does not create a new problem as it fixes the primary problem. For example, a quality metric would be a good secondary metric for an improve cycle time primary metric.
50. 50. Problem Refinement Fish Bone Diagram - A tool used to solve quality problems by brainstorming causes and logically organizing them by branches. Also called the Cause & Effect diagram and Ishikawa diagram Provides tool for exploring cause / effect and 5 whys
51. 51. Topics (Session 2)♦ Problem Identification♦ Cost of Poor Quality♦ Problem Refinement♦ Process Understanding♦ Potential X to Critical X♦ Improvement
52. 52. Process UnderstandingSIPOC – Suppliers, Inputs, Process, Outputs, CustomersYou obtain inputs from suppliers, add value through your process, andprovide an output that meets or exceeds your customers requirements.
53. 53. Process UnderstandingProcess Map – should allow people unfamiliar with the process to understandthe interaction of causes during the work-flow. Should outline Value Added(VA) steps and non-value add (NVA) steps. Full Form Control Open Start Size Sorts Pull & Sort Receipt / Docs Extract Ck / Vouch Verify Perfection Requal Group No Yes Prep cks, Remit Rulrs route Prep cks Ship to IP Pass 1 Pass 2 vouch Vouchers Key from Balance Data Cap image No Vouch OK Inventory Yes Prep Folders / Full Form Ship to Box QCReview Cust
54. 54. Process Understanding Create daily peak Action staff need plan Plan No Yes Can they Call employee Add 30% to To Floor the required make it? (3x) no.Operations No Need OJT Yes Make No Compare to OJT Re-Tng it? Check off original Billet rpt desired Manually Review returnee Yes Update HR Staff staff & "need No Yes Billet Request Billet Need re to retrain" To Floor -train list Add 40% to Call (3x) Stop! staff needed Create Update Staff No IPS No Billet Rev Do they original Do they No Send Letters Yes Yes Have we No Yes Have we No Yes Interview / Meet Fleet Do they want to billet & want to Call Wait Rank as to desired hired hired New hiring respond? work this call work this List pre-hire "1 2 3" staff enough? enough? criteria peak? uncheck peak? ed What if the HR sends Hire in 1- Yes returnee is Yes Yes req for No No 2 order Start already staffing (3s are HR / working here show up No nos. not Place into Call Recruit on another Do they Do they orienta No No placed) dept 3X program? want to want to tion Stop! Stop! Currently stay on the stay on the send the ltr list list anyways Wait List Yes Yes Yes New & Other Take off Set 14 Take off Set 14 People IPS month IPS month call in system flag (on system flag (on IPS?) IPS?) schedule Yes No Gen Event Roster for Reach rpt in IPS training Show No Call Notify up? 1X HR Yes Training Gen rpt for Ops Kronos Recruit Train No Yes Update Pass? IPS
55. 55. Topics (Session 2)♦ Problem Identification♦ Cost of Poor Quality♦ Problem Refinement♦ Process Understanding♦ Potential X to Critical X♦ Improvement
56. 56. Potential X to Critical X“Y” is the dependent output of a variable process. In otherwords, output is a function of input variables (Y=f(x1, x2,x3…).Through hypothesis testing, Six Sigma allows one todetermine which attributes (basic descriptor (generallylimited or binary in nature) for data we gather – ie. day ofthe week, shift, supervisor, site location, machine type,work type, affect the output. For example, statistically,does one shift make more errors or have a longer cycletime than another? Do we make more errors on Fridaysthan on Mondays? Is one site faster than another? Once wedetermine which attributes affect our output, we determinethe degree of impact using Design of Experiment (DOE).
57. 57. Potential X to Critical XA Design of Experiment (DOE) is a structured, organizedmethod for determining the relationship between factors(Xs) affecting a process and the output of that process (Y).Not only is the direct affect of an X1 gauged against Y butalso the affect of X1 on X2 against Y is also gauged. Inother words, DOE allows us to determine - does one input(x1) affect another input (x2) as well as Output (Y).
58. 58. Potential X to Critical XDOE Example Main Effects Plot (data means) for Elapsed Main Effects Plot – 1.4 Lo w Hi g h Lo w Hig h Lo w Hig h Lo w Hig h Direct impact to Y 1.3 Elapsed 1.2 1.1 1.0 Jams DCDEL SK P2Jam Interaction Plot (data means) for Elapsed 1 3 1 3 1 3 1 3 1.50 Jams 1 1.25 3 1.00 1.50 DCDEL 3 1.25 Interaction Plot – 1 1.00 1.50 SK Impacts of X’s on 3 1 1.25 1.00 each other P2Jam 1.50 3 1.25 1 1.00
59. 59. Potential X to Critical XDOE Optimizer –Allows us tostatistically predict theOutput (Y) based onoptimizing the inputs(X) from the Design ofexperiment data.
60. 60. Topics (Session 2)♦ Problem Identification♦ Cost of Poor Quality♦ Problem Refinement♦ Process Understanding♦ Potential X to Critical X♦ Improvement
61. 61. ImprovementOnce we know the degree to which inputs (X) affect ouroutput (Y), we can explore improvement ideas, focusingon the cost benefit of a given improvement as it relatesto the degree it will affect the output. In other words, wegenerally will not attempt to fix every X, only those thatgive us the greatest impact and are financially orcustomer justified.
62. 62. ControlOnce improvements are made, the question becomes, are theimprovement consistent with predicted Design of Experimentresults (ie – are they what we expected) and, are they statisticallydifferent than pre-improvement results. Process Capability Analysis for Sept LSL USL Process Data USL 0.23000 Within Target * LSL -1.00000 Overall Mean -0.02391 Sample N 23 StDev (Within) 0.166425 StDev (Overall) 0.221880 Potential (Within) Capability Z.Bench 1.53 Z.USL 1.53 Z.LSL 5.87 Cpk 0.51 -1.0 -0.5 0.0 0.5 1.0 Cpm * Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance Z.Bench 1.14 % < LSL 0.00 % < LSL 0.00 % < LSL 0.00 Z.USL 1.14 % > USL 13.04 % > USL 6.35 % > USL 12.62 Z.LSL 4.40 % Total 13.04 % Total 6.35 % Total 12.62 Ppk 0.38
63. 63. ControlControl Chart - A graphical tool for monitoring changes that occurwithin a process, by distinguishing variation that is inherent in theprocess(common cause) from variation that yields a change to theprocess(special cause). This change may be a single point or a seriesof points in time - each is a signal that something is different fromwhat was previously observed and measured. I and MR Chart for Sept 1 Individual Value 0.5 UCL=0.5293 0.0 Mean=0.03 2 -0.5 LCL=-0.4693 Subgroup Sept 13 Sept 20 Date 9/13 9/25 0.7 1 0.6 UCL=0.6134 Moving Range 0.5 0.4 0.3 0.2 R=0.1877 0.1 0.0 LCL=0