R-DMAIC-D Six Sigma--TBI

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1. R-DMAIC-D Six Sigma Prepared by Julian Kalac, P.Eng Lean Six Sigma Master Black Belt Define Measure AnalyzeImprove Control Results Define Measu re AnalyzeImprove Control Results 2. 2 Use data-driven, measurement-based, statistical methods to Solve problems, improve performance Focus: Surgical inch-wide, mile-deep investigation and resolution Approach: Solve problems at the system and root cause level Implement robust control plans for sustained improvements What is Six Sigma? An Analytical Methodology that Focuses on Reducing Process Variation 3. Six Sigma as a Metric 1 )( 2 n xxi Sigma = = Deviation ( Square root of variance ) -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 Axis graduated in Sigma 68.27 % 95.45 % 99.73 % 99.9937 % 99.999943 % 99.9999998 % result: 317300 ppm outside (deviation) 45500 ppm 2700 ppm 63 ppm 0.57 ppm 0.002 ppm between + / - 1 between + / - 2 between + / - 3 between + / - 4 between + / - 5 between + / - 6 = 4. 4 Identify customer metrics Select performances standards Select objectives Map the process Validate measurement System (MSA) RESULT: Process maps and good quality data collected by listening to the process Identify sources of variation & failure points Redefine and Re-prioritize Establish process capability Identify improvement opportunities RESULT: The critical sources of variation identified root causes determined Monitor processes to prevent recurrence of variation, defects and non-value work Maintain performance levels find more creative ways to improve fix root causes Find/Implement Preventive Fixes Deploy changes organization RESULT: Performance is more predictable ; culture changing Plan and apply Improve Tools to fix problems and reduce variation Implement improvement opportunities RESULT: Improve tools applied, changes implemented and performance improvement in place DMAIC Improvement Model A Road Map for guiding Improvement Projects Identify customer problems Identify performances standards Identify improvement objectives Link problem to the key performance metrics Find the right problems RESULT: Problem Statement & Project Charter Define Measure AnalyzeImprove Control Results Define Measure AnalyzeImprove Control Results 5. 5 Define Measure AnalyzeImprove Control Results Define Measure AnalyzeImprove Control Results The R-DMAIC D Model Recognize-DMAIC-Deploy: Extend Performance Improvements Applied Learning Theory Practice Coached Projects Training typically occurs over a 4-6 month period using a Learn Apply; Learn Apply; format. Projects that solve real performance problems in your organization are part of the certification and learning process Recognize: Find problems Link to organization's needs Form teams, define resources Understand program reqts Deploy: Validate improved performance that stays in place Spread improvements across the enterprise Harvest the performance improvements Change the culture 6. 8 Defining Projects Starts with Problems 2) Clearly Define your Problem and the Impact Example: Too many mistakes in purchase request specifications are causing rework rates of 34%, high costs and late deliveries (less than 50% on time) to our customers 1) Find Customer Issues Performance Reviews Meetings New Projects Failures, Re-work Projects Continuous improvement activities Champions Stakeholders Employees SCOR Maps Research Brainstorm Wait until the crisis hits you Value Stream Maps 3) Clearly State the Initial Scope for your project (Set targets and timelines!) Example: This first phase of this project by 3Q 2004 - will identify problems and root causes in the purchase request process, increase first pass yield rates to 95%, reduce the cost of poor quality by 50% and increase on-time deliveries to our customers to greater than 90% 4) Link improvement targets to customer needs and organizational objectives 5) Project Plan Charter, Resources, Milestones 7. 9 Process Flow for Measure Phase Data Storage and Archiving Foundations for Wisdom and good decision making start with Good Data What data do we need? Can we use old historical data? Is old data (still) usable? How was it collected? Wisdom Knowledge Data Information Wisdom Knowledge Data Information Wisdom Knowledge Data Information Measure Phase Process Flow How to Get Good Data Data Selection Data Integrity Analysis Data Collection Measurement System Analysis Process Mapping 8. 10 SIPOC Diagrams X1 = _____ Y1 = _____ X2 = _____ Y2 = _____ X1 = _______ Y1 = _______ X2 = _______ Y2 = _______ X3 = _______ Y3 = _______ SIPOC Diagrams can be characterized as a 3-step, high-level (30,000-foot) Process Flow Diagram for a process Critical to Customer Quality Requirements (the CTQs) Key Inputs (Materials & Resources) and Key Process Input Variables (KPIVs) Inputs Processes OutputsSupplier Customer Secondary Metric (e.g. Field Returns) Primary Metric (e.g. Scrap $/Month 9. 11 SIPOC Process Improvement Model Supplier provides inputs Inputs: materials, equipment, information, people, money, environmental conditions Process: activities & tasks that transform inputs Outputs: product or service delivered to the customer Customer receives outputs PI OKPIV KPOV CS FEEDBACK FEEDBACK 10. Measurement System Analysis (MSA) Is your error in the process or in the way you measure it? Could it be that you actually are good but the error in the measurement system shows that you are not good? Overall Variation Occurrence-to- Occurrence ( or Piece- to-Piece) Variation Measurement System Variation Repeatability: Variation due to gage or measurement tool Reproducibility: Variation due to people or operators who are measuring TV= MSA + Process variation 11. 13 Repeatability Repeatability is the variation in measurements obtained with one measurement instrument used several times by one appraiser while measuring the identical characteristic on the same part. For example: Manufacturing: One person measures the purity of multiple samples of the same vial and gets different purity measures. Transactional: One person evaluates a contract multiple times (over a period of time) and makes different determinations of errors. Repeatability Y Source: iSixSigma 12. 14 Reproducibility Reproducibility is the variation in the average of the measurements made by different appraisers using the same measuring instrument when measuring the identical characteristic on the same part. For example: Manufacturing: Different people perform purity test on samples from the same vial and get different results. Transactional: Different people evaluate the same contract and make different determinations. Reproducibility Operator A Operator B Y Source: iSixSigma 13. 15 Organize & Understand your data Inferential Statistics Descriptive Statistics Sort, Collate, Investigate your data Analyze Phase Process Flow Transitioning from Data to Information and Knowledge Organize your data and put it into some sort of perspective, concept, picture or visual representation that is easier to understand Use maps, graphs, charts, summaries, spread sheets, etc., that organize the data Wisdom Knowledge Data Information Wisdom Knowledge Data Information Wisdom Knowledge Data Information 14. 16 We know we must change X to create a change in Y But how do we know which Xs to change and how to change them ? Y =f (x1, x2, ) process output key process input factors affecting process output function of 15. Identify Significant Factors (xs) 17 P value< 0.5--Significant 16. 18 Hypothesis Testing Not different (Ho) Reality TestDecision Different (H1) Not different (Ho) Different (H1) Correct Conclusion Correct Conclusion a Risk Type I Error Producer Risk Type II Error b risk Consumer Risk Test Reality = Different Decision Point b Risk a Risk 17. 19 Process Capability Cp USLLSL Voice of the Customer Voice of The Process Voice of the Customer Voice of the Process Capability Ratio - compares the capability of a process (voice of the process) to the specification limits (voice of the customer): = USL - LSL 6s = Cp Cp = 1: The process is barely capable (Just fits into the tolerance window). Cp = 2: The process is a six sigma process (The tolerance window is twice the process capability). 18. 20 Process Capability Indices Process capability relates actual product variability to the customer specifications. The Cp index estimates process capability if the process mean is centered in the specification window (Process Entitlement). The Cpk estimates the process capability if the process mean is off center. )Capability6( SpecLower-SpecUpper Cp 3) Z Cap3( SpecNearestMean =Cpk 19. 21 Illustration of Cp Cp = 0.5 Cp = 1.0 Cp = 1.5 Cp = 2.0 20. Black Belt Training 22 7565554 535 66,800 ppm Barely Capable Process: With Mean Shift Lower Specification Upper Specification 21. Graph>Box plot 75% 50% 25% Graph>Box plot Without X values DBP Box plots help to see the data distribution Day DBP 10 9 10 4 99 94 109 104 99 94 Operator DBP 10 9 10 4 99 94 Shift DBP 10 9 10 4 99 94 22. 24 Types of Data Charts 23. Box Plot Analyze -jun07 25 0 5 10 15 20 25 30 35 40 45 50 Data Set #1 Brown Blue Green Yellow Red Orange Box Plot Titles Q1 Min Median Max Q3 Other formats Using Statistical Software 24. 26 Pareto Chart A method to display the vital few from the trivial many. These charts are based on the Pareto Principle 20% of the problems have 80% of the impact. The 20% represents the vital few. The Pareto chart helps you to arrange data in order of priority or importance. 90 30 20 10 5 Frequency Categories Percentage 75% 25% 50% 20 180 140 100 60 TITLE n = sample size (time period) LEGEND REFERENCE INFO date initials source 100 % 25. 33% 52% 66% 78% 90% 100% 0 100 200 300 400 500 600 700 800 brown red orange yellow blue green 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 Your Completed Pareto Chart 26. Statistical Analysis 0.0250.0200.0150.0100.0050.000 7 6 5 4 3 2 1 0 New Machine Frequency 0.0250.0200.0150.0100.0050.000 30 20 10 0 Machine 6 mths Frequency Is the factor really important? Do we understand the impact for the factor? Has our improvement made an impact What is the true impact? Hypothesis Testing Regression Analysis 55453525155 60 50 40 30 20 10 0 X Y R-Sq = 86.0 % Y = 2.19469 + 0.918549X 95% PI Regression Regression Plot Apply statistics to validate actions & improvements 27. 29 Improve Phase Process Flow (1) Select - (2) Apply - (3) Implement/Deploy PROBLEM FINDING FACT FINDING PROBLEM DEFINITION IDEA FINDING EVAL. & SELECT PLAN ACCEPT -ANCE ACTION 1 2 3 4 6 7 8 5 Select Tool Analyze Data & Information Apply tools & Make Changes Six Sigma Projects 5S PM/TPM TQM Rapid Improve- ment Event Integrated Process Team (IPT) Establish Baselines/Metrics Other CI Tools Kanban DFSS ApplytheTool (1) (2) (3)Implement / Deploy Fixes (0) Identify failure points, bottlenecks and improvement opportunities from Define-Measure-Analyze Phases PM = Preventive Maintenance TPM = Total Productive Maintenance CI = Continuous Improvement DFSS = Design for Six Sigma TQM = Total Quality Management 28. THE PROCESS PROCESS OUTPUTS CONTROLLED VARIABLES CUSTOMER PROCESS INPUTS UNCONTROLLED NOISE VARIABLES Design of Experiments (DOE) 29. P-value= 0.045 DESIGN OF EXPERIMENT Final Yield= 35.44-0.9xFFT 30. P-value= 0.045 FINAL Yield= 35.44-0.901xFFT 31. DOE RESULTS FINAL YIELD=35.44-0.901xFFT FINAL YIELD=35.44-0.901xFFT FINAL FILTRATION TIME (FFT) SIGNFICANTLY impacts Final Yield (inversely) The greater Final Filtration Time the lower the Final Yield If you control FFT you control Final Yield So what do we need to do to minimize final filtration time? 32. Virus Removal 33. FINAL FILTRATION 34. DOE RESULTSImplement Final Filtration Controls Replace filters whenever FFT > 0.5 Hrs Monitor FFT SPC trends 35. Control Phase Control Tools: Control Plan POKA YOKE Control Charts Statistical Process Control (SPC) Go/No-Go checks Preventative Maintenance (Spare Filters) 39 36. 40 Control Phase Process Flow Sustaining the Improvements Maintaining and Improving Control: Managing Change Quality Management Systems Whats Left? Whats Next? Control Tools: POKA YOKE Statistical Process Control (SPC) Spec Control Document Procedures and Best Practices Create Visual Controls and Dashboards Update Deployment Plan Complete Control FMEA Update and Deploy Communication Plan Execute Deployment Plan Document Other Opportunities Prepare and Implement Control Plan Report out, Hand- off and Closure Prepare and Implement Training Plan Process Flow Diagram for Control Phase: 37. 41 In preparing a control plan, you should concentrate on: Control Plan Elements 1) What needs to be monitored? 2) Who is going to be keeping the process functioning properly? 3) How are they going to monitor? 4) Where will the monitoring be conducted? 5) Where should the plan reside? 6) What will be done if the monitoring detects a condition outside of the customers specifications? 38. 42 Control Plan Key Process Input Variable Key Process Output Variable Process Specification (LSL, USL, Target) Capability (Cp, Cpk, DPMO) and date Measurement Technique Sample Size Sample Frequency Control Element Contigency Remedial Audit Phone Call LSL - NA TGT- 10 sec USL - 30 secs Cpk=.85 05 Dec 03 Call Center Manager Dashboard 30 1/2 hourly CCM to adjust seated agents CCM to reprioritize worload to meet specification Daily report to be examined by Supervisor N New Customer LSL - NA TGT- 15 min USL - 30 min Cpk=. 48 05 Dec 03 New Acct Dept Visual Display 100 Weekly Nacct Mgr to monitor and adjust new Acct Reps Nacct Mgr to reprioritize Monthly report to NAM regional manager Y New Accounts 0 Field defects DPMO = 66289 05 Dec 03 New Acct Dept Visual Display 100 Weekly Nacct Mgr to monitor and adjust new Acct Reps Nacct Mgr to use Pareto Analysis to determine defect category Monthly report to NAM regional manager Y Lead Time LSL - NA TGT- 5 Days USL - 10 Days Cpk = 1.43 05 Dec 03 Operations Dashboard 30 Daily Ops Mgr Review Identify trend root cause Director Review Weekly N Customer Expedite Expedite Order 0 Expedited DPMO = 24721 05 Dec 03 Operations Dashboard 25 Weekly Ops Mgr Review Identify trend root cause Director Review Monthly Y Key Process Input Variable Key Process Output Variable Process Specification or Target Output Value Current Performance Value Sample Size Sample Frequency Control Element Contingency Plans Remedial Plans Is the Performance Audited 39. DPMO / Control Chart 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000 70000 75000 80000 85000 90000 95000 100000 2/5/2 0012/19/20013/5/2 0013/19/20014/2/2 0014/16/20014/30/20015/14/20015/28/20016/11/20016/25/20017/9/2 0017/23/20018/6/2 0018/20/20019/3/2 0019/17/200110/1/2001 10/15 /2001 10/29 /2001 11/12 /2001 11/26 /2001 12/10 /2001 12/24 /2001 DPMO Baseline DPMO UCL LCL Mean Goal Process Change REGIONAL CONTROL BEGINS PAST BKLOG PAST BKLOG P-DUE PAST UNITS DUE PM DUE LUBE SVC DUE REGION ASSIGN PM DAYS LUBE DAYS GOAL SVC NETWORK 2327 1 0 107 4 93 77 CENTRAL 151 0 0 8 4.8 6 8 F.B.U. 74 0 0 0 0 3 2 MIDWEST 178 2 1 9 4.6 7 3 NORTHEAST 312 0 0 33 9.7 12 18 SOUTHERN 389 0 0 43 10.1 16 36 WESTERN 259 0 0 19 6.7 10 11 TOTAL 3689 3 0 219 5.4 150 155 OFFLINE 203 4 1.8 37 16.8 1 4 NEW ACCOUNT EXCEPTION SUMMARY 12/08/2003 Control Dashboards A Visual Display for the Key Control Elements and Metrics Item No. Problem/Opportunity Benefit Person(s) Responsible Due Date % Complete Comments 1 2 3 4 5 6 7 8 Kaizen Event Newspaper Team: Kaizen Event: Kaizen Newspaper The Control Plan explains how the Dashboard Works and what to do if performance goes out of controlImprovement Actions and Activities Updated: __/__/__ By:________ 40. Monitor FFT Filter integrity and performance 41. Proposed LFB SPARE PARTS LIST Item # Equipment Description Qty 1 ANX bubble Trap 1 2 Methyl/Heparin bubble trap 1 3 UV light 2 4 Chromaflow Nozzle 1 5 Variable Column Seal Kit 2 6 Valve Seal 2 7 Chromaflow Frits 2 8 Digital Pressure Gauge 0-100psi 1 9 Digital Pressure Gauge -30-100psi 1 10 Seal Kit Nozzel 1 11 CIP Pump 1 12 ANX & 43-029 UF/DF Retentate Backup Flow Meter/Display 1 13 Heparin Backup Flow Meter/Display 1 14 Vaccuum Pump 1 15 Feed Pump Impeller Turbine 1 16 Citrate addition valve 2 17 360 Casters & hardware 1 18 Conductivity Sensor (Probe) 2 42. Control Charts 43. 47 Deploy Phase Spreading change Across the Organization Deploy: Validate improved performance that stays in place Spread improvements across the enterprise Harvest the performance improvements Change the culture Where else can you tale the improvements Look further upstream Look further downstream What about other customers What about re-fixing processes you already fixed 44. 48 Kaizen Events & TPM SPC Six Sigma (GB &BB) D.O.E Which Tools to Apply Intuitive/ Common Sense Data Driven Non- Value Added Value Added Type of Variation Special Cause Variation Common Cause Variation Lean vs 6 95% vs 5% CONTINUUM 5S PIT&Brainstorming CreativeProblemSolving Process Content Where do Visual Displays Fit In? Visual Displays Identify and Communicate Where You Are 45. 49 Kaizen Events & TPM SPC Six Sigma (GB &BB) D.O.E Which Tools to Apply Intuitive/ Common Sense Data Driven Non- Value Added Value Added Type of Variation Special Cause Variation Common Cause Variation Lean vs 6 95% vs 5% CONTINUUM 5S PIT&Brainstorming CreativeProblemSolving Process Content Where do Kaizen Events Fit In? Ci2 2009 46. 50 Kaizen Events & TPM SPC Six Sigma (GB &BB) D.O.E Which Tools to Apply Intuitive/ Common Sense Data Driven Non- Value Added Value Added Type of Variation Special Cause Variation Common Cause Variation Lean vs 6 95% vs 5% CONTINUUM 5S PIT&Brainstorming CreativeProblemSolving Process Content Where do Green Belt/Black Belt Projects Fit In? Ci2 2009 47. Foundations for Wisdom and good decision finding the right problem Numbers, words, quantities, values stored sitting in piles or queues waiting for future use Charts, summaries, spread sheets, etc. that organize the data Presentations, plans and tools that explain and communicate the information Processes, organizations and team using the information to improve, manage, build systems and develop cultures Push the data up to become Wise Always loop back to check the data Wisdom Knowledge Data Information Where are you looking from? The WISDOM TOWER: Changing Perspectives