This presentation covers the entire aspects of 6 sigma quality methodology. You can have this presentation as a reference to anything related to 6 sigma. This is one of the best material to be refereed before the implementation of 6 sigma in your organization, whether it is in service sector or in manufacturing..

1. ADVISORY SERVICES Six Sigma Green Belt Training Program

2. Prelude to Six Sigma Expectations form the Organization

3. Drivers of Project Selection Voice Of The Employee Business Big Y s Process Ys Y Y Y Y Project Y X 1 Any parameters that influence the Y Bigger Ys Keep an Outside - In perspective Strong Linkage between Projects & Big Ys is Important Key project metric d efined from the customer perspective Key output metrics that summarize process performance Key output metrics that are aligned with the strategic goals / objectives of the business. Big Ys provide a direct measure of business performance X 2 X 3 Voice Of The Customer Voice Of The Shareholder

6. Affinity Diagram – Credit Card Example Organize VOC into broad categories Low Interest Rate Variable Terms Pay Back When I Want No Prepayment Penalties/ Charges Pre-Approved Credit Easy Application Easy Access To Capital Quick Decision Know Status Of Loan (Post- Approval) Will Come To My Facility Available Outside Normal Business Hours Available When I Need To Talk Responsive To My Calls Knowledgeable Reps Professional Make Me Feel Comfortable Patient During Process Knows About My Finances Knows About My Business Makes Finance Suggestions Talk To One Person Friendly Cares About My Business Has Access To Experts Provides Answers To Questions Calls If Problems Arise All Charges Clearly Stated Know Status Of Loan During Application Preference If Bank Customer Can Apply Over Phone Verbatim VOC High-Level Needs Flexible Product Easy Process Availability Personal Interface Advice/ Consulting

7. Translating VOC into CTQ’s Identify customer segments that need to be targeted Gather verbatim VOC & Determine Service Quality Issue Translate to needs statement & develop a CTQ - Project Y metric output characteristic VOC CTQs Customers Requirements determines quantifiable Process Metrics “CTQ’

8. Example: Translating VOC to CTQ’s What gets measured gets managed… ensure measurable CTQs “ You take too much time in getting back to me!” “ These forms are too cumbersome!” - Quick Response - User Friendly Forms - Process Turn Around Time not more than 10 min. - Form < 2 pages & < 10 minutes to complete Validate CTQ with customer Verbatim Specific Needs CTQs

9. Identify “Must Be’s” affecting CTQ’s PROJECT CTQ COMPLIANCE BUSINESS PROCESS EFFECTIVENESS INTEGRITY OF COMMUNICATIONS CONTROLLERSHIP & BUSINESS STRATEGY EMPLOYEES CUSTOMERS INTERNAL UPSTREAM / DOWNSTREAM CUSTOMERS COST & COMPETITORS SHAREHOLDERS &

10. Prioritizing CTQ’s – Kano Model Kano Model helps to prioritize our efforts towards satisfying customers Satisfaction + Dissatisfaction One-Dimensional Delighters Must Be Innovation Competitive Priority Critical Priority Functional Dysfunctional Safe arrival Accurate booking Baggage arrives with passenger 99% system uptime Seat comfort Quality of refreshments Friendliness of staff Baggage speed On-Time arrival Free upgrades Individual movies and games Special staff attention/services Computer plug-ins (power sources)

13. Work out session – Identify area of improvement

14. Contents Define Customer Expectations of the process? Measure What is the frequency of defects? Analyze Why, Where & When do defects occur? Improve How can we fix the Process? Control How can we keep the process fixed?

15. Define Customer Expectations of the Organization

16. Contents - Define CHARTER DETERMINE THE PROJECT CTQs Business Case Problem & Goal Statement Project Scope Milestones Roles & Responsibility Identify your customers Gather “VOC” Organize VOCs Prioritize VOCs Translate VOC to CTQs MAP THE PROCESS Process Operational Definition Benefits of Process Mapping SIPOC Model Levels of Mapping Mapping Guidelines DEFINE THE PROJECT In-Frame/Out-Frame 15 Word Flip Chart Backward Imaging Deliverables: CTQs Identified Charter SIPOC 1 2 3 4

18. Sample Project Charter

19. Sample Project Charter CTQ: Accuracy

24. Process Mapping & Benefits Measures P S I O C Process Map Suppliers Inputs Process Outputs Customers CTQs CTQs Measures

26. Levels of Process Mapping Core Process (Level I) Subprocesses (Level 2) Subprocesses (Level 3) Exports Imports Negotiation Microprocesses (Level 4 & Below) Credit Ops Messaging Payments Payments Collections Trades Billing

31. G.R.P.I

36. Work out session – Create a Project Charter

37. Key Learning Points and Summary

38. Measure Quantification of problem and causes

39. Measure Deliverables: Data Collection Plan MSA Results Data Plots VARIATION SELECT PROJECT Y Display & Describe Variation Causes of Variation Run Chart Bar Chart Normal Curve Measures CTQ Prioritization Types of Data Fishbone Diagram DEVELOP DATA COLLECTION PLAN 1 2 Establish Data collection Plan Define Operational Definitions Define Sampling Procedures Measurement System Analysis 3

40. Select Project Y Essential to validate the linkage between Project CTQ, Process CTQs & Big Ys VOC CTQs Process CTQs/ Process Metrics Big ‘Y’s Project CTQ/ Project Ys Retention Rate Employer of Choice Attrition % Example :

43. Based on SIPOC Y = f(X 1 , X 2 , X 3 …………………. X n ) Output Y is a function of various Inputs and Process Steps Measures can be classified as Input, Process or Output Measures Garbage in Garbage out How good is my process Accurate?? Timely?? INPUT PROCESS OUTPUT Productive??

46. Based on Statistics Binary Ordered categories Count Classified into one of two categories Rankings or ratings Counted discretely Measured on a continuous scale Is this group ready to travel 100 km daily to reach office Yes or No Decision of Group Description Example Discrete Continuous Continuum of Data Types Classification of the group into categories based on distance each travels daily to reach office Categories : (0 - 2 Kms) (2- 5 Kms) (5-10 Kms) (10-20 Kms) (20-50 Kms) (50-100 Kms) Number of people who have come late today Actual distance traveled by each in this group measured in Kms

50. Cause & Effect Diagram Why do we have Late Processing Creation of Bank ID, Cust ID Bunching of Documents Printer Problem Awaiting Memo Replies (Clarifications) Late Scanning & Receipt Forced Priority of deals Lack of Typing Skill Lack of Training Program for new recruits Fear of committing errors Availability of Imex for long hours Alternations of Errors making in previous steps Instns not clear Handwriting not legible Printing not clear Learning Curve To many amendments To many deals held with same person Complicated Deals Linked Deals not getting released on time Absenteeism Bank Profile with wrong Swift IDs Full doc not scanned Correction of errors in previous deal Delays current deall Changes made in operational data Changes in Bill category by Spoke Man Material Machine Measurement Mother nature Method High Level Prompters to help generate possible causes Write the effect here Write the possible causes here Or use 4 P’s

55. Work out session – Complete till Fish Bone and Data Collection in the project

69. Measurement System Analysis (MSA) TOTAL VARIANCE Apparent variation = Process variation + Measurement variation Double Challenge: Reduce variation in both processes!

71. GRR Example

72. GRR Example

73. GRR Example

74. GRR Example

76. AAA - Example

77. AAA - Example

78. AAA - Example

82. Work out session – Measurement System Analysis

83. Tools Displaying Data Variation Discrete Data Continuous Data x Frequency Diagram Box Plot Histogram Run Chart Pie Chart Bar Chart For a period of Time Over a period of Time

90. Normal Probability Plot 12 13 14 15 16 17 18 0 1 2 3 4 5 6 7 7 9 11 13 15 17 19 21 23 0 1 2 3 4 5 6 Frequency Bimodal Distribution Bimodal curve Normal Probability Plot for an Exponential Distribution 0 10 20 30 40 50 60 70 80 90 0 10 20 Exponential Distribution Frequency Exponential Curve 7 9 11 13 15 17 19 21 23 0 1 2 3 4 5 6 Frequency Long Tailed Distribution Percent Normal Probability Plot For Long-Tailed Distribution 0 10 20 30 1 5 10 20 30 40 50 60 70 80 90 95 99 “ S” curve Normal Probability Plot for a Normal Distribution Roughly Normal Distribution Straight Line 0 10 20 30 1 5 10 20 30 40 50 60 70 80 90 95 99 Percent Normal Probability Plot for a Bimodal Distribution Percent

94. Box Plot Box Plot is another tool to visually display process dispersion Highest Value Third Quartile (75%) value Lowest Value First Quartile (25%) value Median * Each segment represents 25% of the data points Outlier * *

98. Patterns Observed in Run Charts Shift / Mixtures Trend Same Value / Clusters Cycle / Oscillation

99. Run Charts

100. Run Charts

102. Analyze Confer the assumption

104. Rolled Throughput Yield Final Yield (FY) Yield at the end of the process excluding scrap Final Yield, FY = U/S = Units Passed/Units Submitted FY = (100-30)/100 = .70 or 70% Step 1 Step 2 Step 3 70 Units 100 Units 10 Units 10 Units 10 Units Scrap

105. Rolled Throughput Yield Final Yield Ignores the Hidden Factory Step 1 Step 2 Step 3 70 Units 100 Units 10 Units 10 Units 10 Units Scrap Rework 10 Units 10 Units 10 Units HIDDEN FACTORY

106. Rolled Throughput Yield Classical Yield Ignores the Role of Hidden Factory Operation Verify Product Rework HIDDEN FACTORY Scrap

107. Rolled Throughput Yield Rolled Throughput Yield (RTY) Step 1 Step 2 Step 3 100 Units 10 Units 10 Units 10 Units Scrap Rework 10 Units 10 Units 10 Units HIDDEN FACTORY Product of Throughput Yields across the entire process 90 Units 80 Units = 30 Units = 30 Units FTY 90 % FTY 88.9 % FTY 87.5 % = 100 Units FTY 70 % TPY 80 % TPY 77.8 % TPY 75.0 % RTY .80 RTY .778 RTY .75 = 46.7 % X X Probability of Zero Defects

109. Sigma Calculation Process Sigma (Z ST) Defects Per Million Opportunities (DPMO) 6 99.99966% 3.4 5 99.9770% 230 4 99.3790% 6,210 3 93.320% 66,800 2 69.20% 308,000 Defects in the Process defects w.r.t performance standards Percentage

112. Abridged Sigma Table

122. Pareto Chart Approximately 80% of Defects from Defects D+B+F. 80 % of the effect on Y is caused by 20 % of the factors (X). Frequency Cumulative Percentage 100 90 80 70 60 50 40 30 20 10 Number Of Units Investigated: 8,000 April 1 – June 30 D B F A C E Other 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 Type of Defect Cumulative Summation Line (Cum Sum line) *f = frequency f* of D f* of D+B f* of D+B+F A: Typographical Errors B: Incomplete Info C: Sign not verified D: Illegible E : Process understanding F: Poor Scan Quality LEGEND

131. Types of Waste Type of waste Example Complexity Unnecessary steps, excessive documentation, too may permission needed Labor In efficient operations, excess head count Overproduction Producing more than the customer demands. Producing before the customer needs it Space Storage for inventory, parts awaiting disposition, parts awaiting rework and scrap storage. Excessively wide aisles. Other wasted space Energy Wasted power or human energy Defects Repair, rework, repeated service, multiple calls to resolve problems Materials Scrap, ordering more than is needed Idle materials Material that just sits, inventory Time Waste of time Transportation Movement that adds no value Safety Hazards Unsafe or accident-prone environments

135. Cycle Time Constituents Process Time + Delay Time = Total Cycle Time Where & Why are we spending the highest time?? What kind of activities are these…. VA / NVA / VE?? Why should we retain activities which are NVA and contributing to total TAT??? Process Step 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 TOTAL % TOTAL % STEPS Time Taken 1 120 15 120 3 180 7 1 120 5 10 15 90 15 120 2 120 5 8 957 100 %

136. Process Flow Analysis Looking at the process together…..work flow & value analysis Process Step 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Total % Total % Steps Est. Avg. Time (Mins) 1 120 15 120 3 180 7 1 120 5 10 15 90 15 120 2 120 5 8 957 100% Value-Added 48 5% Nonvalue-Added - Internal Failure 180 18.80% - External Failure - Control/ Inspection 8 0.80% - Delay 690 72.10% - Prep/Set-Up - Move 30 3.10% - Value-Enabling 1 0.10% Total 957 100%

146. One Sample t Test

147. One Sample t Test

148. Homogeneity of Variance

149. Homogeneity of Variance

150. Two sample t test

151. Two sample t test

152. ANNOVA

154. Chi Square

155. Chi Square

156. Chi Square

162. Some Scenarios For all charts: Y = Participant satisfaction (scale: 1 – worst to 100 – best) X = Trainer experience (# of hours) No Correlation Positive Correlation Strong Positive Correlation Other Pattern Negative Correlation Strong Negative Correlation 1 2 3 4 5 6

164. Word of Caution Lesson learnt : Correlation doesn’t imply causation Growing Population of Human Beings Growing Population of ants

166. Regression Regression Equation: Y = mX + C, where C = Predicted Value Of Y When X = 0 m= Slope Of Line Change in Y Per Unit Change in X X Y Y = mX + C Line of Best Fit

167. Forecasting and optimization are complex Come on! It can‘t go wrong every time...

168. Work out session – Complete the project till choosing hypothesis & Minitab session

170. Key Learning Points and Summary

171. Improve Fix the process (es)

172. Improve GENERATE/SELECT SOLUTIONS REFINE SOLUTION FMEA Error Proofing Brainstorming DOE Criteria Matrix NGT Pilot planning Verification of Results TEST SOLUTION Idea Screening Tools Cost Benefit Analysis JUSTIFY SOLUTION TRIZ Deliverables: Solution Design Developed Solution Tested on a small Scale Cost Benefit Analysis

173. Improve - Steps Creative Approach Refine the Idea Test the new Idea Analytical Approach Justify $ Select Best Idea Brainstorming Data Analysis (DOE) Filter Error Proofing / FMEA Pilot

180. Response Relationship to Other Tools Inputs Outputs Projector has bright light Projector is quiet Colors correct Power On Bulb life Instructor Training Computer Interface Projector Process Map Cause & Effect Diagram for Bright Light Low Bulb Brightness Measurement Person Machine Method Environment Room Brightness Instructions Light Meter Instructor Power On Computer Settings Bulb Rating of Importance to Customer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Process Inputs Bright Light Quiet Color Correct Total 1 119 2 95 3 23 4 95 5 6 7 Power on Bulb Life Instructor Computer 9 3 2 8 8 1 3 3 1 1 1 2 2 1 1 9 8 7 8 Rating of Importance to Customer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Process Inputs Bright Light Quiet Color Correct Total 1 119 2 95 3 23 4 95 5 6 7 Power on Bulb Life Instructor Computer 9 3 2 8 8 1 3 3 1 1 1 2 2 1 1 9 8 7 8 Cause & Effect Matrix

181. Factor Relationship to Other Tools Inputs Outputs Projector has bright light Projector is quiet Colors correct Power On Bulb life Instructor Training Computer Interface Projector Process Map Cause & Effect Diagram for Bright Light Low Bulb Brightness Measurement Person Machine Method Environment Room Brightness Instructions Light Meter Instructor Power On Computer Settings Bulb Rating of Importance to Customer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Process Inputs Bright Light Quiet Color Correct Total 1 119 2 95 3 23 4 95 5 6 7 Power on Bulb Life Instructor Computer 9 3 2 8 8 1 3 3 1 1 1 2 2 1 1 9 8 7 8 Rating of Importance to Customer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Process Inputs Bright Light Quiet Color Correct Total 1 119 2 95 3 23 4 95 5 6 7 Power on Bulb Life Instructor Computer 9 3 2 8 8 1 3 3 1 1 1 2 2 1 1 9 8 7 8 Cause & Effect Matrix

182. Experimental Design Considerations Experimental Design Types Experimental Objective Full Factorial (replication) RSM (Response Surface Method) Optimize Model Fractional Designs OFAT (One Factor at a Time) 2 k Full Factorial Screen 2 k Full Factorial ( w center points/replication) Few Many KPIV’s Less More KNOWLEDGE Less More COST

183. Factors and the Process DOE can be applied to both business and industrial processes Y: Accounts Receivable Hold Business Process Manufacturing Process Y: Scrap Reduction X 1 Temp X 2 Press X 3 Time X 1 Price X 2 PO X 3 Terms

184. Coding Three Factors Low-1 High+1 Press = 2 Temp = 10 Resin = 56 Press = 10 Temp = 125 Resin = 3560 -1 +1 press -1 +1 temp resin +1 Coding convention often uses “+” for high and “-” for low

188. TRIZ – Key Concepts

198. N G T: Nominal Group Technique What is the best method to reduce cost per transaction? Simplify Application process & consolidate Sites