DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)1 Measurement and Analysis Interaction (A1) Measure Hypothesize stratification factors Compute baseline metrics Study process and plan for measurement of Y variables Data Exploration: Find patterns related to treatment differences (i.e. stratification) Hypothesis Generation for X (cause) variables with no stratification Measure Hypothesis Generation for X (cause) variables within stratification Is there a stratification pattern? Are there new stratification factors to consider? NO YES Any probable stratifications within the re- focused data set? Verify Causes NO YES More measurements required? Measure YES NO

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)2 Output of Measurement Selection (A1) Project Y variable(s) (CTQ) identified and linked to problem/goal Project Y variable(s) (CTQ) identified and linked to problem/goal At least one X variable (predictor) to help find cause of Y variable At least one X variable (predictor) to help find cause of Y variable Start with stratification factor as initial type of X variable Start with stratification factor as initial type of X variable Plan for making sure you know: Plan for making sure you know: Where to collect measurements Where to collect measurements Data is available Data is available It is feasible (time, money, personnel) to collect data It is feasible (time, money, personnel) to collect data Exercise: CTQ Tree Exercise: CTQ Tree Exercise: Measurement Assessment Tree Exercise: Measurement Assessment Tree Project Y variable(s) (CTQ) identified and linked to problem/goal Project Y variable(s) (CTQ) identified and linked to problem/goal At least one X variable (predictor) to help find cause of Y variable At least one X variable (predictor) to help find cause of Y variable Start with stratification factor as initial type of X variable Start with stratification factor as initial type of X variable Plan for making sure you know: Plan for making sure you know: Where to collect measurements Where to collect measurements Data is available Data is available It is feasible (time, money, personnel) to collect data It is feasible (time, money, personnel) to collect data Exercise: CTQ Tree Exercise: CTQ Tree Exercise: Measurement Assessment Tree Exercise: Measurement Assessment Tree

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)3 Output of Operational Definition (A2) Clear, concise, detailed, unambiguous description of what is being measured Clear, concise, detailed, unambiguous description of what is being measured Definitions of key terms like defect, product and service Definitions of key terms like defect, product and service Guidelines on how to interpret the routine and the unusual Guidelines on how to interpret the routine and the unusual Initial data collection plan for what (sets up the when and how) Initial data collection plan for what (sets up the when and how) Use Operational Definition Worksheet (pg. 169) Use Operational Definition Worksheet (pg. 169) Clear, concise, detailed, unambiguous description of what is being measured Clear, concise, detailed, unambiguous description of what is being measured Definitions of key terms like defect, product and service Definitions of key terms like defect, product and service Guidelines on how to interpret the routine and the unusual Guidelines on how to interpret the routine and the unusual Initial data collection plan for what (sets up the when and how) Initial data collection plan for what (sets up the when and how) Use Operational Definition Worksheet (pg. 169) Use Operational Definition Worksheet (pg. 169)

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)4 Output of Identifying Data Sources (A3) Identification of existing data sources that will meet some (or all) measurement needs. Criteria for acceptable existing data include: Identification of existing data sources that will meet some (or all) measurement needs. Criteria for acceptable existing data include: Used the same operational definitions developed for the project collection efforts (especially in agreeing with customer definitions) Used the same operational definitions developed for the project collection efforts (especially in agreeing with customer definitions) Structured to support analysis stage (i.e. has required stratification factors) Structured to support analysis stage (i.e. has required stratification factors) Identification of new data sources to needed to meet requirements Identification of new data sources to needed to meet requirements Validating of ability to access and sort existing data Validating of ability to access and sort existing data Identification of existing data sources that will meet some (or all) measurement needs. Criteria for acceptable existing data include: Identification of existing data sources that will meet some (or all) measurement needs. Criteria for acceptable existing data include: Used the same operational definitions developed for the project collection efforts (especially in agreeing with customer definitions) Used the same operational definitions developed for the project collection efforts (especially in agreeing with customer definitions) Structured to support analysis stage (i.e. has required stratification factors) Structured to support analysis stage (i.e. has required stratification factors) Identification of new data sources to needed to meet requirements Identification of new data sources to needed to meet requirements Validating of ability to access and sort existing data Validating of ability to access and sort existing data

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)5 Prepare Data Collection and Sampling Plan (A4) Identify/confirm stratification factors Identify/confirm stratification factors Must begin with some idea of the “end game” Must begin with some idea of the “end game” Data exploration (analysis stage) lives or dies on decisions made here Data exploration (analysis stage) lives or dies on decisions made here Develop sampling scheme Develop sampling scheme Create data collection forms Create data collection forms Identify/confirm stratification factors Identify/confirm stratification factors Must begin with some idea of the “end game” Must begin with some idea of the “end game” Data exploration (analysis stage) lives or dies on decisions made here Data exploration (analysis stage) lives or dies on decisions made here Develop sampling scheme Develop sampling scheme Create data collection forms Create data collection forms

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)6 Developing the Sampling Scheme (A4.2) Choice – Population or Process sampling? Choice – Population or Process sampling? Population sampling: Large (essentially infinite), homogeneous pool of data Population sampling: Large (essentially infinite), homogeneous pool of data Process sampling: Sample taken from a “running process stream” Process sampling: Sample taken from a “running process stream” Ref: Tables 9-1 and 10-2 and Figures 10-6 to Ref: Tables 9-1 and 10-2 and Figures 10-6 to Accounting for “sampling bias” Accounting for “sampling bias” Bad sampling processes: convenience sampling and judgment sampling Bad sampling processes: convenience sampling and judgment sampling Good sampling processes: systematic sampling, random sampling, stratified sampling Good sampling processes: systematic sampling, random sampling, stratified sampling Setting the Confidence Interval (CI) (Detailed discussion at end of Measure Stage of DMAIC model) Setting the Confidence Interval (CI) (Detailed discussion at end of Measure Stage of DMAIC model) Typical interval is set at 95% (this is Minitab default) Typical interval is set at 95% (this is Minitab default) Must know something about process to ballpark the sample size for a 95% CI Must know something about process to ballpark the sample size for a 95% CI Exercise: Manual Sample size calculation (pg ) Exercise: Manual Sample size calculation (pg ) Choice – Population or Process sampling? Choice – Population or Process sampling? Population sampling: Large (essentially infinite), homogeneous pool of data Population sampling: Large (essentially infinite), homogeneous pool of data Process sampling: Sample taken from a “running process stream” Process sampling: Sample taken from a “running process stream” Ref: Tables 9-1 and 10-2 and Figures 10-6 to Ref: Tables 9-1 and 10-2 and Figures 10-6 to Accounting for “sampling bias” Accounting for “sampling bias” Bad sampling processes: convenience sampling and judgment sampling Bad sampling processes: convenience sampling and judgment sampling Good sampling processes: systematic sampling, random sampling, stratified sampling Good sampling processes: systematic sampling, random sampling, stratified sampling Setting the Confidence Interval (CI) (Detailed discussion at end of Measure Stage of DMAIC model) Setting the Confidence Interval (CI) (Detailed discussion at end of Measure Stage of DMAIC model) Typical interval is set at 95% (this is Minitab default) Typical interval is set at 95% (this is Minitab default) Must know something about process to ballpark the sample size for a 95% CI Must know something about process to ballpark the sample size for a 95% CI Exercise: Manual Sample size calculation (pg ) Exercise: Manual Sample size calculation (pg )

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)7 Creating Data Collection Forms (A4.3) Avoiding pitfalls: Avoiding pitfalls: KISS KISS Good labeling Good labeling Space for identifying data: date, time, collector Space for identifying data: date, time, collector Have consistent structure Have consistent structure Include key STRATIFICATION FACTORS Include key STRATIFICATION FACTORS Types of collection forms: Types of collection forms: Check sheets Check sheets Data sheets Data sheets Travelers: Excellent method to “pair data” when stratification factor and Y-variable measurement don’t occur at same place and/or time Travelers: Excellent method to “pair data” when stratification factor and Y-variable measurement don’t occur at same place and/or time Avoiding pitfalls: Avoiding pitfalls: KISS KISS Good labeling Good labeling Space for identifying data: date, time, collector Space for identifying data: date, time, collector Have consistent structure Have consistent structure Include key STRATIFICATION FACTORS Include key STRATIFICATION FACTORS Types of collection forms: Types of collection forms: Check sheets Check sheets Data sheets Data sheets Travelers: Excellent method to “pair data” when stratification factor and Y-variable measurement don’t occur at same place and/or time Travelers: Excellent method to “pair data” when stratification factor and Y-variable measurement don’t occur at same place and/or time

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)8 Output of Data Collection and Sampling Plan (A4) A list of stratification factors A list of stratification factors Completed sampling plan Completed sampling plan Data collection forms Data collection forms A list of stratification factors A list of stratification factors Completed sampling plan Completed sampling plan Data collection forms Data collection forms

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)9 Output of Implement/Refine Measurement Process (A5) Review/finalize collection plan Review/finalize collection plan Perform Measurement System Analysis including Gage R&R, bias assessment, stability and linearity testing, and calibration Perform Measurement System Analysis including Gage R&R, bias assessment, stability and linearity testing, and calibration Prepare workplace: Let all know what’s going on Prepare workplace: Let all know what’s going on Tested collection procedures: Tested collection procedures: KISS and trial run KISS and trial run Validate collector training Validate collector training Collect data Collect data Monitor measurement accuracy and refine Monitor measurement accuracy and refine Exercise: Gage R&R Assessment (continuous and discrete) Exercise: Gage R&R Assessment (continuous and discrete) Review/finalize collection plan Review/finalize collection plan Perform Measurement System Analysis including Gage R&R, bias assessment, stability and linearity testing, and calibration Perform Measurement System Analysis including Gage R&R, bias assessment, stability and linearity testing, and calibration Prepare workplace: Let all know what’s going on Prepare workplace: Let all know what’s going on Tested collection procedures: Tested collection procedures: KISS and trial run KISS and trial run Validate collector training Validate collector training Collect data Collect data Monitor measurement accuracy and refine Monitor measurement accuracy and refine Exercise: Gage R&R Assessment (continuous and discrete) Exercise: Gage R&R Assessment (continuous and discrete)

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)10 Minitab Gage R&R Example

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)11 Minitab Gage R&R Session Window Gage R&R %Contribution Source VarComp (of VarComp) Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation Study Var %Study Var Source StdDev (SD) (6 * SD) (%SV) Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation Number of Distinct Categories = 1

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)12 Calculate Baseline Sigma Levels (B1) Key definitions Key definitions Unit: Item being processed (focus of the project) Unit: Item being processed (focus of the project) Defect: Failure to meet customer expectation Defect: Failure to meet customer expectation Defect Opportunity: Chance for product/service to be defective Defect Opportunity: Chance for product/service to be defective Guidelines for “defect opportunity” definition Guidelines for “defect opportunity” definition Focus on “defects that are important to the customer” Focus on “defects that are important to the customer” Should reflect “number of places in the process where it can go wrong, NOT all the ways it can go wrong” Should reflect “number of places in the process where it can go wrong, NOT all the ways it can go wrong” Focus on routine defects – i.e. don’t count the “rare event” Focus on routine defects – i.e. don’t count the “rare event” Group similar defects in a single “defect category” Group similar defects in a single “defect category” Be consistent (within defect and across company) Be consistent (within defect and across company) Don’t change operation definition without compelling reason Don’t change operation definition without compelling reason Simple 4-step process Simple 4-step process Exercise: Sigma Calculation Worksheet (pg ) Exercise: Sigma Calculation Worksheet (pg ) Key definitions Key definitions Unit: Item being processed (focus of the project) Unit: Item being processed (focus of the project) Defect: Failure to meet customer expectation Defect: Failure to meet customer expectation Defect Opportunity: Chance for product/service to be defective Defect Opportunity: Chance for product/service to be defective Guidelines for “defect opportunity” definition Guidelines for “defect opportunity” definition Focus on “defects that are important to the customer” Focus on “defects that are important to the customer” Should reflect “number of places in the process where it can go wrong, NOT all the ways it can go wrong” Should reflect “number of places in the process where it can go wrong, NOT all the ways it can go wrong” Focus on routine defects – i.e. don’t count the “rare event” Focus on routine defects – i.e. don’t count the “rare event” Group similar defects in a single “defect category” Group similar defects in a single “defect category” Be consistent (within defect and across company) Be consistent (within defect and across company) Don’t change operation definition without compelling reason Don’t change operation definition without compelling reason Simple 4-step process Simple 4-step process Exercise: Sigma Calculation Worksheet (pg ) Exercise: Sigma Calculation Worksheet (pg )

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)13 Calculate Final and First-Pass Yield (B2) Looks at the internal structure of the process Looks at the internal structure of the process Two different ways of looking at yield and process sigma: final yield and first-pass yield Two different ways of looking at yield and process sigma: final yield and first-pass yield Final yield: Final yield: How many defect-free items emerge at the end of the process including those that were successfully reworked How many defect-free items emerge at the end of the process including those that were successfully reworked Internal defects and their costs are hidden Internal defects and their costs are hidden First-pass yield: First-pass yield: Number of items that make it through entire process without any rework included Number of items that make it through entire process without any rework included Same as Rolled Throughput Yield (RTY) Same as Rolled Throughput Yield (RTY) Looks at the internal structure of the process Looks at the internal structure of the process Two different ways of looking at yield and process sigma: final yield and first-pass yield Two different ways of looking at yield and process sigma: final yield and first-pass yield Final yield: Final yield: How many defect-free items emerge at the end of the process including those that were successfully reworked How many defect-free items emerge at the end of the process including those that were successfully reworked Internal defects and their costs are hidden Internal defects and their costs are hidden First-pass yield: First-pass yield: Number of items that make it through entire process without any rework included Number of items that make it through entire process without any rework included Same as Rolled Throughput Yield (RTY) Same as Rolled Throughput Yield (RTY)

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)14 Measuring the Cost of Poor Quality (B3) Cost is connected to, but not the same as defect counts or sigma levels Cost is connected to, but not the same as defect counts or sigma levels Translate defect data into Cost of Poor Quality (COPQ) Translate defect data into Cost of Poor Quality (COPQ) Cost is connected to, but not the same as defect counts or sigma levels Cost is connected to, but not the same as defect counts or sigma levels Translate defect data into Cost of Poor Quality (COPQ) Translate defect data into Cost of Poor Quality (COPQ)

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)15 Output of Calculating the Performance Baseline Well defined units, defects and defect opportunity Well defined units, defects and defect opportunity Calculated baseline sigma level Calculated baseline sigma level Calculated final and/or first-pass yield for Y variable Calculated final and/or first-pass yield for Y variable Identified labor and material rework costs Identified labor and material rework costs Translated defects into dollars Translated defects into dollars Well defined units, defects and defect opportunity Well defined units, defects and defect opportunity Calculated baseline sigma level Calculated baseline sigma level Calculated final and/or first-pass yield for Y variable Calculated final and/or first-pass yield for Y variable Identified labor and material rework costs Identified labor and material rework costs Translated defects into dollars Translated defects into dollars

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)16 Long-Term vs. Short- Term Variation Short-term variation is less than long-term Short-term variation is less than long-term Process shift adjustment of 1.5 sigma Process shift adjustment of 1.5 sigma Short-term capability: The best possible if process is centered Short-term capability: The best possible if process is centered Long-term capability: Sustained reproducibility of the process Long-term capability: Sustained reproducibility of the process The Z calculation and the Z table The Z calculation and the Z table Short-term variation is less than long-term Short-term variation is less than long-term Process shift adjustment of 1.5 sigma Process shift adjustment of 1.5 sigma Short-term capability: The best possible if process is centered Short-term capability: The best possible if process is centered Long-term capability: Sustained reproducibility of the process Long-term capability: Sustained reproducibility of the process The Z calculation and the Z table The Z calculation and the Z table

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)17 Histogram and the Normal Distribution X-S-1.96S-3S+S+1.96S+3S (+2S) (-2S) Measurement Value Frequency of a Measurement Value

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)18 The “Z” Table Mean X -S-3S+S+3S Question 1: How many standard deviations are there between the mean and the reference measurement? Distance from red dashed line to the mean One Standard Deviation Z = Z = 1 std dev Z Distance from yellow solid line to the mean One Standard Deviation Z = Z = about 1.5 Z From Z Table, a value of 1.5 =.0668 or 6.68 percent of the area under the curve is to the right of the solid yellow line S (+2S) -1.96S (-2S)

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)19 “Z” Table Examples X -S-2S-3S+S+2S+3S Inches Distance from red dashed line to the mean One Standard Deviation Z = Z = Z = What percentage of measurements are to the left of the red dashed line? Z =.5 P = 30.85% P = 11.51% What percentage of measurements are to the right of the solid yellow line?

DIMAC 12/2/2003Six Sigma Green Belt (Ref: The Six Sigma Way Team Fieldbook)20 “Z” Table Exercise X -S-2S-3S+S+2S+3S Inches Z =.5208 What percentage of measurements are to the left of the red dashed line? Z = What percentage of measurements are between the dashed and solid lines?