1. Data Collection Six Sigma Foundations Continuous Improvement Training Six Sigma Foundations Continuous Improvement Training Six Sigma Simplicity

2. Key Learning Point  In God we trust, all others have to PROVIDE DATA

3. Data Defined  Raw facts – Qualitative or Quantitative – obtained by observing a population, product, process or service.

4. Road Map: 5-step Data collection process Clarify data collection goals Link customer requirements to output measures Develop Operational definitions & procedures Plan for data consistency and stability Begin data collection Continue to improve measurement consistency Operational definitions New data Types of data Collecting and recording data Sampling Validate measurement system Develop and implement data collection

5. Step 1: Clarify Data Collection Goals  Decide why you are collecting the data  Decide what data you need to collect  Decide how the data will help you  Decide what you will do with the data once you have it  Decide why you are collecting the data  Decide what data you need to collect  Decide how the data will help you  Decide what you will do with the data once you have it

6. Step 2: Develop Operational Definitions & Procedures  Decide what you are trying to evaluate  Decide how you will attach a value to what you are trying to measure  Decide if you need to collect new data  If so, decide how you will collect the data  Decide how you will record the data  Determine the period of time you will study  Estimate how many observations you will need  Learn whether your customer collects similar data And if so, how it compares  Decide what you are trying to evaluate  Decide how you will attach a value to what you are trying to measure  Decide if you need to collect new data  If so, decide how you will collect the data  Decide how you will record the data  Determine the period of time you will study  Estimate how many observations you will need  Learn whether your customer collects similar data And if so, how it compares

7. Defining the measure  An operational definition is a precise description that tells how to get a value for the characteristic you are trying to measure. It includes what something is and how to measure it  To remove ambiguity  Everyone has the same understanding  To provide a clear way to measure the characteristic  Identifies what to measure  Identifies how to measure it  Make sure that no matter who does the measuring, the results are essentially the same  An operational definition is a precise description that tells how to get a value for the characteristic you are trying to measure. It includes what something is and how to measure it  To remove ambiguity  Everyone has the same understanding  To provide a clear way to measure the characteristic  Identifies what to measure  Identifies how to measure it  Make sure that no matter who does the measuring, the results are essentially the same Definition Purpose

8. Operational Definition features  What: Must have specific and concrete criteria  How: Must have a method to measure criteria  Must be useful to both you and the customer  What: Must have specific and concrete criteria  How: Must have a method to measure criteria  Must be useful to both you and the customer

9. Do we need to collect new data?  Has the team established which category of data needs to be collected?  Cost  Time, Cycle time  Wait times  Defects/error rates  Six sigma calculations  Value-add and non value-add  Is there existing data to help with problem solving mission?  Is current data enough?  Does current data meet the needs of the process?  Is the team just using data that is available?  Has the team established which category of data needs to be collected?  Cost  Time, Cycle time  Wait times  Defects/error rates  Six sigma calculations  Value-add and non value-add  Is there existing data to help with problem solving mission?  Is current data enough?  Does current data meet the needs of the process?  Is the team just using data that is available?

10. Strategies: Measures and data sources 1 4 2 3 Measures NewExisting New Existing Sources Go to existing sources for existing measures first!

11. Types of data  Anything that results from being measured on a continuum or scale. Example:  Time to process  Anything that can be categorized or designated as either/or. Examples:  Male/Female  Off/On  Defect/No defect  Days of week (M/T/W/Th/F)  Accept/Reject  Yes/No  Democrat/Republican  Anything that results from being measured on a continuum or scale. Example:  Time to process  Anything that can be categorized or designated as either/or. Examples:  Male/Female  Off/On  Defect/No defect  Days of week (M/T/W/Th/F)  Accept/Reject  Yes/No  Democrat/Republican Variable Or Continuous Attribute Or Discrete

12. Why is type of data important?  Choice of data display and analysis tools  Sample size : variable data requires smaller sample size than attribute data  More information: variable data tells you whether or not there has been a change and by how much (more sensitive to changes in the process)  Choice of data display and analysis tools  Sample size : variable data requires smaller sample size than attribute data  More information: variable data tells you whether or not there has been a change and by how much (more sensitive to changes in the process)

13. Data collection forms  Creating a standard form and defining data collection procedures: why?  You can be sure different people will collect the data the same way  It makes it easier to keep track of data  Creating a standard form and defining data collection procedures: why?  You can be sure different people will collect the data the same way  It makes it easier to keep track of data  Examples:  Check-sheets  Concentration diagrams  Frequency plot check-sheet  Any table or template the team agrees to use

14. SamplingSampling  Sampling is the process of  Collecting just a portion of the data that is available or could be available, and  Using the data in the sample to draw conclusions (statistical inference)  Why sample?  It is often impractical or too costly to collect all the data  Sometimes data collection is a destructive process (e.g.: taste testing, chemical experiments)  Sound conclusions can often be made from a small amount of data  Sampling is the process of  Collecting just a portion of the data that is available or could be available, and  Using the data in the sample to draw conclusions (statistical inference)  Why sample?  It is often impractical or too costly to collect all the data  Sometimes data collection is a destructive process (e.g.: taste testing, chemical experiments)  Sound conclusions can often be made from a small amount of data

15. Two important uses of sampling  We have a process that we want to measure, analyze or control  Establishing baseline performance of the process  Conducting a special study to improve the process  Ongoing monitoring to control the process  We have a large population that we want to describe  Income level of a certain customer segment  % Customer who would purchase a new product or service  Reasons for late payments  We have a process that we want to measure, analyze or control  Establishing baseline performance of the process  Conducting a special study to improve the process  Ongoing monitoring to control the process  We have a large population that we want to describe  Income level of a certain customer segment  % Customer who would purchase a new product or service  Reasons for late payments Sampling provides a snapshot of the process or population at given point of time

16. Step 3: Plan for data consistency and stability  Determine factors that could cause the measurement of an item to vary  Find ways to reduce the impact of those factors  Test your data collection forms  Determine factors that could cause the measurement of an item to vary  Find ways to reduce the impact of those factors  Test your data collection forms

17. Validating measurement systems  Data is only as good as the process that measures it  Develop a methodology to test the measurement system to ensure consistency and stability  Data is only as good as the process that measures it  Develop a methodology to test the measurement system to ensure consistency and stability Measurement systems must be validated to ensure data is consistently free from errors

18. Potential sources of inconsistency and instability  Knowledge  Skill  Technique  Capability  Ease of use  Error-proofed  Calibration  Appropriate for use  Documented  Implemented  Unclear Operational definitions  Different Suppliers  Knowledge  Skill  Technique  Capability  Ease of use  Error-proofed  Calibration  Appropriate for use  Documented  Implemented  Unclear Operational definitions  Different Suppliers Human Methods/Procedures Automated/Manual Material Equipment

19. Data collection  The benefit of getting the data must outweigh the cost of collecting it!

20. Step 4: Begin data collection  Communicate the what and why to the data collectors and process participants  Train everyone who will be collecting data  Make data collection procedures error- proof  Be there in the beginning to oversee data collection  Confirm understanding of operational definitions  Communicate the what and why to the data collectors and process participants  Train everyone who will be collecting data  Make data collection procedures error- proof  Be there in the beginning to oversee data collection  Confirm understanding of operational definitions

21. Step 5: Continue improving measurement consistency and stability  Check to make sure data measurements are stable  Check to make sure data measurement procedures remain consistent  Check to see if the data looks reasonable  Check to make sure data measurements are stable  Check to make sure data measurement procedures remain consistent  Check to see if the data looks reasonable

22. Data Collection Six Sigma Foundations Continuous Improvement Training Six Sigma Foundations Continuous Improvement Training