IndyASQ Workshop September 12, Measure for Six Sigma and Beyond

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1 IndyASQ Workshop September 12, 2007 Measure for Six Sigma and Beyond 1

2 Introductions Tom Pearson MS OR Old #7 Golf Guy Innovator Dog Lover Tomorrowist Entrepreneur Author / Speaker Systems Scientist Measurement Scientist Six Sigma Master Black Belt Dilbert / Pointy-Haired Boss / Dogbert ASQ Fellow, Past Chair MQD and Section 903 Tom Pearson Consulting 200 Slide #2

3 Measure for Six Sigma and Beyond Measure is more than just a step in Six Sigma's DMAIC method. Good measurements are basic building blocks of good information. Good measurements are critical for good decision making. This workshop will investigate the key elements of good measurement systems. Tom Pearson Consulting 200 Slide #3

4 Measurement and Statistics Measurements have a long and rich relationship with statistical sciences. Metrology (measurement science) is the study of measurement error (uncertainty). It is essentially a statistical pursuit. Were there no uncertainty, there would be very little science in measurement science. Philip Stein, Statistical Issues in Measurement, ASQ Statistics Division Special Publication (July 2002) Tom Pearson Consulting 200 Slide #4

5 What to Measure All Possible Input Xs Process Mapping Fishbone Diagrams X-Y Matrices FMEA Data Mining Hypothesis Testing Design of Experiments Critical Few Xs Tom Pearson Consulting 200 Slide #5

6 Types of Data Variable or Continuous Tonight s Focus Quantitative a scale that can take an infinite number of values along it s length, with or without end points (e.g., temperature, pressure), or with an absolute zero point (e.g., height, weight). Attribute or Discrete Qualitative Count or percentage Binomial Nominal Ordinal Tom Pearson Consulting 200 Slide #

7 Class Attribute Data Ordinal Categorical variables that have three or more possible levels with natural ordering. Distance between the levels is unknown I.e., poor, fair, excellent, or Olympic scoring. Can be attribute or discrete variable data. Nominal Categorical variables that have two or more possible mutually exclusive levels with no natural ordering (e.g., sex, race). Typically attribute data. Tom Pearson Consulting 200 Slide #7 Joe Swartz 2005

8 Critical Six Sigma questions What is the Voice of the Customer? What to Measure: Cost, Quality, Features, Availability What is the Voice of the Process? Center, Spread, Shape, Stability (Control) Does our process meet customer needs? Compliance, Process Capability, Opportunities? Can we make it better? Continuous Improvement, Breakthrough, Innovation. Tom Pearson Consulting 200 Slide #8

9 Example: VOP Voice Of the Process VOC Voice Of the Customer I-MR Chart of Defects Lab Test Requested IMR Chart CTQ < > Observation Specifications Note the Process Center Shape Spread Stability Tom Pearson Consulting 200 Slide #9

10 Consider the Shape, Center, and Spread Tom Pearson Consulting 200 Slide #10

11 What about Stability? p= p=(.02485) 2 =.0002 p=0.8 8% P=(0.5) 8 = Tom Pearson Consulting 200 Slide #11

12 Hearing the VOP Lab Test Requested IMR Chart 12 8% Tom Pearson Consulting 200 Slide #12

13 Does it meet customer needs (VOC)? Process Capability of Defects Lab Test Process Capability Process Data LSL Target USL Sample Mean Sample N 52 S tdev (Within) StDev (O v erall) LSL Target USL Within Overall Potential (Within) C apability Cp 0.71 CPL 0.59 CPU 0.83 C pk 0.59 CCpk 0.71 O verall C apability Pp 0.57 PPL 0.48 PPU 0.7 Ppk 0.48 C pm O bserv ed P erformance PPM < LSL PPM > USL PPM Total E xp. Within P erformance PPM < LSL PPM > USL PPM Total Exp. Overall Performance PPM < LSL PPM > USL PPM Total Tom Pearson Consulting 200 Slide #13

14 VOP helps us plan future operations Lab Probability Test Probability Plot of Defects Plot Normal Mean StDev 1.74 N 52 AD 1.35 P-Value < Percent Defects Tom Pearson Consulting 200 Slide #14

15 VOP helps us find improvement opportunities Boxplot of Defects vs Team Boxplot of Defects vs Method Defects 3 Defects Team Method 3 4 Boxplot of Defects vs Trial 5 4 Defects Trial Tom Pearson Consulting 200 Slide #15

16 Measurement Systems Analysis MSA insures: Good correlation Adequate discrimination In statistical control Measurement uncertainty small: Compared to process variation Compared to specification limits Tom Pearson Consulting 200 Slide #1

17 Measurment Variation σ 2 total = σ2 process + σ 2 measurement system σ 2 measuring system = σ 2 operator + σ 2 measurement device + σ 2 environment Target Spec Tom Pearson Consulting 200 Slide #17

18 Measurment Variation with Bias σ 2 total = σ2 process + σ 2 measurement system Note: The observed mean is the average of the process mean and the measurement system mean. Target Spec Tom Pearson Consulting 200 Slide #18

19 Sources of Measurement Uncertainty Measurement Accuracy How closely the average measured value agrees with the true value. Average Measurement True Value = Bias More Accurate Less Accurate True Value True Value Tom Pearson Consulting 200 Slide #19

20 Sources of Measurement Uncertainty Measurement Precision How closely repeated measurements agree with each other. Compensate for Poor Precision by Better Measuring Device Better Measurement Method Averaging repeat measurements More Precise Less Precise Tom Pearson Consulting 200 Slide #20

21 Sources of Measurement Uncertainty Measurement Resolution Minimum of 10 increments within the specification. At least 5 increments within the SPC Range Chart. Increase Resolution, Normality, (and Cost) by averaging repeated measurements. Less Precise Averages of 4 Readings have ½ the variation of individuals. Remember: 2 xbar = 2 x Tom Pearson Consulting 200 Slide #21

22 How Good is Good Enough? If 2 measure / 2 observed Good Measurement System is less than or equal to 0.1: Use as is, look for ways to simplify or reduce expense If 2 measure / 2 observed is between 0.1 and 0.3: Marginal Measurement System use with caution. Improve the measurement system by training operators, standardizing procedures, using statistics, investigating new methods and equipment. If 2 measure / 2 observed is 0.3 or greater: Unacceptable Measurement System Do not use for critical decisions Correct ASAP. Tom Pearson Consulting 200 Slide #22

23 Example: Needs improved or replaced. σ 2 measure = 9 σ 2 observed = 25 σ 2 meas /σ2 obs =.3 >.3 Target Tom Pearson Consulting 200 Slide #23

24 Measurement System Errors Precision (Gage R&R) Repeatability The variation between successive measurements of same product or service, same characteristic, by same person, using same measurement device Reproducibility Variation in appraisers Additional Factors? Environment Equipment Other Determine via Designed Experiments Tom Pearson Consulting 200 Slide #24

25 Questions? Tom Pearson Consulting 200 Slide #25