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Engineers: Apply Automation to Increase Quality, Speed to Market

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We live in the age of machine learning, artificial intelligence and other automated systems. Why, then, are we performing tedious tasks that we can streamline during the product development phase? First, there is Design Verification testing. Second, there is Design Validation testing. Some of these tests use simple pass/fail attribute data, while others use continuous data. We will focus on ways to automate the analysis of that continuous data, which can ensure more accurate and timely results.

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Engineers: Apply Automation to Increase Quality, Speed to Market

  1. 1. We manufacture quality into everything we do
  2. 2. Apply Automation to Decrease Time to Market and Increase Quality Edward Jaeck VP of Strategic Growth and Business Development Lowell Inc. June 13th, 2019
  3. 3. 3 #1 Define success clearly #2 Create visual assessments and summaries #3 Execute detailed data analysis There are three key aspects of applying automation to design validations and process validations ALL TESTS PASSED
  4. 4. 4 #1 Define success clearlyALL TESTS PASSED
  5. 5. 5 Successful design engineers seek to understand user needs, cost and validation considerations prior to designing Simple More Complex
  6. 6. 6 Risk management starts by simplifying the design to minimize the number of critical specifications
  7. 7. 7 Risk management starts by simplifying the design to minimize the number of critical specifications
  8. 8. 8 Risk management starts by simplifying the design to minimize the number of critical specifications Minimize the A/B ratio where: A = # of critical features B = # of negligible features
  9. 9. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 9 An example to clarify this crucial design KPI (Key Performance Indicator)
  10. 10. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 10 This can be done like a stepwise regression analysis • Forwards regression analogy: Which of the ten features would we make critical if we could only have one critical feature?
  11. 11. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 11 This can be done like a stepwise regression analysis • Backwards regression analogy: Which of the ten features would we remove last from the all critical list?
  12. 12. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 12 This can be done like a stepwise regression analysis • This can and should be done by two separate teams prior to final design review to check for synergy and rationale
  13. 13. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 13 This can be done like a stepwise regression analysis • Forwards regression analogy: Which of the ten features would we make critical if we could only have one critical feature? • Backwards regression analogy: Which of the ten features would we remove last from the all critical list? • This can and should be done by two separate teams prior to final design review to check for synergy and rationale Same critical feature/specification
  14. 14. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 14 Success depends on limiting the number of major and minor features
  15. 15. A B Critical/Negligible Ratio (Major+Minor)/ Total Ratio Critical Major Minor Negligible Total Number of Specifications A/B Ratio Residual Ratio Design 1 1 2 3 4 10 Design 1 0.250 0.5 Design 2 1 2 0 7 10 Design 2 0.143 0.2 Design 3 1 1 0 8 10 Design 3 0.125 0.1 Design 4 1 0 0 9 10 Design 4 0.111 0 15 Success depends on limiting the number of major and minor features Design 1 Worst Design 4 Best
  16. 16. 16 On first glance, this drawing may seem busy
  17. 17. 17 On second glance, this drawing gets simpler
  18. 18. 18 Drawings get simpler when we focus on just the critical features
  19. 19. 19 Validation requirements for the dimensional specifications are straightforward Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Gauge R&R P/T < 25% Demonstrate 95% Confidence and 90% Coverage.
  20. 20. 20 Validation requirements for the dimensional specifications are straightforward Success = Each dimensional specification must pass a basic GR&R Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Gauge R&R P/T < 25% Demonstrate 95% Confidence and 90% Coverage.
  21. 21. 21 Validation requirements for the dimensional specifications are straightforward Success = Each dimensional specification must pass the 95/90 validation criteria Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Gauge R&R P/T < 25% Demonstrate 95% Confidence and 90% Coverage.
  22. 22. 22 Validation requirements for the dimensional specifications are straightforward Success = Each dimensional specification must pass a basic GR&R AND each must pass the 95/90 validation criteria Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Gauge R&R P/T < 25% Demonstrate 95% Confidence and 90% Coverage.
  23. 23. 23 Validation requirements for the functional specifications are more complex however Success = Each functional specification must pass an attribute agreement study Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Functional Washer assembled correctly PASS PASS Functional Washer fit snugly in mating feature PASS PASS Functional Washer withstood 20 lbf in compression PASS PASS Attribute Agreement Study Kappa >75% Demonstrate 95% Confidence and 90% Coverage.
  24. 24. 24 Validation requirements for the functional specifications are more complex however Success = Each functional specification must pass the 95/90 validation criteria with attribute data Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Functional Washer assembled correctly PASS PASS Functional Washer fit snugly in mating feature PASS PASS Functional Washer withstood 20 lbf in compression PASS PASS Attribute Agreement Study Kappa >75% Demonstrate 95% Confidence and 90% Coverage.
  25. 25. 25 Validation requirements for the functional specifications are more complex however Success = Each functional specification must pass an attribute agreement study AND each must pass the 95/90 validation criteria with attribute data Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Functional Washer assembled correctly PASS PASS Functional Washer fit snugly in mating feature PASS PASS Functional Washer withstood 20 lbf in compression PASS PASS Attribute Agreement Study Kappa >75% Demonstrate 95% Confidence and 90% Coverage.
  26. 26. 26 Putting all validation requirements together shows the overall complexity of the design Success = Each of the seven specifications must pass their respective test method validation criteria and validation criteria Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Functional Washer assembled correctly PASS PASS Functional Washer fit snugly in mating feature PASS PASS Functional Washer withstood 20 lbf in compression PASS PASS Gauge R&R P/T < 25% Attribute Agreement Study Kappa >75% Demonstrate 95% Confidence and 90% Coverage.
  27. 27. 27 In most project schedules, it is assumed that all test methods and validations pass on first attempt +7 7 Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Functional Washer assembled correctly PASS PASS Functional Washer fit snugly in mating feature PASS PASS Functional Washer withstood 20 lbf in compression PASS PASS Gauge R&R P/T < 25% Attribute Agreement Study Kappa >75% Demonstrate 95% Confidence and 90% Coverage.
  28. 28. 28 In most project schedules, it is assumed that all test methods and validations pass on first attempt +7 7In sporting terms, that’s a perfect 14-0 record Product Specification Type Dimensional Feature Measured or Functional Test Run Test Method Validation Criteria Test Method Validation Results Validation Success Criteria Validation Results Dimensional Outer Diameter PASS PASS Dimensional Inner Diameter PASS PASS Dimensional True Position of Inner Diameter PASS PASS Dimensional Thickness PASS PASS Functional Washer assembled correctly PASS PASS Functional Washer fit snugly in mating feature PASS PASS Functional Washer withstood 20 lbf in compression PASS PASS Gauge R&R P/T < 25% Attribute Agreement Study Kappa >75% Demonstrate 95% Confidence and 90% Coverage.
  29. 29. 29 #2 Create visual assessments and summaries
  30. 30. 30 Route 66 is a popular scenic bypass in northern Arizona
  31. 31. 31 Well-designed validation strategies also have bypasses
  32. 32. 32 Validation bypasses can provide options with complex data Attribute Bypass V&V Plan Start Pick 1. Confidence Level (CL) Ex. 95% Confidence 2. Population Coverage Ex. 90% Coverage Pick Sampling Path based on Data type or logic Define Attribute Sampling Plan to meet Confidence/Coverage requirements. A common one in Medical Device for 95/ 90 is N=29, C=0 Choose Appropriate Sampling Plan based on Confidence/Coverage Execute the V&V Testing Does the data pass an appropriate Normality Test? Assuming N=29, C=0, does all data pass or is all continuous data in spec? Is the entire tolerance interval inside spec limits Test Fails, Fix Issue with Design Test Passes Variable Attribute Variable Attribute If powered at attribute sample size, treat as attribute. Else, add samples and test and re-evaluate No Yes No No Yes Yes A B C D E F
  33. 33. 33 Here is what really happens with variable sample-size discussions OD ID ID TP Thickness mean 0.7505 0.2491 0.0041 0.2538 stdev 0.0010 0.0015 0.0005 0.0005 sample size needed 5 12 28 18
  34. 34. 34 Here is what really happens with variable sample-size discussions OD ID ID TP Thickness mean 0.7505 0.2491 0.0041 0.2538 stdev 0.0010 0.0015 0.0005 0.0005 sample size needed 5 12 28 18 • Can’t check normality on OD N=5, so round up to N=9 minimum
  35. 35. 35 Here is what really happens with variable sample-size discussions OD ID ID TP Thickness mean 0.7505 0.2491 0.0041 0.2538 stdev 0.0010 0.0015 0.0005 0.0005 sample size needed 5 12 28 18 • Can’t check normality on OD N=5, so round up to N=9 minimum • Both N=9 and ID N=12 are less than N=15, which is the minimum to run Skewness-Kurtosis check for normality; round both up to N=15
  36. 36. 36 Here is what really happens with variable sample-size discussions OD ID ID TP Thickness mean 0.7505 0.2491 0.0041 0.2538 stdev 0.0010 0.0015 0.0005 0.0005 sample size needed 5 12 28 18 • Can’t check normality on OD N=5, so round up to N=9 minimum • Both N=9 and ID N=12 are less than N=15, which is the minimum to run Skewness-Kurtosis check for normality; round both up to N=15 • N=15 and N=18 are less than N=20, which is the recommended minimum for some ad-hoc tests, so round both up to N=20
  37. 37. 37 Here is what really happens with variable sample-size discussions OD ID ID TP Thickness mean 0.7505 0.2491 0.0041 0.2538 stdev 0.0010 0.0015 0.0005 0.0005 sample size needed 5 12 28 18 • Can’t check normality on OD N=5, so round up to N=9 minimum • Both N=9 and ID N=12 are less than N=15, which is the minimum to run Skewness-Kurtosis check for normality; round both up to N=15 • N=15 and N=18 are less than N=20, which is the recommended minimum for some ad-hoc tests, so round both up to N=20 • OD, ID and Thickness are all up to N=20; round all three up to N=28 to match ID TP
  38. 38. 38 Here is what really happens with variable sample-size discussions OD ID ID TP Thickness mean 0.7505 0.2491 0.0041 0.2538 stdev 0.0010 0.0015 0.0005 0.0005 sample size needed 5 12 28 18 • Can’t check normality on OD N=5, so round up to N=9 minimum • Both N=9 and ID N=12 are less than N=15, which is the minimum to run Skewness-Kurtosis check for normality; round both up to N=15 • N=15 and N=18 are less than N=20, which is the recommended minimum for some ad-hoc tests, so round both up to N=20 • OD, ID and Thickness are all up to N=20; round all three up to N=28 to match ID TP • For a proper capability analysis we need N=100 so 3 lot of N=34
  39. 39. 39 Data formatting is key to streamlining any data analysis Most large statistical programs are column based
  40. 40. 40 Step 1: Create boxplot with spec limits as reference lines 0.0010 0.0008 0.0006 0.0004 0.0002 0.0000 B1Lot1 0.001 Boxplot of B1 Lot 1
  41. 41. 41 Step 2: Run Grubbs Outlier Test 0.00100.00080.00060.00040.00020.0000 0.00 0.00 2.35 0.503 Min Max G P Grubbs' Test B1 Lot 1 0.001 Outlier Plot of B1 Lot 1
  42. 42. 42 Step 3: Check normality with AD test 0.00080.00070.00060.00050.00040.00030.0002 99 95 90 80 70 60 50 40 30 20 10 5 1 Mean 0.0005059 StDev 0.0001254 N 34 AD 1.572 P-Value <0.005 B1 Lot 1 Percent Probability Plot of B1 Lot 1 Normal
  43. 43. 43 Step 4: Check normality with RJ test (for binned data) 0.00080.00070.00060.00050.00040.00030.0002 99 95 90 80 70 60 50 40 30 20 10 5 1 Mean 0.0005059 StDev 0.0001254 N 34 RJ 0.995 P-Value >0.100 B1 Lot 1 Percent Probability Plot of B1 Lot 1 Normal
  44. 44. 44 Step 5: Calculate 95/90 Tolerance Interval N 34 Mean 0.001 StDev 0.000 Upper 0.001 Upper 0.001 97.2% AD 1.572 P-Value < 0.005 Statistics Normal Nonparametric Achieved Confidence Normality Test 0.00100.00080.00060.00040.0002 Nonparametric Normal 0.00100.00080.00060.00040.0002 0.00080.00070.00060.00050.00040.00030.0002 99 90 50 10 1 Percent Normal Probability Plot Tolerance Interval Plot for B1 Lot 1 95% Upper Bound At Least 90% of Population Covered
  45. 45. 45 Step 6: Run Capability Analysis 0.00100.00080.00060.00040.0002 LSL * Target * USL 0.001 Sample Mean 0.000505882 Sample N 34 StDev(Overall) 0.000125387 StDev(Within) 0.00012634 Process Data Pp * PPL * PPU 1.31 Ppk 1.31 Cpm * Cp * CPL * CPU 1.30 Cpk 1.30 Potential (Within) Capability Overall Capability PPM < LSL * * * PPM > USL 0.00 40.62 45.96 PPM Total 0.00 40.62 45.96 Observed Expected Overall Expected Within Performance USL Overall Within Process Capability Report for B1 Lot 1
  46. 46. 46 One down, seventeen to go!
  47. 47. 47 Repeating that process 17 more times is mundane and it would take several hours to summarize the data efficiently Now we know why we were asked to push mow the whole yard…
  48. 48. 48 Great visual validation summaries focus on key concerns Green is good
  49. 49. 49 #3 Execute detailed data analysis
  50. 50. 50 Lowell’s PQ-it™ automates most of the work The four plot shows a boxplot, outlier test and AD & RJ normality tests
  51. 51. 51 Lowell’s PQ-it™ automates most of the work The capability analysis function gives the appropriate Ppk index
  52. 52. 52 Lowell’s PQ-it™ automates most of the work This tables focus is # out of outliers, # out of spec, & normality tests
  53. 53. 53 Lowell’s PQ-it™ automates most of the work This tables focus is N, and Pp and Ppk Variable Lower Spec Upper Spec Standard 95%-90% 95%-90% Process Capability Name Limit Limit Deviation Lower Upper Pp Ppk Tolerance Tolerance Int Int 1 B1 Lot 1 34 * * 0.001 0.00051 0.00013 * 0.00072 1.31 1.31 2 B1 Lot 2 34 * * 0.001 0.00060 0.00008 * 0.00074 1.75 1.75 3 B1 Lot 3 34 * * 0.001 0.00055 0.00010 * 0.00072 1.55 1.55 4 B2 Lot 1 34 0.621 0.631 0.641 0.63154 0.00057 0.63034 0.63274 5.84 5.53 5 B2 Lot 2 34 0.621 0.631 0.641 0.62997 0.00036 0.62921 0.63072 9.30 8.33 6 B2 Lot 3 34 0.621 0.631 0.641 0.63001 0.00049 0.62898 0.63104 6.82 6.14 7 B3 Lot 1 34 0.1805 0.1810 0.1815 0.18087 0.00008 0.18071 0.18103 2.20 1.62 8 B3 Lot 2 34 0.1805 0.1810 0.1815 0.18096 0.00006 0.18083 0.18108 2.79 2.56 9 B3 Lot 3 34 0.1805 0.1810 0.1815 0.18088 0.00007 0.18074 0.18103 2.43 1.85 10 B4 Lot 1 34 0.090 0.092 0.094 0.09226 0.00020 0.09185 0.09267 3.40 2.96 11 B4 Lot 2 34 0.090 0.092 0.094 0.09194 0.00017 0.09160 0.09229 4.04 3.93 12 B4 Lot 3 34 0.090 0.092 0.094 0.09267 0.00043 0.09176 0.09357 1.55 1.03 13 B5 Lot 1 34 0.151 0.155 0.155 0.15269 0.00018 0.15232 0.15306 3.80 3.20 14 B5 Lot 2 34 0.151 0.155 0.155 0.15319 0.00023 0.15271 0.15367 2.91 2.63 15 B5 Lot 3 34 0.151 0.155 0.155 0.15266 0.00013 0.15239 0.15293 5.22 4.33 16 B6 Lot 1 34 0.139 0.140 0.141 0.14014 0.00008 0.13997 0.14032 4.00 3.44 17 B6 Lot 2 34 0.139 0.140 0.141 0.14000 0.00014 0.13970 0.14030 2.34 2.33 18 B6 Lot 3 34 0.139 0.140 0.141 0.14010 0.00016 0.13977 0.14043 2.13 1.91 Row N Nom Mean
  54. 54. 54 Lowell’s PQ-it™ automates most of the work This tables focuses on the results of the variable 95/90 TI analysis Tolerance Intervals Confidence level                           95%                                  Percent of population in interval  90%                                          Data Normal Normal Normal Normal Tolerance Tolerance Tolerance Tol Int Tol Int Interval Int Lower Int Upper lo>=LSL? up<=USL? within Spec? 1 B1 Lot 1 * 0.001 * 0.00072   Yes Yes 2 B1 Lot 2 * 0.001 * 0.00074   Yes Yes 3 B1 Lot 3 * 0.001 * 0.00072   Yes Yes 4 B2 Lot 1 0.621 0.641 0.63034 0.63274 Yes Yes Yes 5 B2 Lot 2 0.621 0.641 0.62921 0.63072 Yes Yes Yes 6 B2 Lot 3 0.621 0.641 0.62898 0.63104 Yes Yes Yes 7 B3 Lot 1 0.1805 0.1815 0.18071 0.18103 Yes Yes Yes 8 B3 Lot 2 0.1805 0.1815 0.18083 0.18108 Yes Yes Yes 9 B3 Lot 3 0.1805 0.1815 0.18074 0.18103 Yes Yes Yes 10 B4 Lot 1 0.09 0.094 0.09185 0.09267 Yes Yes Yes 11 B4 Lot 2 0.09 0.094 0.09160 0.09229 Yes Yes Yes 12 B4 Lot 3 0.09 0.094 0.09176 0.09357 Yes Yes Yes 13 B5 Lot 1 0.151 0.155 0.15232 0.15306 Yes Yes Yes 14 B5 Lot 2 0.151 0.155 0.15271 0.15367 Yes Yes Yes 15 B5 Lot 3 0.151 0.155 0.15239 0.15293 Yes Yes Yes 16 B6 Lot 1 0.139 0.141 0.13997 0.14032 Yes Yes Yes 17 B6 Lot 2 0.139 0.141 0.13970 0.14030 Yes Yes Yes 18 B6 Lot 3 0.139 0.141 0.13977 0.14043 Yes Yes Yes Row Variable LSL USL
  55. 55. 55 We should have a new appreciation for this summary table Each cell would need to be hand cut and pasted Data Row Variable Name Number of boxplot outliers N Number of data points outside specification limits Is the entire 95/90 tolerance interval within spec limits? Capability- Ppk 1 B1 Lot 1 0 34 0 Yes 1.08 2 B1 Lot 2 1 34 0 Yes 1.50 3 B1 Lot 3 2 34 0 Yes 1.25 4 B2 Lot 1 3 34 0 Yes 5.53 5 B2 Lot 2 0 34 0 Yes 8.33 6 B2 Lot 3 0 34 0 Yes 6.14 7 B3 Lot 1 3 34 0 Yes 1.62 8 B3 Lot 2 0 34 0 Yes 2.56 9 B3 Lot 3 0 34 0 Yes 1.85 10 B4 Lot 1 0 34 0 Yes 2.96 11 B4 Lot 2 0 34 0 Yes 3.93 12 B4 Lot 3 0 34 0 Yes 1.03 13 B5 Lot 1 0 34 0 Yes 3.20 14 B5 Lot 2 0 34 0 Yes 2.63 15 B5 Lot 3 0 34 0 Yes 4.33 16 B6 Lot 1 0 34 0 Yes 3.44 17 B6 Lot 2 2 34 0 Yes 2.33 18 B6 Lot 3 5 34 0 Yes 1.91
  56. 56. 56 #1 Define success clearly • Simplify the design • Create validations plans with bypasses #2 Create visual assessments and summaries • Make sure the summaries are focused #3 Execute detailed data analysis • Define the validation plan then automate it In summary, data analysis macros shorten analysis time, improve data integrity and increase quality ALL TESTS PASSED
  57. 57. 57 Questions?
  58. 58. 58 Acknowledgements Assertion-Evidence Template: • https://www.assertion-evidence.com Automated OQ/PQ Minitab Macro Creation: • Mercer Quality Consulting, LLC • http://MercerQualityConsulting.com Graphics Support: • Edward Britz • www.edwardbritz.com Lowell Inc Website • www.lowellinc.com
  59. 59. 59 Appendix

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