Rutgers Green Belt

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Summary of Spring Six Sigma Greenbelt Course at Rutgers

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Rutgers Green Belt

  1. 1. Certified Six Sigma Green Belt Course Brandon Theiss Brandon.Theiss@gmail.com
  2. 2. Motivation• Teaching the tools, techniques and Methods of Lean Six Sigma is inherently difficult in academic setting.• When taught in a industrial setting students have a common motivation (the improved welfare of the company), similar levels of education and knowledge of domain specific information. Student are encouraged to learn by applying the material to their daily activities.• This is not possible in an academic setting particularly in a mixed environment that includes everything from undergraduate juniors through senior PhD researchers.• In addition undergraduate students tend either lack professional or have experience in Fields that are not traditionally thought of as benefiting or implementing Six Sigma (waitressing, check out clerk etc.)
  3. 3. Solution• The beauty of the Six Sigma Methodology is that it can be applied to any process.• The definition of a process is quite broad and can be reduced to any verb- noun combination.• Therefore the collective process which the class studied and improved was to Pass [the] ASQ Certified Six Sigma Green Belt Exam• Therefore the foundational Six Sigma Concept of DMAIC (Define Measure Analyze Improve Control) represents both the material covered in the course as well as the pedagogical method used for instruction
  4. 4. About the Course & Partnership• Offered as a Non-Credit extracurricular course at Rutgers University in Piscataway NJ• Co-Sponsored by the Rutgers Student Chapter of the Institute for Industrial Engineers (IIE) and the Princeton NJ section of American Society for Quality (ASQ)• Open and advertised to all members of the Rutgers Community (students, staff and faculty) as well as the surrounding public• Objective of the course was to train students to pass the June 2nd 2012 administration of the ASQ Certified Six Sigma Green Belt Exam
  5. 5. Class Demographics • 71 Students Registered – 57 At Student Tuition Rate ($296) – 14 At Professional Tuition Rate ($495) Histogram of Years Of Work Exprience Highest Accademic Grade 3 Completed 2040.0%35.0% 1530.0% Frequency25.0% 1020.0%15.0%10.0% 5 5.0% 0.0% 0 Junior Year Senior BA/BS Some MA/MS/JD PhD/PE 2 4 6 8 10 12 14 16 18 20 22 24 Year Grdudate Years Of Work Exprience
  6. 6. Course Syllabus 1. Introduction, Sample Exam 7. Analyze 2, Analyze 3 2. Review Exam, Define 1 8. Improve 1, Sample 50 Question Exam 3. Define 2, Measure 1 9. Review Exam, Control 1 4. Measure 2, Measure 3 10. Sample 100 Question Exam 5. Measure 4, Sample 50 Question 11. Review Exam, Additional Questions Exam 6. Review Exam, Analyze 1 Define Measure Analyze Improve Control• Project Definition • Measurement • Inferential • Pareto Charts• Team Dynamics Systems Statistics • Process• Brainstorming • Histograms • Confidence Capability• Process Mapping • Box Plots Intervals • Lean • Dot Plots • Hypothesis • Probability Tests Plots • Regression • Control Charts Analysis
  7. 7. Pre Test• On the first night of classes students were given an introductory survey of Six Sigma by means of a worked example applying DMAIC to the Starbucks Experience from a Customers Prospective.• Students were then given a copy of the Certified Six Sigma Green Belt Handbook by Roderick A. Munro• Then given a 50 Question Multiple Choice Test representative of the ASQ CSSGB Exam• The Test was administered on two successive nights (Monday and Tuesday)
  8. 8. Measurement System• An Apperson GradeMaster™ 600 Test Scanner was utilized which enabled test to be scored and returned immediately upon student submission at the exam site.• In addition all of each answer to every question was downloaded to connected computer enabling further detailed analysis
  9. 9. MONDAY RESULTS
  10. 10. Test Scores Histogram of Test Scores Normal Mean 0.5589 9 StDev 0.1177 N 35 8 7 6Frequency 5 4 3 2 1 0 36.00% 48.00% 60.00% 72.00% 84.00% Test Scores
  11. 11. Test for Normality Probability Plot of Test Score Normal - 95% CI 99 Mean 0.5589 StDev 0.1177 95 N 35 AD 0.396 90 P-Value 0.352 80 70Percent 60 50 40 30 20 10 5 1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Test Score
  12. 12. Is process in Control? I Chart of Test Score 1.0 UCL=0.9468 0.9 0.8 0.7Individual Value 0.6 _ X=0.5589 0.5 0.4 0.3 0.2 LCL=0.1709 0.1 1 4 7 10 13 16 19 22 25 28 31 34 Observation
  13. 13. Is the Process Capable? Process Capability of Test Scores LSL P rocess Data WithinLS L 0.78 OverallTarget *USL * P otential (Within) C apabilityS ample M ean 0.558857 Cp *S ample N 35 C PL -0.61S tDev (Within) 0.120985 C PU *S tDev (O v erall) 0.117718 C pk -0.61 O v erall C apability Pp * PPL -0.63 PPU * P pk -0.63 C pm * 0.36 0.48 0.60 0.72 0.84 O bserv ed P erformance E xp. Within P erformance E xp. O v erall P erformanceP P M < LS L 971428.57 PPM < LS L 966214.72 P P M < LS L 969849.40PPM > USL * PPM > USL * PPM > USL *P P M Total 971428.57 PPM Total 966214.72 P P M Total 969849.40
  14. 14. Are there bad questions? NP Chart of Wrong Answers 40 1 1 30 1 1 1 1Sample Count UCL=24.25 20 __ NP=15.44 10 LCL=6.63 11 1 0 1 1 6 11 16 21 26 31 36 41 46 Sample
  15. 15. Does the order the exams are turned in effect the score? Trend Analysis Plot for Test Score Linear Trend Model Yt = 0.5018 + 0.00317*t 0.9 Variable Actual Fits 0.8 Accuracy Measures MAPE 15.9381 0.7 MAD 0.0840 Test Score MSD 0.0124 0.6 0.5 0.4 0.3 3 6 9 12 15 18 21 24 27 30 33 Index
  16. 16. TUESDAY RESULTS
  17. 17. Test Scores
  18. 18. Test for Normality
  19. 19. Is the process in Control? I Chart of Scores 1 90.00% UCL=84.62% 80.00% 70.00% Individual Value 60.00% _ X=55.93% 50.00% 40.00% 30.00% LCL=27.25% 20.00% 1 4 7 10 13 16 19 22 25 28 Observation
  20. 20. Is the process capable?
  21. 21. Are there Bad Questions? NP Chart of Incorrect 30 1 25 1 UCL=20.80 20 Sample Count 15 __ NP=12.78 10 5 LCL=4.76 1 1 1 1 1 0 1 1 6 11 16 21 26 31 36 41 46 Sample
  22. 22. Does the order exams are turned in effect test scores? Trend Analysis Plot for Scores Linear Trend Model Yt = 0.5614 - 0.000138*t 0.9 Variable Actual Fits 0.8 Accuracy Measures MAPE 13.9747 MAD 0.0779 0.7 MSD 0.0100 Scores 0.6 0.5 0.4 3 6 9 12 15 18 21 24 27 Index
  23. 23. COMBINED RESULTS
  24. 24. Combined Test Scores Histogram of Combined Normal 20 Mean 0.5591 StDev 0.1099 N 64 15Frequency 10 5 0 0.36 0.48 0.60 0.72 0.84 Combined
  25. 25. Test Scores Histogram of Monday, Tuesday Normal 0 0% 0 0% 0 0% 0 0% 0 0% 3 6. 4 8. 6 0. 7 2. 8 4. Monday Tuesday Monday 9 9 Mean 0.5589 StDev 0.1177 8 8 N 35 7 7 Tuesday Mean 0.5593Frequency 6 6 StDev 0.1018 N 29 5 5 4 4 3 3 2 2 1 1 0 0 36 48 60 72 84 0. 0. 0. 0. 0.
  26. 26. Is there a difference Between Classes? Boxplot of Monday, Tuesday Monday Tuesday 0.9 0.8 0.7 0.6 0.5 0.4 0.3
  27. 27. Is there a statistical Difference? Anova: Single Factor SUMMARY Groups Count Sum Average Variance Monday 35 19.56 0.558857 0.013857 Tuesday 29 16.22 0.55931 0.010357 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 3.26E-06 1 3.26E-06 0.000265 0.987056 3.995887 Within Groups 0.76114 62 0.012276 Total 0.761144 63
  28. 28. Brainstorming Techniques• At the beginning of class students were asked as a group to brainstorm ideas for why they failed the pre-test – Only 4 ideas were proposed• Students were taught the different brainstorming techniques contained in the CSSGB Body of Knowledge – Nominal Group Technique – Multi-Voting – Affinity Diagrams – Force Field Analysis – Tree Diagrams – Cause and Effect Diagrams• Students were then broken up into 6 different groups, assigned one of the brainstorming techniques and given the task to brainstorm why they failed the pre-test
  29. 29. Brainstorming Techniques Continued• Students then presented their results to the Group
  30. 30. Brainstorming ResultsCause and Effect (Fishbone) Affinity Diagram
  31. 31. Brainstorming Results Tree DiagramForce Field Analysis
  32. 32. Brainstorming Results Nominal Group TechniqueMulti-Voting
  33. 33. Brainstorming Continued• Students then were given told to return to their groups and apply their “favorite” of the brainstorming techniques to the task how can you Pass the midterm exam• Students Found the positive formulation of the task much more challenging and most groups stayed with the same technique they used for the Negative version.
  34. 34. Team Dynamics• The 3rd weeks lesson began with an introduction of the Tuckman cycle of team dynamics • Students were asked to reflect upon their experience in the brainstorming activity to see if their experiences paralleled those predicted by the model
  35. 35. Process Mapping• The second portion of the 3rd Class was spent introducing the process mapping strategies in the CSSGB BoK – SIPOC (Suppliers Inputs Outputs Customers) – Process Mapping – Value Stream Mapping
  36. 36. Process Mapping Continued• Students were again divided into 6 groups. Each group was assigned a map type and told to Map the Exam Taking Process at either a Micro or Macro Level• Micro Level Groups Handled the Physical steps of taking the exam such as reading the question, locating the answer and filling in the bubbles• Macro Groups Handled the all of the preparation leading up to taking the exam• The point was to emphasize that the same tools techniques and methods can be used on the very micro level (an operator tightening a bolt) to the very macro level (the operations of a fortune 500 company)
  37. 37. Control Charts• Class 4 Introduced Students to the Control Charts Covered in the CSSGB BoK – I-MR – X Bar-R – X Bar- S – P – NP – U – C• Students were emailed prior to class a Microsoft Excel Workbook containing the test results and told to bring their laptops to class• Students were asked to do the following by hand (with Excel helping for the calculations): – I-MR Chart for Test Scores – P Chart testing for “Bad Questions” – NP Chart testing for “Bad Questions” – C Chart for the number of wrong responses per exam – U Chart for the number of wrong responses per exam
  38. 38. Control Charts ResultsNP Chart C Chart
  39. 39. Midterm Analysis
  40. 40. Midterm Exam Results
  41. 41. Pre Class Exam Results
  42. 42. Comparison
  43. 43. Does a T-Test Indicate there was improvement? t-Test: Two-Sample Assuming Unequal Variances Mid Pre Mean 0.607234 0.561702 Variance 0.014373 0.01111 Observations 47 47 Hypothesized Mean Difference 0 df 91 t Stat 1.955429 P(T<=t) one-tail 0.0268 t Critical one-tail 1.661771 P(T<=t) two-tail 0.0536 t Critical two-tail 1.986377
  44. 44. Does ANOVA Indicate there was Improvement? Anova: Single Factor SUMMARY Groups Count Sum Average Variance Pre Total 64 35.78 0.559063 0.012082 Mid Total 53 31.72 0.598491 0.013705 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.045069 1 0.045069 3.516685 0.06329 3.923599 Within Groups 1.473823 115 0.012816 Total 1.518892 116
  45. 45. Change in Scores
  46. 46. Is the Change in Control? C Chart of Change in # of Correct Responses1510 UCL = 8.29 5 Mid= 2.28 0 LCL = -3.74 -5-10-15
  47. 47. Is the change in Scores Significant? t-Test: Paired Two Sample for Means Mid Pre Mean 0.607234043 0.561702 Variance 0.014372618 0.01111 Observations 47 47 Pearson Correlation 0.689206844 Hypothesized Mean Difference 0 df 46 t Stat 3.475995635 P(T<=t) one-tail 0.000560995 t Critical one-tail 1.678660414 P(T<=t) two-tail 0.00112199 t Critical two-tail 2.012895599
  48. 48. Not all Material on the Exam has been Covered in Class
  49. 49. Midterm Comparison
  50. 50. Pre Test Comparison
  51. 51. Comparison of Results for Material that has been Covered Boxplot of Covered Scores 1.0 0.9 0.8Covered Scores 0.7 0.6 0.5 0.4 0.3 Pre Covered Mid Covered Subscripts
  52. 52. Comparison of Covered Material Histogram of Pre Covered, Mid Covered Normal 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Pre Covered Mid Covered Pre Cov ered Mean 0.5785 12 StDev 0.1252 N 64 10 Mid Cov ered Mean 0.6516 StDev 0.1174 Frequency 8 N 53 6 4 2 0 0.3 0.4 0.5 0.6 0.7 0.8 0.9
  53. 53. Does ANOVA Indicate there was improvement? Anova: Single Factor SUMMARY Groups Count Sum Average Variance Pre Covered 64 37.02632 0.578536 0.015686 Mid Covered 53 34.53333 0.651572 0.013785 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.154648 1 0.154648 10.43065 0.001616 3.923599 Within Groups 1.70503 115 0.014826 Total 1.859678 116
  54. 54. Comparison of Results for Material that has not been Covered Boxplot of Scores 0.9 0.8 0.7 0.6 0.5 Scores 0.4 0.3 0.2 0.1 0.0 Pre Not Covered Mid Not Covered Subscripts
  55. 55. Comparison of Material Not Covered
  56. 56. Does ANOVA indicate the Exam was harder? Anova: Single Factor SUMMARY Groups Count Sum Average Variance Pre Not Covered 64 31.83333 0.497396 0.01785 Mid Not Covered 53 27.5 0.518868 0.024926 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.013367 1 0.013367 0.635003 0.427168 3.923599 Within Groups 2.420698 115 0.02105 Total 2.434065 116
  57. 57. Is the Exam Taking Process Capable?
  58. 58. Control Charts with Minitab• Students were emailed a Microsoft Excel Workbook with the Mid- Term data set• It was heavily suggested that students purchase the Minitab academic license and bring their laptops to class.• Students then divided themselves into groups around those who purchased the software and created the analysis control charts on the preceding slides.
  59. 59. Hypothesis Testing Exercises• In week 8 students were introduced to the hypothesis tests covered in CSSGB BoK – Z Test – Student T – Two Sample T (known variance) – Two Sample T (unknown variance) – Paired T Test – ANOVA – Chi Squared T – F Test• Students were emailed a data set containing both the Pre-Test and Mid-Term data and asked to perform each of the listed test using either Minitab or Microsoft Excel. The emphasis was placed on the conclusions from the data
  60. 60. Confidence Intervals• Not all students took the Mid-Term that took the pre-test.• This enabled students to utilize inferential statistics to draw conclusions about the population parameters (mean and variance particularly)• By using the class data set provided students were able to calculate their confidence in the overall population parameters for the average test score as well as the standard deviation of the entire class
  61. 61. Improve-Control• Improve and Control are not an emphasis in the CSSGB BoK. For the coverage of the material and extended example of the Starbucks Experience from a customers perspective is presented.• When introducing Lean and the types of Waste the process of making various beverages are presented. Students then proposed improvement strategies to minimize the ‘Muda’ Triple Tall Half Hot Half Cold Americano (Future State) Triple Tall Half Hot Half Cold Americano (Current State)
  62. 62. Final Exam Analysis
  63. 63. Exam Scores
  64. 64. Doesn’t Look Normal
  65. 65. It’s Bi-Modal!
  66. 66. Did the scores Improve?
  67. 67. Was The Difference Significant? Anova: Single Factor SUMMARY Groups Count Sum Average Variance Pre 64 35.78 0.559063 0.012082 Mid 47 28.54 0.607234 0.014373 Final 40 30.43 0.76075 0.020084 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 1.029282 2 0.514641 34.534 4.91E-13 3.057197 Within Groups 2.205562 148 0.014902 Total 3.234844 150
  68. 68. Individual ImprovementVariable N N* Mean StDev Minimum Q1 Median Q3Change 36 0 0.1939 0.1419 -0.0600 0.0675 0.2000 0.2875
  69. 69. Was the Individual Improvement Significant? t-Test: Paired Two Sample for Means Final Pre Mean 0.750556 0.556667 Variance 0.019743 0.010023 Observations 36 36 Pearson Correlation 0.342582 Hypothesized Mean Difference 0 df 35 t Stat 8.199954 P(T<=t) one-tail 5.8E-10 t Critical one-tail 1.689572 P(T<=t) two-tail 1.16E-09 t Critical two-tail 2.030108
  70. 70. Where there Hard Questions?
  71. 71. Pareto Chart on Topic Pareto Chart of Question Topic 16 100 14 12 80 10 Percent 60Count 8 6 40 4 20 2 0 0 Question Topic s r l at sis ro bl ity s va r ts EA St he Er a am er ha FM c ot ap Te In t lC si yp C e ro Ba H s c nt es en Co oc fid Pr n Co Count 3 3 2 2 2 1 1 1 Percent 20.0 20.0 13.3 13.3 13.3 6.7 6.7 6.7 Cum % 20.0 40.0 53.3 66.7 80.0 86.7 93.3 100.0
  72. 72. Initial Process Capability
  73. 73. Final Process Capability
  74. 74. Results• Students test scores improved on average 19.4%• The Passage Rate on the actual ASQ Administered Certified Six Sigma Greenbelt Exam Far exceeded the national average*• 68.75% of respondents to an online survey ranked their level of satisfaction with the course at a 5 or higher on a 7 point scale• Increased ASQ Princeton Membership by 62 members
  75. 75. Lessons Learned• Using the passing the exam process as a class exam for the implementation of the tools and techniques of Six Sigma is an effective methodology• There is demand for teaching Six Sigma in an academic setting• The joint venture between Rutgers and ASQ is feasible and mutually beneficial.• Having a diverse student population increases the overall performance of the group.• Students need to be adequately qualified to sit for ASQ exam prior to taking the course.

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