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2013 QMOD Presentation

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Presentation for the 16th QMOD conference which details a novel approach of using the tools techniques and methods of Six Sigma to improve students learning of Six Sigma

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2013 QMOD Presentation

  1. 1. Teaching Six Sigma Using Six Sigma a DMAIC Approach Brandon Theiss Brandon.Theiss@gmail.com 2013 QMOD Conference
  2. 2. About Me • Academics – MS Industrial Engineering Rutgers University – BS Electrical & Computer Engineering Rutgers University – BA Physics Rutgers University • Awards – ASQ Top 40 Leader in Quality Under 40 – ASQ National Education Quality Excellence Award Finalist – IIE Early Career Achievement Award Winner 2013 • Professional – Principal Industrial Engineer -Medrtonic – Master Black belt- American Standard Brands – Systems Engineer- Johnson Scale Co • Licenses – Licensed Engineer – State of Vermont – Registered to Practice before the US Patent and Trademark Office • Certifications – ASQ Certified Manager of Quality/ Org Excellence Cert # 13788 – ASQ Certified Quality Auditor Cert # 41232 – ASQ Certified Quality Engineer Cert # 56176 – ASQ Certified Reliability Engineer Cert #7203 – ASQ Certified Six Sigma Green Belt Cert # 3962 – ASQ Certified Six Sigma Black Belt Cert # 9641 – ASQ Certified Software Quality Engineer Cert # 4941 • Publications – Going with the Flow- The importance of collecting data without holding up your processes- Quality Progress March 2011 – "Numbers Are Not Enough: Improved Manufacturing Comes From Using Quality Data the Right Way" (cover story). Industrial Engineering Magazine- Journal of the Institute of Industrial Engineers September (2011): 28-33. Print
  3. 3. Learning Objectives • Apply Six Sigma to the Teaching of Six Sigma • Create Practitioner Academic Partnerships • Uniquely Apply SPC Charts • Use Statistical Hypothesis testing to improve learning outcomes
  4. 4. 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. Students 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.)
  5. 5. 5 Putting some numbers to the motivation • Lean Six Sigma is a commonly adopted business improvement technique which integrates, the scientific method, statistics and defect reduction to obtain tangible results. •Within 50 miles of Rutgers there are 2,249 active job listings for the phrase “six sigma green belt” •Non University Affiliated Classes are available however are prohibitively expensive for most students ~$2,000. •ASQ de facto industry standard for Greenbelt Certification •Current Industrial Engineering Undergraduate and Graduate programs do not prepare students to effectively implement the Six Sigma toolkit. •Salary Report indicates Certified Green belts earn $12,000 more per year
  6. 6. Class Demographics • 71 Students Registered – 57 At Student Tuition Rate ($296) – 14 At Professional Tuition Rate ($495) 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% Junior Year Senior Year BA/BS Some Grdudate MA/MS/JD PhD/PE Highest Accademic Grade Completed 24222018161412108642 20 15 10 5 0 Years Of Work Exprience Frequency 3 Histogram of Years Of Work Exprience
  7. 7. 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
  8. 8. Theoretical Pedagogical Support • Knowles' Theory of Andragogy is based upon six fundamental assumptions related to motivation in adult learning: – Adults become aware of their "need to know" and make a case for the value of learning.(Need to Know) • the students in the class have self selected to enroll in the course, which was marketed solely to prepare students to pass the certification exam. – Adults approach learning with different experiences furthermore the richest resource for learning resides in adults themselves (Foundation). • for each topic introduced there is a tactical application applying the tool or technique to the process of preparing for the ASQ CSSGB exam. – Adults need to be responsible for their decisions on education; They need to be seen and treated as capable and self-directed. (Self-concept). • no formal grades are given in the course and no homework is assigned. Students are responsible for gauging what additional preparation was required in addition to the 3-hour weekly course
  9. 9. Theoretical Pedagogical Support (continued) – Adults are most interested in learning subjects having immediate relevance to cope effectively with the present real-life situations (Readiness). • the course culminated in students taking the actual ASQ CSSGB exam, thus for the 11 weeks of the course students were constantly driving towards a relevant and timely goal – Adult learning is problem-centered rather than content-oriented they want to learn what will help them perform tasks or deal with problems they confront in everyday situations and those presented in the context of application to real-life (Orientation). • each activity throughout the course is driven at solving a problem with only a mild suggestion of which tool to use. – Adults respond better to internal versus external motivators (Motivation). • the motivation for the students to take the course was entirely self originating.
  10. 10. 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
  11. 11. Course Syllabus 1. Introduction, Sample Exam 2. Review Exam, Define 1 3. Define 2, Measure 1 4. Measure 2, Measure 3 5. Measure 4, Sample 50 Question Exam 6. Review Exam, Analyze 1 7. Analyze 2, Analyze 3 8. Improve 1, Sample 50 Question Exam 9. Review Exam, Control 1 10. Sample 100 Question Exam 11. Review Exam, Additional Questions Define Measure Analyze Improve Control • Project Definition • Team Dynamics • Brainstorming • Process Mapping • Measurement Systems • Histograms • Box Plots • Dot Plots • Probability Plots • Control Charts • Inferential Statistics • Confidence Intervals • Hypothesis Tests • Regression Analysis • Pareto Charts • Process Capability • Lean
  12. 12. 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)
  13. 13. 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
  14. 14. MONDAY RESULTS
  15. 15. Test Scores 84.00%72.00%60.00%48.00%36.00% 9 8 7 6 5 4 3 2 1 0 Test Scores Frequency Mean 0.5589 StDev 0.1177 N 35 Histogram of Test Scores Normal
  16. 16. Test for Normality 1.00.90.80.70.60.50.40.30.2 99 95 90 80 70 60 50 40 30 20 10 5 1 Test Score Percent Mean 0.5589 StDev 0.1177 N 35 AD 0.396 P-Value 0.352 Probability Plot of Test Score Normal - 95% CI
  17. 17. Is process in Control? 343128252219161310741 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Observation IndividualValue _ X=0.5589 UCL=0.9468 LCL=0.1709 I Chart of Test Score
  18. 18. Is the Process Capable? 0.840.720.600.480.36 LSL LSL 0.78 Target * USL * Sample Mean 0.558857 Sample N 35 StDev (Within) 0.120985 StDev (O v erall) 0.117718 Process Data C p * C PL -0.61 C PU * C pk -0.61 Pp * PPL -0.63 PPU * Ppk -0.63 C pm * O v erall C apability Potential (Within) C apability PPM < LSL 971428.57 PPM > USL * PPM Total 971428.57 O bserv ed Performance PPM < LSL 966214.72 PPM > USL * PPM Total 966214.72 Exp. Within Performance PPM < LSL 969849.40 PPM > USL * PPM Total 969849.40 Exp. O v erall Performance Within Overall Process Capability of Test Scores overall standard deviation for the entire study overall standard deviation for the entire study if special cause eliminated based on variation within subgroups
  19. 19. Are there bad questions? 464136312621161161 1.0 0.8 0.6 0.4 0.2 0.0 Sample Proportion _ P=0.441 UCL=0.693 LCL=0.189 1 1 1 1 1 1 11 11 P Chart of Wrong
  20. 20. Does the order the exams are turned in effect the score? 3330272421181512963 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Index TestScore MAPE 15.9381 MAD 0.0840 MSD 0.0124 Accuracy Measures Actual Fits Variable Trend Analysis Plot for Test Score Linear Trend Model Yt = 0.5018 + 0.00317*t
  21. 21. TUESDAY RESULTS
  22. 22. Test Scores
  23. 23. Test for Normality
  24. 24. Is the process in Control? 28252219161310741 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% Observation IndividualValue _ X=55.93% UCL=84.62% LCL=27.25% 1 I Chart of Scores
  25. 25. Is the process capable?
  26. 26. Are there Bad Questions? 464136312621161161 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Sample Proportion _ P=0.441 UCL=0.717 LCL=0.164 1 1 1 11 1 1 1 P Chart of Incorrect
  27. 27. 272421181512963 0.9 0.8 0.7 0.6 0.5 0.4 Index Scores MAPE 13.9747 MAD 0.0779 MSD 0.0100 Accuracy Measures Actual Fits Variable Trend Analysis Plot for Scores Linear Trend Model Yt = 0.5614 - 0.000138*t Does the order exams are turned in effect test scores?
  28. 28. COMBINED RESULTS
  29. 29. Combined Test Scores 0.840.720.600.480.36 20 15 10 5 0 Combined Frequency Mean 0.5591 StDev 0.1099 N 64 Histogram of Combined Normal
  30. 30. Test Scores 0.84 0.72 0.60 0.48 0.36 9 8 7 6 5 4 3 2 1 0 84.00% 72.00% 60.00% 48.00% 36.00% 9 8 7 6 5 4 3 2 1 0 Monday Frequency Tuesday Mean 0.5589 StDev 0.1177 N 35 Monday Mean 0.5593 StDev 0.1018 N 29 Tuesday Histogram of Monday, Tuesday Normal
  31. 31. Is there a difference Between Classes? 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Monday Tuesday Boxplot of Monday, Tuesday
  32. 32. 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
  33. 33. Is the variation different?
  34. 34. 34 464136312621161161 1.0 0.8 0.6 0.4 0.2 0.0 Sample Proportion _ P=0.441 UCL=0.627 LCL=0.255 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 P Chart of Wrong What Can we See from the Out of Control Points?
  35. 35. 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
  36. 36. Brainstorming Techniques Continued • Students then presented their results to the Group
  37. 37. Brainstorming Results Cause and Effect (Fishbone) Affinity Diagram
  38. 38. Brainstorming Results Tree Diagram Force Field Analysis
  39. 39. Brainstorming Results Multi-Voting Nominal Group Technique
  40. 40. Brainstorming Continued • Students then 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.
  41. 41. 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
  42. 42. Process Mapping • The second portion of the 3rd Class was spent introducing the process mapping strategies in the CSSGB BoK – SIPOC (Suppliers Inputs Process Outputs Customers) – Process Mapping – Value Stream Mapping
  43. 43. 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)
  44. 44. 44 SIPOC at a even higher level Input • Students • Body of Knowledge • Instructor • Textbook • Facilities Supplier •ASQ Princeton •ASQ Corporate •Rutgers University Output • Knowledge • Certification Customers • Future Employers • Current Employers • Students • Rutgers University • ASQ Princeton • Rutgers IIE Educate Students in Six Sigma Process Identify Educational Shortcoming Create Course Develop Methodology Locate Students Teach Students Administer Test
  45. 45. 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
  46. 46. Control Charts Results NP Chart C Chart
  47. 47. Midterm Analysis
  48. 48. Midterm Exam Results
  49. 49. Pre Class Exam Results
  50. 50. Comparison
  51. 51. 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
  52. 52. 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
  53. 53. Change in Scores
  54. 54. Is the Change in Control? -15 -10 -5 0 5 10 15 C Chart of Change in # of Correct Responses UCL = 8.29 LCL = -3.74 Mid= 2.28
  55. 55. 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
  56. 56. Not all Material on the Exam has been Covered in Class
  57. 57. Midterm Comparison
  58. 58. Pre Test Comparison
  59. 59. Comparison of Results for Material that has been Covered Mid CoveredPre Covered 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Subscripts CoveredScores Boxplot of Covered Scores
  60. 60. Comparison of Covered Material 0.90.80.70.60.50.40.3 12 10 8 6 4 2 0 0.90.80.70.60.50.40.3 Pre Covered Frequency Mid Covered Mean 0.5785 StDev 0.1252 N 64 Pre Covered Mean 0.6516 StDev 0.1174 N 53 Mid Covered Histogram of Pre Covered, Mid Covered Normal
  61. 61. 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
  62. 62. Comparison of Results for Material that has not been Covered Mid Not CoveredPre Not Covered 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Subscripts Scores Boxplot of Scores
  63. 63. Comparison of Material Not Covered
  64. 64. 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
  65. 65. Is the Exam Taking Process Capable?
  66. 66. 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.
  67. 67. 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
  68. 68. 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
  69. 69. Was the Pre-Test a Predictor of the Mid Term Scores?
  70. 70. Improve-Control “Improve” and “Control” phase represent a small fraction of the material covered on the ASQ CSSGB exam Within the Body of Knowledge there are the following : • Gantt Chart • Activity Network Diagrams • Critical Path Method • Program (or Project) Evaluation and Review Technique.
  71. 71. Final Exam Analysis
  72. 72. Exam Scores
  73. 73. Doesn’t Look Normal
  74. 74. It’s Bi-Modal!
  75. 75. Did the scores Improve?
  76. 76. 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
  77. 77. Individual Improvement Variable N N* Mean StDev Minimum Q1 Median Q3 Change 36 0 0.1939 0.1419 -0.0600 0.0675 0.2000 0.2875
  78. 78. 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
  79. 79. Where there Hard Questions?
  80. 80. Pareto Chart on Topic 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 Question Topic FM EA Control Charts Confidence Interval Team s Process Capablity Error Hypothesis Basic Stats 16 14 12 10 8 6 4 2 0 100 80 60 40 20 0 Count Percent Pareto Chart of Question Topic
  81. 81. Initial Process Capability
  82. 82. Final Process Capability
  83. 83. Results • Ruba Amarin • Margit Barot • Miriam Bicej • Matthew Brown • Salem El-Nimri • William Ewart • Elizabeth Fuschetti • Robert Gaglione • Thomas Hansen • Tarun Jada • Javier Jaramillo • Michael Kagan • Anoop Krishnamurthy • Timothy Lin • Helen Liou • Rebecca Marzec • Charles Ott • Sneha Patil • Eugene Reshetov • Matthew Rodis • Thomas Schleicher • Dante Triana • Albert Tseng • Bond Wann • Paul White • Sun Wong • Shih Yen • Jacob Ziegler 28 out of 37 Students that took the June 2nd Exam Passed the June 2nd Exam Nationally 788 out of 1160 individuals passed the exam
  84. 84. Was the Result Significant? Rutgers ASQ
  85. 85. Results • Students test scores improved on average 19.4% • 76% of Students Passed the exam compared to 68% National Average • Increased ASQ Princeton Membership by 62 members • Largest Ever Fund Raiser for the Rutgers IIE
  86. 86. 86 Added Benefit • From the funds generated by the course Rutgers was able to send 21 Students to the national IIE Conference in Orlando (shown above)
  87. 87. 87 It took a team • Nate Manco – ASQ Princeton Education Chair • Richard Herczeg – ASQ Princeton Section President • Jeff Metzler – Rutgers IIE President • Dr. James Luxhoj – Rutgers Industrial and Systems Eng • Brandon Theiss – Instructor • Cindy Ielmini – Rutgers Industrial and Systems Eng
  88. 88. Lessons Learned • Using the passing the exam process as a class example 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.
  89. 89. 89 We are sharing the Results • Presented results at Institute of Industrial Engineers Lean and Six Sigma Conference • Will be presented at the ASQ International Conference on Quality
  90. 90. 90 Progress continues onward • Course Scheduled to Run again in the Spring through Official Continuing Education Office • First of its kind joint meeting with ASQ Princeton and Rutgers IIE in which the course results were presented.
  91. 91. Questions? • Contact info – Brandon Theiss – Brandon.theiss@gmail.com – Connect to me on LinkedIn

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