Teaching Six Sigma Using Six Sigma


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Presentation from the 2013 ASQ World Conference on Quality and Improvement

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  • Six Sigma is a problem solving tool kit that seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes.Six Sigma Green Belts are the tactical leads on improving functions within a job function that are able to apply the Lean Sigma Concepts to their daily work.The methods are universally applicable to anything where a customer is being serviced.
  • The cost of the course for students included the textbook and ASQ student membershipThe professional rate only included the text.The ASQ Certified Six Sigma Green Belt Requires 3 or more years of work experience in one of more areas of the Body of Knowledge. There was a very long and at times heated exchange with the ASQ certification committee about what constitutes work experience. A compromise was ultimately reached however there were still a large number of qualified students that were denied the right to sit for the exam
  • This is a unique pedagogical approach and from philosophically is quite “meta”. The objective under examination is in fact the actor performing the examination.The most brilliant of teacher can write the most profound equation on a chalkboard, and the most diligent of students can take pristine notes. However learning only occurs when the student is able to apply the material. Johann Wolfgang von Goethe was correct when he said “Knowing is not enough; we must apply.”Given the diversity of the composition of the students in terms of education, life experience, income and industry finding a common task in which to apply the LSS would have been impossible. The only true commonality between the group was that they were all humans and wanted to earn their greenbelt. We were able to leverage this fact in developing the instructional roadmap for course.Also the utilization of Shewhart Control Charts which are used to differentiate between common cause and special cause variation, is fairly novel in academic settings.
  • The instructor for the course, Brandon Theiss, is a Senior Member of ASQ and a Graduate student at Rutgers University. Currently there is not a course offered in the undergraduate Industrial and Systems Engineering Program at Rutgers. This course provided an opportunity for students to not only be exposed to the material but also to earn a nationally recognized certification in the tools techniques and methods of Six Sigma. It represented a first of its kind partnership between the student chapter of the IIE and ASQ Princeton section. Part of the proceeds for the course were used to fund the IIE trip to their national conference in Orlando.
  • The course met once per week over an 11 week period from 6:30 to 9:30PM. There were two sessions per week and students were free to attend either the Monday or Tuesday class based upon which ever was more convenient for their schedule
  • Students were notified via email prior to the first night of the course that an exam would be administered on the first night.This provided both a baseline for the future improvement as well as showing students directly the level of mastery they would need to obtain to become certified.
  • Feedback in any system is critically important. With a course that only meets once per week, having students wait a week would be to long. By providing students immediate feedback they were able to best utilize their time to study as well as not mis-learn material thinking that they had been correct on a question when in fact they were not.
  • A simple histogram of the exam results from the Monday section with a normal distribution fit. It does appear to be normal but has a very large standard deviation 11.8%
  • The probability plot indicates that there is insufficient data to reject the null hypothesis that the data is normally distributed. This is indicated by the P value which indicates the probability that the difference between the measured data and the model occurred by pure chance. The null hypothesis of normality would have been rejected if the value had been less than alpha (5%) representing a 95% confidence level.
  • It is technically debatable if the test scores are continuous or discrete variable and if a I chart is appropriate. However the point is to introduce students to control charts and an Individuals chart.Since no point lies about the Upper or Lower Control Limit, the process is in a state of “statistical control”. However common sense shows that this is nonsensical as the range of the limits is between 17% and 95%. This was caused by the large standard deviation observed.This was used as an opportunity to discuss the difference between statistical significance and actual significance. This reinforces the concept that the math does not know where the numbers came from and can at best direct teams to derive the true underlying meaning.
  • Again there is a technical point if the test scores are discrete or continuous. The above Process Capability study requires that the data be considered continuous. Process capability is essentially the probability of producing a product that will meet your customers specification. In this case the passing score (78%) sets that limit. As you can see in the above chart for every 1,000,000 students from the Monday population that took the pre-test exam ~970,000 students will fail.
  • Everyone has taken a test where the test taker believes there was a question that either had the wrong answer or was too difficult. By using a NP (or P) control chart, one can easily distinguish if a question was statistically significantly too difficult above the UCL or too easy below the LCL
  • There were several students who handed in their exams very quickly. We wanted to see if the amount of time a student spent on the exam effected their scores. And for the Monday data set it appears it did.
  • A histogram of the Tuesday data set
  • Again the data is normal as indicated by a P value greater than 5%. It is however notable in the above plot that there is a clear outlier.
  • Again we can see that there is clearly an outlier in the data set.
  • The Tuesday process is very similar in its inability to produce a unit meeting customers expectations and again will generate ~970,000 failures for every million students from the population that take the exam
  • In the above graph it does appear that there were questions that a statistically significant number of students got wrong.
  • Interestingly, the order in which a student turned in their exam did not have an effect on the Tuesday data set.
  • Combined Histogram of the results
  • Both distributions look somewhat similar.
  • The above shows a box plot comparing the two classes. The median appears to be higher in the Tuesday class. However is the difference significant?
  • An ANOVA analysis was performed which results in a very high p value which means that there is not a statistically significant difference between the two population means.
  • A Pareto Chart of the topic involved for each of the Out of Control Data points from the combined P Chart
  • Nominal Group -> when individuals over power a groupMulti-Voting -> Reduce a large list of items to a workable number quicklyAffinity Diagram -> Group solutionsForce Field Analysis -> Overcome Resistance to ChangeTree Diagram -> Breaks complex into simpleCause- Effect Diagram -> identify root causes
  • Most Common Model of group Development was proposed by Bruce Tuckman in 1965.In order for the team to grow, to face up to challenges, to tackle problems, to find solutions, to plan work, and to deliver results. They must go through the cycleFormingTeam members getting to know each otherTrying to please each otherMay tend to agree too much on initial discussion topicsNot much work accomplishedMembers orientation on the team goalsGroup is going through “honeymoon period”StormingVoice their ideaUnderstand project scope and responsibilitiesIdeas and understanding cause conflictNot much work gets accomplishedDisagreement slows down the teamNormingResolve own conflictsCome to mutually agreed planSome work gets doneStart to trust each otherPerformingLarge amount of work gets doneSynergy realized Competent and autonomous decisions are madeAdjourningTeam is disbanded, restructured or project re-scoped.Regression to Forming stage
  • Control Charts are used to differentiate between common cause (normal) and special cause (abnormal) variation.
  • There does not appear to be a large change between the Pre Test and the Mid Term
  • A T-Test indicates that there is significant improvement, as indicated by the one tail P value.
  • ANOVA on the other hand indicates that there is not a difference between the two means.
  • Displays a histogram of the changes in scores, about 40% of the students went down and 60% increased their score.
  • This is a somewhat novel adaptation of a C chart that allows for negative values. However there appear to be students that did much better and much worse than the other students.
  • Looking at a Paired-T test there was absolutely a statistically significant improvement.
  • Why did the test scores not improve more dramatically? Well the exams cover all of the material in the CSSGB BoK the course was only half complete. When we looked at the material covered up to the midterm on both the pre-test and the mid term the above pie charts show the percentage of the covered material on each exam.
  • Not surprisingly students performed better on the material that was covered as compared to the material that was not covered.
  • However the students also scored better on that same material on the pre test.
  • So was there actual improvement?
  • The change in the means indicates a ~8% improvement. However is that statistically significant?
  • ANOVA does indicates that there is a difference in the means. The students did in fact learn the material that was covered.
  • There does not appear to be a difference in the scores in the material that was not covered yet in the course.
  • There was a small increase in the means ~2% is that significant?
  • No. There is not a statistically significant difference between the pre-test and mid-term scores on the material that was not covered. As a result it would indicate that the exams were roughly the same difficulty.
  • The process is still incapable of generating a passing score on the test.
  • Minitab is the de facto industry standard for statistical process control. Unfortunately the undergraduate program at Rutgers does not include any training in the software suite. It is fairly intuitive however students needed additional instruction.
  • Unfortunately, as this courses primary purpose was to act preparation for the Greenbelt Exam a larger focus could not placed on this material. However in an industrial setting most projects fail in the control phase. Regression to the mean is the natural trend. Anyone that has ever tried to lose weight or quit smoking knows that the trouble is always in sustaining the improvement.
  • The above histogram does not quite look normal and has a very large standard deviation 14%.
  • A dot plot again shows a strange pattern.
  • The distribution is in fact bimodal. Unfortunately due to ASQ’s interpretation of the meaning of work, a large number of qualified application were unable to sit for the actual Greenbelt exam and became disenchanted with the course and represent the lower distribution. This assumption was supported by a post hoc online survey.
  • However the test scores did appear to approve (even with the lower distribution)
  • And the improvement was very significant as indicated P value of 4.91 x 10^-13
  • On average the students improved 19.4% only a few students scores decreased,
  • The Paired T Test Results also confirm that the students test scores improved!
  • A P Chart was again used to detect difficult questions.
  • A Pareto Chart above shows the topics that generated that special cause variation in the prior P chart.
  • The initial process capability was quite poor, producing defects ~970,000 failures per 1,000,0000
  • The final process capability though still not best in class, is much better, producing 475,000 failures per million (the observed is used since the data was already proven to be non normal as it is bimodal)
  • *Actual data has not yet been released for the national average yetAs Confucius says “I hear and I forget. I see and I remember. I do and I understand.”
  • Teaching Six Sigma Using Six Sigma

    1. 1. Teaching Six Sigma Using Six Sigma aDMAIC ApproachBrandon TheissBrandon.Theiss@gmail.com2013 ASQ WCQI Session (M30)
    2. 2. About Me• Academics– MS Industrial Engineering Rutgers University (hopefully)– 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• 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– Licensed to practice before United States Patent and Trademark Office• Publications– Going with the Flow- The importance of collecting data without holding up your processes- Quality Progress March2011– "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 improvelearning outcomes
    4. 4. Motivation• Teaching the tools, techniques and Methods of Lean SixSigma is inherently difficult in academic setting.• When taught in a industrial setting students have acommon motivation (the improved welfare of thecompany), similar levels of education and knowledge ofdomain specific information. Students are encouraged tolearn by applying the material to their daily activities.• This is not possible in an academic setting particularly in amixed environment that includes everything fromundergraduate juniors through senior PhD researchers.• In addition undergraduate students tend either lackprofessional or have experience in Fields that are nottraditionally thought of as benefiting or implementing SixSigma (waitressing, check out clerk etc.)
    5. 5. 5Putting some numbers to the motivation• Lean Six Sigma is a commonly adopted businessimprovement 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 listingsfor the phrase “six sigma green belt”•Non University Affiliated Classes are available however areprohibitively expensive for most students ~$2,000.•ASQ de facto industry standard for Greenbelt Certification•Current Industrial Engineering Undergraduate and Graduateprograms do not prepare students to effectively implement theSix Sigma toolkit.•Salary Report indicates Certified Green belts earn $12,000more 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 SeniorYearBA/BS SomeGrdudateMA/MS/JD PhD/PEHighest Accademic GradeCompleted2422201816141210864220151050Years Of Work ExprienceFrequency3Histogram of Years Of Work Exprience
    7. 7. Solution• The beauty of the Six Sigma Methodology is that it can be appliedto any process.• The definition of a process is quite broad and can be reduced to anyverb- noun combination.• Therefore the collective process which the class studied andimproved was toPass [the]ASQCertified Six Sigma GreenBelt Exam• Therefore the foundational Six Sigma Concept of DMAIC (DefineMeasure Analyze Improve Control) represents both the materialcovered in the course as well as the pedagogical method used forinstruction
    8. 8. About the Course & Partnership• Offered as a Non-Credit extracurricular courseat Rutgers University in Piscataway NJ• Co-Sponsored by the Rutgers Student Chapterof the Institute for Industrial Engineers (IIE) andthe Princeton NJ section of American Society forQuality (ASQ)• Open and advertised to all members of theRutgers Community (students, staff and faculty)as well as the surrounding public• Objective of the course was to train students topass the June 2nd 2012 administration of theASQ Certified Six Sigma Green Belt Exam
    9. 9. Course Syllabus1. Introduction, Sample Exam2. Review Exam, Define 13. Define 2, Measure 14. Measure 2, Measure 35. Measure 4, Sample 50 QuestionExam6. Review Exam, Analyze 17. Analyze 2, Analyze 38. Improve 1, Sample 50 Question Exam9. Review Exam, Control 110. Sample 100 Question Exam11. Review Exam, Additional QuestionsDefine Measure Analyze Improve Control• Project Definition• Team Dynamics• Brainstorming• Process Mapping• MeasurementSystems• Histograms• Box Plots• Dot Plots• ProbabilityPlots• Control Charts• InferentialStatistics• ConfidenceIntervals• HypothesisTests• RegressionAnalysis• Pareto Charts• ProcessCapability• Lean
    10. 10. Pre Test• On the first night of classes students weregiven an introductory survey of Six Sigmaby means of a worked example applyingDMAIC to the Starbucks Experience from aCustomers Prospective.• Students were then given a copy of theCertified Six Sigma Green Belt Handbook byRoderick A. Munro• Then given a 50 Question Multiple ChoiceTest representative of the ASQ CSSGB Exam• The Test was administered on twosuccessive nights (Monday and Tuesday)
    11. 11. Measurement System• An Apperson GradeMaster™ 600 TestScanner was utilized which enabled test tobe scored and returned immediately uponstudent submission at the exam site.• In addition all of each answer to everyquestion was downloaded to connectedcomputer enabling further detailed analysis
    12. 12. MONDAY RESULTS
    13. 13. Test Scores84.00%72.00%60.00%48.00%36.00%9876543210Test ScoresFrequencyMean 0.5589StDev 0.1177N 35Histogram of Test ScoresNormal
    14. 14. Test for Normality1. ScorePercentMean 0.5589StDev 0.1177N 35AD 0.396P-Value 0.352Probability Plot of Test ScoreNormal - 95% CI
    15. 15. Is process in Control?3431282522191613107411. Chart of Test Score
    16. 16. Is the Process Capable?0.840.720.600.480.36LSLLSL 0.78Target *USL *Sample Mean 0.558857Sample N 35StDev (Within) 0.120985StDev (O v erall) 0.117718Process DataC p *C PL -0.61C PU *C pk -0.61Pp *PPL -0.63PPU *Ppk -0.63C pm *O v erall C apabilityPotential (Within) C apabilityPPM < LSL 971428.57PPM > USL *PPM Total 971428.57O bserv ed PerformancePPM < LSL 966214.72PPM > USL *PPM Total 966214.72Exp. Within PerformancePPM < LSL 969849.40PPM > USL *PPM Total 969849.40Exp. O v erall PerformanceWithinOverallProcess Capability of Test Scoresoverall standarddeviation for theentire studyoverall standard deviation forthe entire study if specialcause eliminatedbased on variationwithin subgroups
    17. 17. Are there bad questions?4641363126211611611. Chart of Wrong
    18. 18. Does the order the exams are turned in effect thescore?33302724211815129630. 15.9381MAD 0.0840MSD 0.0124Accuracy MeasuresActualFitsVariableTrend Analysis Plot for Test ScoreLinear Trend ModelYt = 0.5018 + 0.00317*t
    20. 20. Test Scores
    21. 21. Test for Normality
    22. 22. Is the process in Control?2825221916131074190.00%80.00%70.00%60.00%50.00%40.00%30.00%20.00%ObservationIndividualValue_X=55.93%UCL=84.62%LCL=27.25%1I Chart of Scores
    23. 23. Is the process capable?
    24. 24. Are there Bad Questions?4641363126211611610. Chart of Incorrect
    25. 25. 2724211815129630. 13.9747MAD 0.0779MSD 0.0100Accuracy MeasuresActualFitsVariableTrend Analysis Plot for ScoresLinear Trend ModelYt = 0.5614 - 0.000138*tDoes the order exams are turned ineffect test scores?
    27. 27. Combined Test Scores0.840.720.600.480.3620151050CombinedFrequencyMean 0.5591StDev 0.1099N 64Histogram of CombinedNormal
    28. 28. Test Scores0.840.720.600.480.36987654321084.00%72.00%60.00%48.00%36.00%9876543210MondayFrequencyTuesdayMean 0.5589StDev 0.1177N 35MondayMean 0.5593StDev 0.1018N 29TuesdayHistogram of Monday, TuesdayNormal
    29. 29. Is there a difference Between Classes? TuesdayBoxplot of Monday, Tuesday
    30. 30. Is there a statistical Difference?Anova: Single FactorSUMMARYGroups Count Sum Average VarianceMonday 35 19.56 0.558857 0.013857Tuesday 29 16.22 0.55931 0.010357ANOVASource of Variation SS df MS F P-value F critBetween Groups 3.26E-06 1 3.26E-06 0.000265 0.987056 3.995887Within Groups 0.76114 62 0.012276Total 0.761144 63
    31. 31. Is the variation different?
    32. 32. 324641363126211611611. Chart of WrongWhat Can we See from the Out ofControl Points?
    33. 33. Brainstorming Techniques• At the beginning of class students were asked as a group tobrainstorm ideas for why they failed the pre-test– Only 4 ideas were proposed• Students were taught the different brainstorming techniquescontained 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 oneof the brainstorming techniques and given the task to brainstormwhy they failed the pre-test
    34. 34. Brainstorming Techniques Continued• Students then presented their results to theGroup
    35. 35. Brainstorming ResultsCause and Effect (Fishbone)Affinity Diagram
    36. 36. Brainstorming ResultsTree DiagramForce Field Analysis
    37. 37. Brainstorming ResultsMulti-VotingNominal Group Technique
    38. 38. Brainstorming Continued• Students then told to return to their groupsand apply their “favorite” of the brainstormingtechniques to the task how can you Pass themidterm exam• Students Found the positive formulation ofthe task much more challenging and mostgroups stayed with the same technique theyused for the Negative version.
    39. 39. Team Dynamics• The 3rd weeks lesson began with anintroduction of the Tuckman cycle of teamdynamics• Students were askedto reflect upon theirexperience in thebrainstorming activityto see if theirexperiences paralleledthose predicted by themodel
    40. 40. Process Mapping• The second portion of the 3rd Class was spentintroducing the process mapping strategies inthe CSSGB BoK– SIPOC (Suppliers Inputs Process Outputs Customers)– Process Mapping– Value StreamMapping
    41. 41. Process Mapping Continued• Students were again divided into 6 groups. Each group was assigned a map type andtold 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 readingthe 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 beused on the very micro level (an operator tightening a bolt) to the very macro level(the operations of a fortune 500 company)
    42. 42. 42SIPOC at a even higher levelInput• Students• Body ofKnowledge• Instructor• Textbook• FacilitiesSupplier•ASQ Princeton•ASQ Corporate•Rutgers UniversityOutput• Knowledge• CertificationCustomers• Future Employers• Current Employers• Students• Rutgers University• ASQ Princeton• Rutgers IIEEducateStudents in SixSigmaProcessIdentify EducationalShortcomingCreate CourseDevelopMethodologyLocate StudentsTeach StudentsAdminister Test
    43. 43. Control Charts• Class 4 Introduced Students to the Control Charts Covered in theCSSGB BoK– I-MR– X Bar-R– X Bar- S– P– NP– U– C• Students were emailed prior to class a Microsoft Excel Workbookcontaining the test results and told to bring their laptops to class• Students were asked to do the following by hand (with Excelhelping 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
    44. 44. Control Charts ResultsNP ChartC Chart
    45. 45. Midterm Analysis
    46. 46. Midterm Exam Results
    47. 47. Pre Class Exam Results
    48. 48. Comparison
    49. 49. Does a T-Test Indicate there was improvement?t-Test: Two-Sample Assuming Unequal VariancesMid PreMean 0.607234 0.561702Variance 0.014373 0.01111Observations 47 47Hypothesized Mean Difference 0df 91t Stat 1.955429P(T<=t) one-tail 0.0268t Critical one-tail 1.661771P(T<=t) two-tail 0.0536t Critical two-tail 1.986377
    50. 50. Does ANOVA Indicate there wasImprovement?Anova: Single FactorSUMMARYGroups Count Sum Average VariancePre Total 64 35.78 0.559063 0.012082Mid Total 53 31.72 0.598491 0.013705ANOVASource of Variation SS df MS F P-value F critBetween Groups 0.045069 1 0.045069 3.516685 0.06329 3.923599Within Groups 1.473823 115 0.012816Total 1.518892 116
    51. 51. Change in Scores
    52. 52. Is the Change in Control?-15-10-5051015C Chart of Change in # of Correct ResponsesUCL = 8.29LCL = -3.74Mid= 2.28
    53. 53. Is the change in Scores Significant?t-Test: Paired Two Sample for MeansMid PreMean 0.607234043 0.561702Variance 0.014372618 0.01111Observations 47 47Pearson Correlation 0.689206844Hypothesized Mean Difference 0df 46t Stat 3.475995635P(T<=t) one-tail 0.000560995t Critical one-tail 1.678660414P(T<=t) two-tail 0.00112199t Critical two-tail 2.012895599
    54. 54. Not all Material on the Exam has beenCovered in Class
    55. 55. Midterm Comparison
    56. 56. Pre Test Comparison
    57. 57. Comparison of Results for Materialthat has been CoveredMid CoveredPre Covered1. of Covered Scores
    58. 58. Comparison of Covered Material0. CoveredFrequencyMid CoveredMean 0.5785StDev 0.1252N 64Pre CoveredMean 0.6516StDev 0.1174N 53Mid CoveredHistogram of Pre Covered, Mid CoveredNormal
    59. 59. Does ANOVA Indicate there wasimprovement?Anova: Single FactorSUMMARYGroups Count Sum Average VariancePre Covered 64 37.02632 0.578536 0.015686Mid Covered 53 34.53333 0.651572 0.013785ANOVASource of Variation SS df MS F P-value F critBetween Groups 0.154648 1 0.154648 10.43065 0.001616 3.923599Within Groups 1.70503 115 0.014826Total 1.859678 116
    60. 60. Comparison of Results for Materialthat has not been CoveredMid Not CoveredPre Not Covered0. of Scores
    61. 61. Comparison of Material Not Covered
    62. 62. Does ANOVA indicate the Exam washarder?Anova: Single FactorSUMMARYGroups Count Sum Average VariancePre Not Covered 64 31.83333 0.497396 0.01785Mid Not Covered 53 27.5 0.518868 0.024926ANOVASource of Variation SS df MS F P-value F critBetween Groups 0.013367 1 0.013367 0.635003 0.427168 3.923599Within Groups 2.420698 115 0.02105Total 2.434065 116
    63. 63. Is the Exam Taking Process Capable?
    64. 64. 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 Minitabacademic license and bring their laptops to class.• Students then divided themselves into groups around those whopurchased the software and created the analysis control charts onthe preceding slides.
    65. 65. Hypothesis Testing Exercises• In week 8 students were introduced to the hypothesis tests coveredin 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 andMid-Term data and asked to perform each of the listed test usingeither Minitab or Microsoft Excel. The emphasis was placed on theconclusions from the data
    66. 66. Confidence Intervals• Not all students took the Mid-Term that took thepre-test.• This enabled students to utilize inferentialstatistics to draw conclusions about thepopulation parameters (mean and varianceparticularly)• By using the class data set provided studentswere able to calculate their confidence in theoverall population parameters for the averagetest score as well as the standard deviation of theentire class
    67. 67. Improve-Control• Improve and Control are not an emphasis in the CSSGB BoK. For thecoverage of the material and extended example of the StarbucksExperience from a customers perspective is presented.• When introducing Lean and the types of Waste the process of makingvarious beverages are presented. Students then proposed improvementstrategies to minimize the ‘Muda’Triple Tall Half Hot Half Cold Americano(Current State)Triple Tall Half Hot Half Cold Americano(Future State)
    68. 68. Final Exam Analysis
    69. 69. Exam Scores
    70. 70. Doesn’t Look Normal
    71. 71. It’s Bi-Modal!
    72. 72. Did the scores Improve?
    73. 73. Was The Difference Significant?Anova: Single FactorSUMMARYGroups Count Sum Average VariancePre 64 35.78 0.559063 0.012082Mid 47 28.54 0.607234 0.014373Final 40 30.43 0.76075 0.020084ANOVASource of Variation SS df MS F P-value F critBetween Groups 1.029282 2 0.514641 34.534 4.91E-13 3.057197Within Groups 2.205562 148 0.014902Total 3.234844 150
    74. 74. Individual ImprovementVariable N N* Mean StDev Minimum Q1 Median Q3Change 36 0 0.1939 0.1419 -0.0600 0.0675 0.2000 0.2875
    75. 75. Was the Individual ImprovementSignificant?t-Test: Paired Two Sample for MeansFinal PreMean 0.750556 0.556667Variance 0.019743 0.010023Observations 36 36Pearson Correlation 0.342582Hypothesized Mean Difference 0df 35t Stat 8.199954P(T<=t) one-tail 5.8E-10t Critical one-tail 1.689572P(T<=t) two-tail 1.16E-09t Critical two-tail 2.030108
    76. 76. Where there Hard Questions?
    77. 77. Pareto Chart on TopicCount 3 3 2 2 2 1 1 1Percent 20.0 20.0 13.3 13.3 13.3 6.7 6.7 6.7Cum % 20.0 40.0 53.3 66.7 80.0 86.7 93.3 100.0Question TopicFMEAControl ChartsConfidenceIntervalTeamsProcessCapablityErrorHypothesisBasicStats1614121086420100806040200CountPercentPareto Chart of Question Topic
    78. 78. Initial Process Capability
    79. 79. Final Process Capability
    80. 80. 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 Ziegler28 out of 37 Students that took the June 2nd Exam Passed the June 2nd ExamNationally 788 out of 1160 individuals passed the exam
    81. 81. Was the Result Significant?Rutgers ASQ
    82. 82. Results• Students test scores improved on average19.4%• 76% of Students Passed the exam comparedto 68% National Average• Increased ASQ Princeton Membership by 62members• Largest Ever Fund Raiser for the Rutgers IIE
    83. 83. 83Added Benefit• From the funds generated by the course Rutgers wasable to send 21 Students to the national IIEConference in Orlando (shown above)
    84. 84. 84It 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
    85. 85. Lessons Learned• Using the passing the exam process as a class examplefor the implementation of the tools and techniques ofSix Sigma is an effective methodology• There is demand for teaching Six Sigma in an academicsetting• The joint venture between Rutgers and ASQ is feasibleand mutually beneficial.• Having a diverse student population increases theoverall performance of the group.• Students need to be adequately qualified to sit for ASQexam prior to taking the course.
    86. 86. 86We are sharing the Results• Presented results at Institute of Industrial Engineers Lean and Six SigmaConference• Will be presented at the ASQ International Conference on Quality
    87. 87. 87Progress continues onward• Course Scheduled to Run again in the Spring through Official ContinuingEducation Office• First of its kind joint meeting with ASQ Princeton and Rutgers IIE inwhich the course results were presented.
    88. 88. Questions?• Contact info– Brandon Theiss– Brandon.theiss@gmail.com– Connect to me on LinkedIn