Teaching Six Sigma Using Six Sigma a DMAIC Approach

Brandon TheissBrandon.Theiss@gmail.com

2013 QMOD Conference

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

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

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.)

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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

Class Demographics• 71 Students Registered

– 57 At Student Tuition Rate ($296)– 14 At Professional Tuition Rate ($495)

Junior Yea

r

Senior Y

ear

BA/BS

Some G

rdudate

MA/MS/J

D

PhD/PE

0.0%5.0%

10.0%15.0%20.0%25.0%30.0%35.0%40.0%

Highest Accademic Grade Completed

24222018161412108642

20

15

10

5

0

Years Of Work Exprience

Frequency

3

Histogram of Years Of Work Exprience

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 toPass [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

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

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.

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

Course Syllabus1. Introduction, Sample Exam2. Review Exam, Define 13. Define 2, Measure 14. Measure 2, Measure 3 5. Measure 4, Sample 50 Question Exam6. Review Exam, Analyze 1

7. Analyze 2, Analyze 38. Improve 1, Sample 50 Question Exam9. Review Exam, Control 110. Sample 100 Question Exam11. 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

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)

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

MONDAY RESULTS

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 0.5589StDev 0.1177N 35

Histogram of Test ScoresNormal

Test for Normality

1.00.90.80.70.60.50.40.30.2

99

95

90

80

70

605040

30

20

10

5

1

Test Score

Perc

ent

Mean 0.5589StDev 0.1177N 35AD 0.396P-Value 0.352

Probability Plot of Test ScoreNormal - 95% CI

Is process in Control?

343128252219161310741

1.0

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Observation

Indiv

idual V

alu

e

_X=0.5589

UCL=0.9468

LCL=0.1709

I Chart of Test Score

Is the Process Capable?

0.840.720.600.480.36

LSL

LSL 0.78Target *USL *Sample Mean 0.558857Sample N 35StDev(Within) 0.120985StDev(Overall) 0.117718

Process Data

Cp *CPL -0.61CPU *Cpk -0.61

Pp *PPL -0.63PPU *Ppk -0.63Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 971428.57PPM > USL *PPM Total 971428.57

Observed PerformancePPM < LSL 966214.72PPM > USL *PPM Total 966214.72

Exp. Within PerformancePPM < LSL 969849.40PPM > USL *PPM Total 969849.40

Exp. Overall Performance

WithinOverall

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

Are there bad questions?

464136312621161161

1.0

0.8

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0.2

0.0

Sample

Pro

port

ion

_P=0.441

UCL=0.693

LCL=0.189

1

1

1

1

1

1

11

11

P Chart of Wrong

Does the order the exams are turned in effect the score?

3330272421181512963

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Index

Test

Sco

re

MAPE 15.9381MAD 0.0840MSD 0.0124

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot for Test ScoreLinear Trend Model

Yt = 0.5018 + 0.00317*t

TUESDAY RESULTS

Test Scores

Test for Normality

Is the process in Control?

28252219161310741

90.00%

80.00%

70.00%

60.00%

50.00%

40.00%

30.00%

20.00%

Observation

Indiv

idual V

alu

e

_X=55.93%

UCL=84.62%

LCL=27.25%

1

I Chart of Scores

Is the process capable?

Are there Bad Questions?

464136312621161161

0.9

0.8

0.7

0.6

0.5

0.4

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0.2

0.1

0.0

Sample

Pro

port

ion

_P=0.441

UCL=0.717

LCL=0.164

1

11

11

1

1

1

P Chart of Incorrect

272421181512963

0.9

0.8

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Index

Sco

res

MAPE 13.9747MAD 0.0779MSD 0.0100

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot for ScoresLinear Trend Model

Yt = 0.5614 - 0.000138*t

Does the order exams are turned in effect test scores?

COMBINED RESULTS

Combined Test Scores

0.840.720.600.480.36

20

15

10

5

0

Combined

Frequency

Mean 0.5591StDev 0.1099N 64

Histogram of CombinedNormal

Test Scores

0.84

0.72

0.60

0.48

0.36

9

8

7

6

5

4

3

2

1

0

84.0

0%

72.0

0%

60.0

0%

48.0

0%

36.0

0%

9

8

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3

2

1

0

Monday

Frequency

TuesdayMean 0.5589StDev 0.1177N 35

Monday

Mean 0.5593StDev 0.1018N 29

Tuesday

Histogram of Monday, TuesdayNormal

Is there a difference Between Classes?

0.9

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Monday Tuesday

Boxplot of Monday, Tuesday

Is there a statistical Difference?Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Monday 35 19.56 0.558857 0.013857Tuesday 29 16.22 0.55931 0.010357

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 3.26E-06 1 3.26E-06 0.000265 0.987056 3.995887Within Groups 0.76114 62 0.012276

Total 0.761144 63

Is the variation different?

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464136312621161161

1.0

0.8

0.6

0.4

0.2

0.0

Sample

Pro

port

ion

_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?

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

Brainstorming Techniques Continued

• Students then presented their results to the Group

Brainstorming Results

Cause and Effect (Fishbone)

Affinity Diagram

Brainstorming Results

Tree Diagram

Force Field Analysis

Brainstorming Results

Multi-Voting

Nominal Group Technique

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.

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

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

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)

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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

ProcessIdentify Educational

Shortcoming

Create Course

Develop Methodology

Locate Students

Teach Students

Administer Test

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

Control Charts Results

NP Chart

C Chart

Midterm Analysis

Midterm Exam Results

Pre Class Exam Results

Comparison

Does a T-Test Indicate there was improvement?

t-Test: Two-Sample Assuming Unequal Variances

Mid 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

Does ANOVA Indicate there was Improvement?

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Pre Total 64 35.78 0.559063 0.012082Mid Total 53 31.72 0.598491 0.013705

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.045069 1 0.045069 3.516685 0.06329 3.923599Within Groups 1.473823 115 0.012816

Total 1.518892 116

Change in Scores

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

Is the change in Scores Significant?

t-Test: Paired Two Sample for Means

Mid 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

Not all Material on the Exam has been Covered in Class

Midterm Comparison

Pre Test Comparison

Comparison of Results for Material that has been Covered

Mid CoveredPre Covered

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Subscripts

Covere

d S

core

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Boxplot of Covered Scores

Comparison of Covered Material

0.90.80.70.60.50.40.3

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0.90.80.70.60.50.40.3

Pre Covered

Frequency

Mid CoveredMean 0.5785StDev 0.1252N 64

Pre Covered

Mean 0.6516StDev 0.1174N 53

Mid Covered

Histogram of Pre Covered, Mid CoveredNormal

Does ANOVA Indicate there was improvement?

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Pre Covered 64 37.02632 0.578536 0.015686Mid Covered 53 34.53333 0.651572 0.013785

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.154648 1 0.154648 10.43065 0.001616 3.923599Within Groups 1.70503 115 0.014826

Total 1.859678 116

Comparison of Results for Material that has not been Covered

Mid Not CoveredPre Not Covered

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Subscripts

Sco

res

Boxplot of Scores

Comparison of Material Not Covered

Does ANOVA indicate the Exam was harder?

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Pre Not Covered 64 31.83333 0.497396 0.01785Mid Not Covered 53 27.5 0.518868 0.024926

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 0.013367 1 0.013367 0.635003 0.427168 3.923599Within Groups 2.420698 115 0.02105

Total 2.434065 116

Is the Exam Taking Process Capable?

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.

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

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

Was the Pre-Test a Predictor of the Mid Term Scores?

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.

Final Exam Analysis

Exam Scores

Doesn’t Look Normal

It’s Bi-Modal!

Did the scores Improve?

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

Individual Improvement

Variable N N* Mean StDev Minimum Q1 Median Q3Change 36 0 0.1939 0.1419 -0.0600 0.0675 0.2000 0.2875

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

Where there Hard Questions?

Pareto Chart on Topic

Count 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 93.3 100.0

Question Topic

FMEA

Contro

l Cha

rts

Confide

nce Inter

val

Team

s

Proc

ess C

apab

lityEr

ror

Hypo

thes

is

Basic

Stats

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80

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Perc

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Pareto Chart of Question Topic

Initial Process Capability

Final Process Capability

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

Was the Result Significant?

Rutgers ASQ

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

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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)

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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

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.

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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

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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.

Questions?

• Contact info– Brandon Theiss– Brandon.theiss@gmail.com– Connect to me on LinkedIn