ETD 515
Six Sigma and Applied Statistics: A New Course in Electronic Systems Engineering
Technology
Wei Zhan
Electronic Systems Engineering Technology
Department of Engineering Technology & Industrial Distribution
Texas A&M University
Abstract
Six Sigma has been widely deployed in industry, service, government agencies, and other
sectors. Feedback from the industrial advisory board over the past few years indicated that
the curriculum needed to be enhanced through the use of statistics in engineering design and
analysis in the Electronic Systems Engineering Technology (ESET) program. ESET started
to develop a new course, Six Sigma and Applied Statistics, in 2012. The course materials and
laboratories went through several rounds of revisions. This paper discusses the detailed
contents for the lectures, laboratories, and course projects. A continuous education workshop
was offered based on the materials from this course. Six Sigma Greenbelts were offered to
the students in the course and the workshop participants. This course also opened doors for
collaborations with industry. Several guests from industry were invited to give guest lectures
for the class. Students also had a chance to work on real-world projects sponsored by
industry.
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
1. Introduction
Six Sigma is a structured, data-driven, quality control methodology that uses statistical tools for
process improvement16. It has been widely deployed in industry, government agencies, health
care, and other sectors as an engineering and management tool for process improvement10.
Extensive reviews on Six Sigma can be found in literature15, 23. Statistics is a critical component
in Six Sigma. As a part of the Six Sigma process, the use of statistics in engineering design,
testing, and troubleshooting is becoming increasingly critical for companies to stay competitive
in the global market.
The enhancement of the education on statistics has been discussed by many educators over the
last 30 years1, 2, 5, 8, 12, 13, 14, 20, 24. How to effectively teach engineering students statistics so that
they can make the connection between statistics and their engineering subject has been a major
research topic22.
Lean manufacturing is another important concept that is widely used in industry25. Since the
combination of Lean and Six Sigma proposed by M. George7 in 2003, there have been increasing
needs in many sectors to train the workforce with knowledge in Lean Six Sigma. The demand for
Lean Six Sigma training is so high that many high educational institutions started to consider
offering courses or introducing the subject in some quality related courses3, 6, 11, 19, 21, 29. These
curricular modifications are typically deployed to industrial engineering, manufacturing
engineering, or engineering technology departments6, 21.
The demands from industry are also driving other engineering technology (ET) programs to
educate their students in Lean Six Sigma. Incorporating statistics and Lean Six Sigma into ET
education has been a challenge for the ET community. There have been many attempts by
educators from different universities6, 9, 21. Many educators are interested in this educational
research topic, as indicated by the increase in number of publications in the ASEE annual
conferences in the relevant areas from 22 in 1998 to 174 in 200721. It is also reflected in the
readers’ interest in statistics: A paper published in the American Journal of Engineering
Education on teaching statistics to ET students has remained in the top of most frequently
downloaded papers list over the past six years28. Since the start of the effort to incorporate
applied statistics and Lean Six Sigma in to the curriculum, the feedback from the ESET
industrial advisory board on the enhancement of the ESET curriculum has been overwhelmingly
positive.
There are several successful implementations of Lean Six Sigma courses in curriculum of ET6, 9,
21
. For programs such as industrial engineering technology and manufacturing engineering
technology, the program has more flexibility to accommodate Lean Six Sigma in the curriculum.
Scachitti et al 21 presented their curriculum change effort to add Lean Six Sigma to industrial
engineering technology program at Purdue University Calumet, Indiana University Purdue
University Indianapolis, and Purdue University West Lafayette. They modified several courses
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
and added new courses to incorporate the Lean Six Sigma contents into the curriculum. This
required significant amount of effort from faculty. Through a $1.2M training grant with, faculty
worked with a local health care system and brought the real life project experience into the class
room to benefit their students. These success stories motived ESET faculty to move forward with
the curricular enhancement in the area of statistics and Lean Six Sigma.
However, it was found that there were many constraints to implement similar curriculum
changes in ESET. Quality control was not the focus of ESET, therefore, adding a new course to
the curriculum was a major change. In recent years, the state of Texas has asked public
universities to reduce the total credit hours required by B.S. degrees. ESET had 134 total
semester credit hours and needed to reduce to 128 semester credit hours. Adding a new course
was against this trend. Starting in 2007, the ESET program at TAMU experimented with several
different ways of teaching statistics and Six Sigma to ET students. First, Six Sigma was used in
course projects in an instrumentation course29, 30. More efforts followed to incorporate similar
contents into other courses within the ESET program28. These were done within existing courses
and the modifications to the curriculum were limited to two courses. There were no significant
revisions in the learning objectives in these courses. Therefore, no official course change
requests were needed. The individual faculty members implemented these modifications to their
courses. However, these efforts are far from sufficient, considering the importance of statistics
and Lean Six Sigma to ET students.
ESET students used to take a statistics course offered by Statistics Department. However, the
course was taught to all engineering students, therefore, it couldn’t address the unique needs of
ESET students. Engineering technology students have a unique learning style, they learn better
when the knowledge is applied in practical design and analysis. Therefore, laboratory and course
projects are critically important.
In the summer of 2012, ESET faculty had a retreat to discuss the program curriculum. There
were two major objectives, one was to reduce the total semester credit hours to 128, and the
other is to realign the courses to better reflect the need in today’s industry. It was decided at the
retreat that ESET should shift its focus to product development17. The need for teaching Lean Six
Sigma was brought up because it was a key component in product development. Base on the
findings in Zhan et al28, it was decided that the statistics course would be eliminated from the list
of ESET required courses and a new course, “Six Sigma and Applied Statistics,” would be
offered by ESET.
2. The course contents and structure
In 2002, ESET started to develop a new course ENTC 329, Six Sigma and Applied Statistics. It
was designed to be a required course of three semester credit hours for ESET students. The
course includes two lectures of 50 minutes each and a three hour laboratory sessions each week.
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
In order to decrease the total semester credit hours for the ESET program to 128, the course was
designed to cover the basics of statistics to eliminate the need for STAT 211. The syllabus of
STAT 211 was studied carefully before ENTC 329 was designed. The materials that were
determined to be relevant for ESET students were kept in the syllabus and other materials were
eliminated. More advanced topics that are needed for Six Sigma, but were not covered in STAT
211, were added to ENTC 329. It was found that some of these concepts were covered in a
follow-up course STAT212, which most ESET students would not take. The introduction of the
fundamental knowledge in statistics takes about half of a semester. The other half of the course
was designed for learning and applying Six Sigma. After the creation of the syllabus, it was
submitted to the Statistics Department for review. Based on the syllabus and presentation made
by ESET, the Statistics Department quickly approved the replacement of the STAT 211 by
ENTC 329 for ESET students.
Before the official creation of ENTC 329, a temporary course ENTC 489 was created for testing
the new course. After the successful delivery of the course for two semesters, ENTC 329 was
officially created as a required course for ESET program in 2013.
Other students from other programs, departments, or even colleges can take this course as a
technical elective course.
The prerequisite courses are: ENTC 210 (Circuit Analysis) and MATH 152 (Engineering
Mathematics II). The rationale for the prerequisites is: students need to have basic knowledge
about engineering math; the course was designed with laboratories that make connection
between the technical courses in ESET and applied statistics and Six Sigma. Due to the hands-on
learning style for engineering technology students, the course was designed with intensive
laboratories related to circuit analysis. This could cause problems for students from other
departments. Some students might not be familiar with laboratory equipment and others might
not have the lab kits that were required for all ESET students. It was not a big issue for students
from other engineering departments; however, it could potentially be a problem for students from
other schools such as business school. Fortunately, the number of students from outside ESET
enrolled in the course has been mostly limited. The laboratory issues have been mitigated by
pairing ESET students and non-ESET students.
The course Objectives are:





To learn the basic concepts of probability and statistics;
To be able to apply these basic concepts to design and analyze electronic circuits;
To be able to use statistical tools in troubleshooting electronic circuits;
To be able to use Six Sigma DMAIC process and tools in design and analysis of
electronic circuits;
To be able to use software to conduct statistical calculations.
The Main Topics covered in the course are:
 Samples, set theory, random variables, probability space, probability, mean, variance,
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
error, accuracy, precision.
 Gaussian, uniform, Student, Weibull distributions, confidence level, Central Limit
Theorem, estimation of mean, sample size, hypothesis testing.
 Six Sigma process (DMAIC) and tools including Gauge R&R, test of hypotheses,
analysis of variance, linear regression, response surface method, Monte Carlo analysis,
control charts, design of experiments (DOE), and robustness analysis and design.
The course schedule is as follows:
Week
1
2
Chapter
Lecture
notes
1
3
2
4
3,4
Random variables, distributions
5
10
Exam 1, ANOVA
6
7
Confidence intervals
7
8
Tests of hypotheses
8
9
12
16
Linear regression, Gauge R&R
Control charts
10
Topic
Six Sigma: the DMAIC process
Overview of probability, statistics, and
their application in electronics
engineering technology
Basic concepts in probability
Exam 2
Lab
Software: Excel,
MATLAB, Minitab
Resistance measurement
Six Sigma Project:
Define
Motor characteristicsback emf gain
Motor
resistance/inductance
measurement
Motor torque gain and
friction measurement
Statistical analysis of
motor
Gauge R&R
Six Sigma Project:
Measure
Linear regression:
temperature sensor
Six Sigma Project:
Analyze
11
Lecture
notes
Six Sigma tools
12
Lecture
notes
Six Sigma tools
Six Sigma Project:
Improve
13
Lecture
notes
Six Sigma tools
Six Sigma Project:
Improve
14
Lecture
notes
DOE, Monte Carlo, RSM
Six Sigma Project:
Control
15
Review for final exam, Six Sigma Project
(presentation)
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
One of the problems faced in the development of this course was finding an appropriate
textbook. While many references were available, as pointed out by Gore9, not a single textbook
would fit the needs of this course. It was also not financially practical for students to buy several
reference books. For the first two years, a textbook by Devore “Probability & Statistics for
Engineering and the Sciences”4 was adopted as the official required textbook. It was used for
about half a semester. The rest of the materials related to Six Sigma were provided to the
students in the form of lecture notes. The author is currently working on a textbook together with
an expert in Six Sigma from industry27.
Laboratory design
Laboratory is the focal point of this course. Students spend more time working in the laboratory
than attending the lectures. The experiential learning style works best for ET students. There
were seven laboratories to allow students to learn to apply statistics in engineering tasks. The rest
of the laboratory time was devoted to a course project, which will be discussed later in the next
section.
Lab 1: MATLAB and Excel Tutorial – Statistical Tools
In this lab, students learn to use MATLAB and Excel to conduct basic statistical analysis, such as
calculating the mean, standard deviation, generating random numbers, and plotting histograms
and stem-and-leaf graphs.
Histogram
10
Frequency
8
6
4
2
0
-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11
Bin Width
Figure 1 Histogram generated in Excel
Lab 2: Measurements
In this lab, students learn to take multiple measurements of resistance values. They use what they
learned in Lab 1 to conduct some basic statistical analysis. The data they collected will be used
in a later lab (Lab 6) for Gag R&R analysis.
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
Lab 3: Motor Characteristics – Back EMF Gain
This lab allows students to combine what they learned in circuit analysis and statistics to
measure the back emf gain for a DC permanent magnetic motor. Test data are collected using
LabVIEW software. Collected data will be used to do regression in MATLAB to estimate values
for multiple parameters.
Figure 2 LabVIEW data collection
Lab 4: Motor Characteristics Measurements
This lab allows students to combine what they learned in circuit analysis and statistics to
measure the inductance, the inertia, and friction torque for a DC permanent magnetic motor.
motor
3
R1
1
R2
2
L1
V1
4
Figure 3 Circuit setting for inductance measurement
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Copyright ©2016, American Society for Engineering Education
ETD 515
Lab 5: Statistical analysis of Motor: MATLAB Simulation
In this lab, students use the motor parameters they measured in labs 1-4 to build a motor model
in MATLAB. Motor parameters were randomly generated according to the test data from Lab 14. They then conduct basic statistical analysis on the motor using simulation tools.
Figure 4 MATLAB simulation
Lab 6: GR&R
This lab introduces concepts of and relating to ANOVA Gauge R&R. Students will understand
the following concepts: Reproducibility, Part-to-Part Variation, and Repeatability. Using the
resistor values measured earlier, students will get to see the variance associated with their
measurement system.
Figure 5 Gage R&R analysis
Lab 7: Regression for temperature sensor
In this lab, students learn to use the regression method to characterize a temperature sensor.
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
10000
8000
6000
y = 7164.6e-0.048x
R² = 0.9993
4000
2000
0
-10
-5
0
5
10
15
20
25
30
Figure 6 Temperature sensor characterization using regression
The course materials and laboratories went through several rounds of revisions and are being
revised and enhanced constantly.
3. Course projects
Students spend 7-8 weeks to work on their greenbelt projects. Upon completing the projects and
successfully passing the course, a Six Sigma greenbelt certificate is awarded to each student.
Teams consist of 4-6 students with the students making their own decision on team formation.
Different types of projects were used:
 projects from industry
 projects found by students
 projects assigned to students by the instructor.
Each type has its advantage and disadvantage. Projects from industry are preferred by most
students; however, they are not always easy to find. Project scope and timing for these projects
could cause problems. Students may not have the required knowledge to work on these realworld projects. Students can look for projects that they would like to work on; however, they
may not be able to find appropriate projects. Projects assigned by the instructor save students
time looking for projects, but the instructor may run out of projects to give to students. Repeating
old projects did not work well because students talked to others who took the course before and
found the results ahead of time. Finding appropriate projects has been one of the biggest
challenges faced in this course. Another challenge was the before-and-after analysis. Often, there
was not enough time to do this within one semester.
Students worked on their project during the lab time and sometimes outside of the lab time. They
follow the DMAIC process to complete their projects. At the end of the semester, a presentation
was given by each team. A final report was required. The presentations were graded by students
and each member of the winning team receives an ESET mug with the Lean Six Sigma web
address printed on it.
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
Sample projects include:




ESET course and lab scheduling
This project was assigned to the students by the instructor. The ESET program
coordinator complained about the time-consuming task of course and lab scheduling
every semester and lack of suitable low-cost software. Several student teams worked on
this project. One team identified Google Calendar as the free software that has most of
the needed features for scheduling. Another team wrote their own software using C#.
Cycle time reduction for a PCB heating process
This project was based on a research project sponsored by industry. It required some
knowledge in thermal dynamics, which most ESET students did not have. It was a good
opportunity for students to work on a multi-disciplinary project. However, students were
lost in the beginning.
Krisys Robot Kit: An outreach effort
This was one of the first Six Sigma projects in ENTC 329. Student teams tried to reduce
the cost and cycle time for making Krisys Robot Kits, which was a product that ESET
was trying to develop as an effort for both outreach to recruit high school students and for
student workers to gain experience in product development. Two teams worked on this
project and the results were extraordinary. This project has become a case study in the
lecture notes. The feedback from students learning Six Sigma was very positive.
Burger Service Improvement
This project was identified by a student team. They studied the service at a popular local
fast food restaurant and used Lean Six Sigma principle to come up with improvement
ideas. The implementation was a problem, because the restaurant owner was not ready to
implement the recommended changed made by the team in time for them to do a beforeand-after comparison.
Currently, discussions are being held with several industrial sponsors to secure Six Sigma
student projects. This can provide the sponsor with ideas of solving some of their problems. In
addition, it can be used as a recruiting method to attract students to work for the sponsor either as
summer interns or as permanent employees upon graduation.
4. Positive feedback and continuing education workshop
Several students from other departments and colleges took this course (including graduate
students). Most of them were interested in the Greenbelt in Six Sigma. Some wanted to apply Six
Sigma to their research. After an article introducing ENTC 329 was published in Texas A&M
Engineering Weekly Newsletter, many people enquired about the possibility of taking the course
to get a Six Sigma Greenbelt certificate.
Positive feedback also came from the Career Center at Texas A&M University. One of the hiring
managers complained about lack of Six Sigma courses offered in the College of Engineering. A
Proceedings of the 2016 Conference for Industry and Education Collaboration
Copyright ©2016, American Society for Engineering Education
ETD 515
quick search by the Career Center director found ENTC 329 and directed the hiring manager to
ESET. The manager was later invited to give a guest lecture in ENTC 329. Two students said
they were asked during job interviews about the Six Sigma course and greenbelt certificate. One
student was hired as a quality engineer.
Based on the success of ENC 329, the positive feedback received from students and industry,
and success by other universities9, ESET made the decision to expand the effort to offer a
continuing education workshop. Upon successful completion of the workshop, Six Sigma
Greenbelts were given to the participants18. The first Lean Six Sigma Workshop was offered in
the summer of 2014.
5. Conclusions and Future Work
The new course ENTC 329, Six Sigma and Applied Statistics, offered by ESET has gone
through several round of revision and is creating educational opportunities for ESET students
and other students at Texas A&M University. A continuing education workshop was created
based on the course material. A new textbook is being written specifically for this course. The
initial feedback from industry and students were overwhelmingly positive. Future work includes
offering of workshops for Six Sigma Yellowbelt and Blackbelt trainings18, 26. Different types of
Six Sigma projects will be explored, with the focus on developing closer relationships with
industry by having companies sponsor real-world course projects. Data will be collected for
continuous improvement of these efforts for analysis and further improvement. These results will
be presented in ASEE conferences or in a journal paper for dissemination.
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Proceedings of the 2016 Conference for Industry and Education Collaboration
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AUTHOR’S BIOGRAPHY
Wei Zhan is an Associate Professor of the Electronic Systems Engineering Technology program at Texas A&M
University. He earned his D.Sc. in Systems Science from Washington University in St. Louis in 1991. From 1991 to
1995, he worked at University of California, San Diego and Wayne State University. From 1995 to 2006, he worked
in the automotive industry as a system engineer. In 2006, he joined the Electronic Systems Engineering Technology
faculty at Texas A&M University. His research activities include control system theory and applications to industry,
systems engineering, robust design, Six Sigma, modeling, simulation, and optimization.
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Copyright ©2016, American Society for Engineering Education