ETD 515 Six Sigma and Applied Statistics: A New Course in...

ETD 515

Proceedings of the 2016 Conference for Industry and Education Collaboration

Copyright ©2016, American Society for Engineering Education

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.

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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

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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.

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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,

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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 Chapter Topic Lab

1 Lecture

notes

Six Sigma: the DMAIC process Software: Excel,

MATLAB, Minitab

2 1 Overview of probability, statistics, and

their application in electronics

engineering technology

Resistance measurement

3 2 Basic concepts in probability Six Sigma Project:

Define

4 3,4 Random variables, distributions Motor characteristics-

back emf gain

5 10 Exam 1, ANOVA Motor

resistance/inductance

measurement

6 7 Confidence intervals Motor torque gain and

friction measurement

7 8 Tests of hypotheses Statistical analysis of

motor

8 12 Linear regression, Gauge R&R Gauge R&R

9 16 Control charts Six Sigma Project:

Measure

10 Exam 2 Linear regression:

temperature sensor

11 Lecture

notes

Six Sigma tools Six Sigma Project:

Analyze

12 Lecture

notes


Improve

13 Lecture

notes


Improve

14 Lecture

notes

DOE, Monte Carlo, RSM Six Sigma Project:

Control

15 Review for final exam, Six Sigma Project

(presentation)

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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.

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.

0

2

4

6

8

10

-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11

Fre

qu

en

cy

Bin Width

Histogram

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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.

Figure 3 Circuit setting for inductance measurement

R1 L1

V1

R21 23

4

motor

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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 1-

4. 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.

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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 real-

world 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.

y = 7164.6e-0.048x

R² = 0.9993

0

2000

4000

6000

8000

10000

-10 -5 0 5 10 15 20 25 30

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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 before-

and-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

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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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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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