1. 元
智
大
學
工業工程與管理研究所
碩士論文
An Enhanced Process Improvement Roadmap in Six Sigma
Methodology
Student：Mungunshagai Enkhbold
Advisor：Dr. Chi-Kuang Chen
中 華 民 國 101年 06月

2. An Enhanced Process Improvement Roadmap in Six Sigma
Methodology
研 究 生：艾明佳
指導教授：陳啟光
Student: Mungunshagai Enkhbol
博士
Advisor: Dr. Chi-Kuang Chen
元 智 大 學
工業工程與管理研究所
碩士論文
A Thesis
Submitted to Institute of Industrial Engineering and Management
Yuan-Ze University
in Partial Fulfillment of the Requirements
for the Degree of
Master
in
The Department of Industrial Engineering and Management
Chung-Li, Taiwan, Republic of China
June 2012
i

3. Acknowledgement
There are a number of people who I would like to thank for their support during the
course of this research. However, before I acknowledge them I would like to express
my profound gratitude to my family. Your support, encouragement, help and love
during the course of the past two years made it possible for this work to materialize.
Special thanks to Dr. Chi-Kuang Chen, Industrial Engineering Department of
Yuan Ze University, without whom this research could not have been completed.
Many thanks for his selfless commitment to guiding me and motivating me. Thanks
to Mr. Cheng-Ho Tsai for your valuable comments and corrections and for being so
helpful all the time. I want to thanks also to my graduate committee member
Professor Henyi Jen for the advices and suggestions.
To International Cooperation and Development Fund (ICDF) and Yuan Ze
University (YZU) for giving me the opportunity to study in Taiwan and for provide
me a professional education, and to my professors in YZU, for sharing their
knowledge and experiences.
Thanks to all the people that directly or indirectly helped me to finish my thesis,
thanks to all my friends around the world that gave moments of happiness and good
memories. Special thanks to my friend Michael Smith for being with me all the time,
for your support and help.
Thanks to USIP2, where I worked before I come to Taiwan, director L.
Badamkhorloo and my lovely colleagues for built up the person who I am today. All
my knowledge based on your help and support and without you I cannot be reach to
this point.
ii

4. An Enhanced Process Improvement Roadmap in Six Sigma
Methodology
Student: Mungunshagai Enkhbold
Advisor: Dr. Chi-Kuang Chen
Department of Industrial Engineering and Management
Yuan-Ze University
Abstract
There are numerous different approaches available nowadays to improve the
performance of a process and ensure on time delivery. The Six Sigma offers a
unique roadmap that is widely used in industries in order to improve the process and
thereby reduce the number of defects. The most commonly used roadmap for
existing
process
improvement
in
Six
Sigma
is
the
DMAIC
(Define-Measure-Analyze-Improve-Control) improvement roadmap which is a
five-step roadmap that utilizes different Six Sigma tools to generate ideas, collect
and measure data, analyze and come up with improvement plans to improve the
process under study. While analyzing the DMAIC roadmap and its application to the
case studies, some deficiencies were found. Various case studies application of the
DMAIC roadmap illustrated issues in reaching the Six Sigma goal (Cpk=2). In order
to solve the deficiencies of the original DMAIC roadmap, the present study seeks to
enhance the original improvement roadmap by some statistical tools with emphases
on the process capability (Cpk) to better insure improvement. Thus, this study is
proposing an enhanced improvement roadmap that seeks to achieve the required Six
Sigma goal. A case study is conducted to demonstrate the feasibility and
effectiveness of the proposed improvement roadmap. The result of this study will
be a proposal of the enhanced improvement roadmap in Six Sigma methodology,
along with the benefits deliverable from the application of the methodology.
Keywords: Six-Sigma, DMAIC, Design of Experiment (DOE), Voice of the
Process (VOP), Process Capability (Cp and Cpk), Enhanced Process Improvement
Roadmap
iii

5. Table of Contents
Acknowledgement ............................................................................................. ii
Abstract……………. ....................................................................................... iii
Table of Contents ..............................................................................................iv
List of Figures....................................................................................................vi
List of Tables………. ...................................................................................... vii
Chapter 1 Introduction ..................................................................................... 1
1.1 Research Background .................................................................................1
1.2 Research Motivation ...................................................................................3
1.3 Research Objective ..................................................................................... 3
1.4 Organization of the Study ...........................................................................4
Chapter 2 Improvement Roadmap of the Six Sigma Methodology ..............5
2.1 What is Six Sigma? ..................................................................................... 5
2.2 History of Six Sigma...................................................................................5
2.3 Definition of Six Sigma ..............................................................................6
2.4 Six Sigma Tools and Techniques ................................................................ 8
2.5 The DMAIC Improvement Roadmap ......................................................... 9
2.5.1 Define Phase ..............................................................................10
2.5.2 Measure Phase ........................................................................... 11
2.5.3 Analyze Phase ............................................................................12
2.5.4 Improve Phase ...........................................................................13
2.5.5 Control Phase .............................................................................13
2.6 DMADV Roadmap ................................................................................... 14
2.7 Summary ...................................................................................................15
Chapter 3 Development of an Enhanced Improvement Roadmap in Six
Sigma Methodology .................................................................16
3.1 Development of the Enhanced DMAIC Improvement Roadmap .............16
3.1.1 Voices of the Customer and Process Relationship Information
Measurement Enhancement ....................................................... 17
3.1.2 Define and Measure the Initial VOC, VOP and Identify CTS...19
3.1.3 Enhancement of DOE for Process Capability Analysis and
Improvement ..............................................................................21
3.1.4 Development of the Process Capability via DOE...................... 22
3.2 Argumentation to the Enhancements of the Improvement Roadmap .......27
3.2.1 Enhancement Focus ...................................................................27
3.2.2 Argument Responds to the Enhancements.................................27
iv

6. 3.3 Summary ...................................................................................................29
Chapter 4 Case Study ...................................................................................... 31
4.1 Case Description ....................................................................................... 31
4.2 Application of the original Improvement Roadmap .................................32
4.2.1 Definition and Measurement of the Current Process .................32
4.2.2 Analysis and Improvement of the DMAIC Application ............33
4.3 Application of the Enhanced Improvement Roadmap .............................. 34
4.3.1 Define and measure the VOC and VOP ....................................35
4.3.2Analysis and Improvement via DOE ..........................................36
4.4 The Second Analysis and Improvement of the Payroll Process ...............39
4.5 Summary ...................................................................................................42
Chapter 5 Conclusions and Suggestion .......................................................... 44
5.1 Conclusions ............................................................................................... 44
5.2 Suggestion .................................................................................................45
Reference…….. ................................................................................................ 47
Appendix A –Full Factorial Design of Experiment for Payroll Process
with Enhanced Improvement Roadmap ................................ 51
Appendix B –Full Factorial Design of Experiment for Payroll Process, the
Second Analysis and Improvement ........................................53
v

7. List of Figures
1Figure 2.1: Sigma variation shown in normal curve (Itil &ITSM World, 2003) ....... 7
2Figure 2.2: A process tends to shift 1.5 sigma units (Arnheiter, 2005) ...................... 7
3Figure 2.3 SIPOC diagram ....................................................................................... 11
4Figure 2.4 Possible source of variation (Kaushik and Khanduja, 2008) .................. 12
5Figure 3.1 Comparing the VOP vs. the VOC (York, 2009) ...................................... 20
6Figure 3.2 VOP matrix template (Furterer, 2004) .................................................... 20
7Figure 3.3 Pareto Chart............................................................................................. 21
8Figure 3.4 Sigma to DPMO-conversion, assuming 1.5 sigma shift ......................... 23
9Figure 3.5 DPMO representing a Six Sigma quality level, allowing 1.5 sigma
shift average............................................................................................. 23
10Figure 3.6 DOE identification of the variation factors ............................................. 24
11Figure 3.7 DOE establishment of the performance baseline .................................... 25
12Figure 3.8 DOE process capability analysis and exposed defected variations for
improvement ............................................................................................ 25
13Figure 3.9 DOE optimized process variations and improved performance baseline 26
14Figure 3.10 DOE monitoring and verification procedure for optimized variations . 26
15Figure 4.1 SIPOC diagram........................................................................................ 33
16Figure 4.2 Cause and effect diagram ........................................................................ 33
17Figure 4.3 Histogram of payroll process before (left) and after (right)
improvement ............................................................................................ 34
18Figure 4.4 Pareto chart for information system problems ........................................ 37
19Figure 4.5 DOE graphical analysis of the payroll process ....................................... 37
20Figure 4.6 DOE of the payroll process ..................................................................... 38
21Figure 4.7 Improved process capability with enhanced improvement roadmap ...... 39
22Figure 4.8 DOE reanalysis for the payroll process ................................................... 41
23Figure 4.9 DOE analysis of variation ....................................................................... 41
24Figure 4.10 Improved process capability of payroll process with the enhanced
improvement roadmap ............................................................................. 42
vi

8. List of Tables
1Table 2: The DMAIC roadmap and the steps included in each phase ...................... 10
2Table 3: An enhanced DMAIC improvement roadmap and the tasks ...................... 18
3Table 4.1 Estimated processing time and summary of process capability of each
processes .................................................................................................... 32
4Table 4.2 Raw data of the payroll process ................................................................ 32
5Table 4.3 Summary of the improved process capability and process time .............. 34
6Table 4.4 Payroll process data collection plan ......................................................... 35
8Table 4.5 VOP Matrix for payroll process................................................................ 36
9Table 4.6 Employee VOC survey results summary for reanalysis ........................... 39
10Table 4.7 Revised VOP matrix ................................................................................. 40
11Table 5: Payroll process improvements result .......................................................... 45
12Table A2: Data for the Second full factorial DOE .................................................... 54
vii

9. Chapter 1 Introduction
1.1 Research Background
In the modern world of manufacturing, due to massive competition, different
companies have started to look for different approaches and practices to improve the
quality level of the product at a reduced cost, create a safe and rewarding workplace,
and eventually achieve higher customer satisfaction. Most organizations strive for an
improved level of process capability and manufacturing quality to achieve the
bottom-line objectives of generating a profitable margin and sustainable
competiveness and share in the market. Six-Sigma is a quality improvement strategy
that helps companies to achieve these results.
According to Harry CEO of Six Sigma Academy Phoenix, USA: Six Sigma is a
well-structured, disciplined, data driven methodology for eliminating defects, waste,
or quality control problems of all kinds of manufacturing, service deliver,
management and other business activities; and it is the business strategy that allows
companies to drastically improve their performance by designing and monitoring
everyday business activities in ways that minimize waste and resources while
increasing customer satisfaction. O’Neal and Duvall (2004) stated that Six Sigma is
a disciplined quality improvement methodology that focuses on moving every
process that touches the customers –every product service –towards near perfect
quality. Hence, Six Sigma is the measure of the company’s quality. Maleyeff and
Karyenvenger (2004) noted that Six Sigma implies three things: statistical
measurement, management strategy and quality culture. It is a measure of how well
a process is performing through statistical measuring of quality level. It is a new
management strategy under the leadership of the top management that creates
quality innovation and total customer satisfaction. Moreover, Six Sigma is also a
quality culture. It provides the way to do things right the first time and to work
smarter by using data information. It also provides an atmosphere to solve many
CTS (critical-to-satisfaction) problems through team efforts.
Pande, Neuman and Roland (2000) mentioned that Six-Sigma is an
improvement methodology, developed by Motorola in the 1980’s, whose benefits
and financial results are well documented in many areas. Six-Sigma is a way for
Motorola to express its quality goal of 3.4 Defects per Million Opportunity (DPMO)
where a defect opportunity is a process failure that is critical to the customer.
Motorola set this goal so that process variability is +/-6 Standard Deviation (SD)
from the mean. They further assumed that the process was subject to disturbances
that could cause the Process Mean to shift by as much as 1.5 SD. Motorola
1

10. developed “The Six Steps to Six Sigma’’ process improvement roadmap to achieve
six sigma quality (3.4 DPMO). More specifically, this improvement roadmap was
focusing on the improvement of the process capability of the process. Dahlgaard
(2006) noted that due to the sixth step of the Motorola’s improvement roadmap “if
the process capability (Cp, Cpk) is less than two then redesign materials, product,
and process as required. It is clear that the Six Sigma requires higher process
capability during its application. With these stated requirements in Six Sigma, the
process must be capable through successful implementation. Thus, Six-Sigma
requires that the Process Capability (Cp, Cpk) have to be greater or equal to two,
Cpk≥2, during the Six Sigma implementation and continuous control of the process.
Later, Motorola’s six steps to six sigma roadmap replaced by GE as a 5 phases of
DMAIC (Define, Measure, Analyze, Improve, and Control) improvement roadmap.
The DMAIC improvement roadmap is the most commonly used roadmap in Six
Sigma after all. It is a Six Sigma roadmap for improvement of an existing process.
While analyzing the DMAIC improvement roadmap and its application to case
studies, some deficiencies were found. Seeing from some research works and case
studies, the DMAIC is not fully assured to achieve Six Sigma requirement (Cpk≥2).
Antony, Kumar and Tiwari (2005)’s research work, “An application of Six Sigma
methodology to reduce the engine-overheating problem in an automotive company”,
that adopted DMAIC roadmap for the improvement of the processes where these
adaptations did not reach the Six Sigma requirement of Cpk≥2. In order to solve the
deficiencies of the original DMAIC roadmap, the present study seeks to enhance the
original improvement roadmap by significant statistical tools with emphases on the
process capability to better insure improvement. Thus, this study is proposing an
enhanced improvement roadmap that seeks to achieve the required Six Sigma goal
(Cpk≥2). The process capability enhancements achievement will bring about
successful improvements for the process and product quality with the successful
application of the Six Sigma project.
This thesis study will provide insightful results and examinations of the
methodology centering on its implementation and application to the case study. The
main portion of this work will be dedicated to enhance the improvement roadmap in
Six Sigma methodology. Based on the a variety of literature and case study review,
an enhanced improvement roadmap in Six-Sigma methodology will be developed
and implemented at the facility. The result of this study will be a proposal of the
enhanced improvement roadmap in Six Sigma methodology, along with the benefits
derivable from the application of the methodology.
2

11. 1.2 Research Motivation
Woo and Hong (2007) noted that to satisfy a growing demand and expectation from
customers while coping with increasing product complexity and limited resources,
companies must improve in a continuous basis. As products become more complex,
the number of components at the sub-assembly level becomes increasingly large,
leading to a higher probability of defective assembly, as a result, there is a drive for
superior component quality. Moreover, processes need to have a greater capacity and
efficiency to provide a greater throughput to meet customer requirement. Continuous
improvement tools and techniques are introduced to address these issues, allowing
the manufacturing of superior quality products with efficient processes. The Six
Sigma methodology is one of them. Since Six Sigma’s introduction, the
methodology has become widely popular among many industries. Despite its
popularity and success rate in numerous cases, there are misconceptions about the
methodology that fosters some companies’ reluctance in accepting and adapting it.
Furthermore, because of the allocation of substantial resources and time required for
implementation, and the risk of interrupting regular business operations, some
companies are hesitant to implement the methodology. Therefore, with better
understanding and more insightful research of Six Sigma these misconceptions can
be dismissed.
The motivation for this research topic is aid in the intuitive knowledge of Six
Sigma in the business environment. The major benefits of Six Sigma to the business
environment are having a measureable way to track performance improvement,
focusing the attention on process management at all organizational levels, improving
customer relationships by addressing defects, improving efficiency and effectiveness
of process by aligning them with the customer’s need. Moreover, the DMAIC
improvement roadmap includes five phases of improving any existing
process –Define, Measure, Analyze, Improve, and Control. These phases are
virtually the same in any company that has adopted what is now known as Six
Sigma. The individual steps within each phase may vary slightly from one
company’s implementation to another; such variance is usually minor and almost
inconsequential. It is very important that five phases be consistently followed to
achieve anticipated results and keep the benefit at the appropriate level for the
company.
1.3 Research Objective
This study seeks to provide insightful research, and enhance the DMAIC
improvement roadmap for centering on its implementation and application to better
process capability requirement. Basically, this study intends to achieve the following
3

12. objectives: 1) To review and summarize the Six-Sigma’s roadmaps from the past
literature and understand the original roadmap; 2) To develop an enhanced
improvement roadmap in Six Sigma methodology; and 3) To conduct a case study to
demonstrate the feasibility of the proposed roadmap.
1.4 Organization of the Study
This report will begin with the literature review section. Some background
knowledge about Six-Sigma and its roadmap will be given, along with a description
of some of the tools and techniques. Attention will then be directed towards the
improvement roadmap. An enhancement of the improvement roadmap would be the
main focus of the thesis. Following the introduction is the implementation of
proposed roadmap for the case, describing in detail the work that has been done in
every stage of the DMAIC approach in the implementation of Six Sigma.
Application of the proposed roadmap will be executed on the case study. A list of the
research materials and some of the referenced graphs/tables will be given in
Appendix at the end of the report.
4

13. Chapter 2 Improvement Roadmap of the Six Sigma
Methodology
2.1 What is Six Sigma?
Sigma is a letter in the Greek alphabet that has become the statistical symbol, which
is used in mathematics and statistics to define standard deviation. The sigma scale of
measurement is perfectly correlated to such characteristics as defects-per-unit and
the probability of a failure. Six is the number of sigma measured in a process, when
the variation around the target is such that only 3.4 outputs out of one million are
defects. Coronado and Antony (2002) pointed that Six-Sigma methodology have
recently gained wide popularity because it has proved to be successful not only at
improving quality but also at producing large cost savings along with those
improvements. So, an organization needs to give smarter Six Sigma solutions that
linked to bottom line benefits. Kumar (2002) has stated that Six Sigma is statistical
measurement, which provides that opportunity and discipline to eliminate mistakes,
improve morale, and save money. Doing things rightly and keeping them consistent
are the basic ideas behind Six Sigma. A fundamental objective of Six Sigma is to
achieve customer satisfaction with continuous improvement in process.
2.2 History of Six Sigma
The roots of Six Sigma as a measurement standard can be tracked back to Carl
Frederick Gauss (1777-1855) who introduced the concept of the normal curve.
Racing (2005) noted that the Six-Sigma as a measurement standard in product
variation can be traced back to the 1920’s when Walter Shewhart showed that three
sigma from the mean is the point where a process requires correction. Many
measurement standards (Cpk, Zero Defects, etc.) later came in the scene but credit
for coining the term “Six-Sigma” goes to Motorola engineer named Bill Smith.
According to George (1992), in the early and mid-1980s with Chairman Bob
Galvin at the helm, Motorola engineers decided that the traditional quality
levels—measuring defects in thousands of opportunities—didn’t provide enough
granularity. Instead, they wanted to measure the defects per million opportunities.
Motorola developed this new standard and created the methodology and needed
cultural change associated with it. Six-Sigma helped Motorola realize powerful
bottom-line results in their organization- in fact; they documented more than $16
Billion in savings as a result of Six-Sigma efforts. In the period 1983-1989,
Motorola developed “the six steps six sigma” process improvement methodology
5

14. which helped Motorola to save billions of dollars.
Since then, hundreds of companies around the world have adopted Six Sigma
as a way of doing business. Dahlgaard (2006) noted that this is a direct result of
many of America’s leaders openly praising the benefits of Six-Sigma, such as Larry
Bossidly of Allied Signal (now Honeywell), and Jack Welch General Electric
Company. The Motorola “six steps top six sigma” were replaced by GE when Jack
Welch, chairman and CEO of GE, declared the six sigma process to be GE’s
corporate strategy for improving quality and competitiveness. As noted Park (2003),
the change of roadmap directly from the extract from his speech:
Motorola has defined a rigorous and proven process for improving each of
the tens of millions of processes that produce the goods and services a company
provides. The methodology is called the six sigma process and involves four simple
but rigorous steps (MAIC): First, measuring every process and transaction; then
analyzing each of them; then painstaking improving them; and finally rigorously
controlling then for consistency once they have been improved. Dahlgaard (2006)
mentioned that by comparing these four simple but rigorous steps with Motorola’s
six steps to six sigma quality it seems on the surface as if GE (or Jack Welch) in
beginning of their six sigma journey focused only on 6 Step Motorola’s Roadmap.
Park (2003)stated pointed that later on that the six sigma improvement process
usually followed the so-called DMAIC process, which defined as follows:
Define–Identification of the process or product that needs improvement;
Measure–Identify those characteristics of the product or process that are critical to
the customer’s requirement for quality performance and which contribute to
customer satisfaction; Analyze–Evaluate the current operation of the process to
determine the potential sources of variation for critical performance parameters;
Improve–Select those product or process characteristics which must be improved to
achieve the goal and implement improvement; and Control–Ensure that the new
process conditions are documented and monitored via statistical process control
methods.
Six-Sigma has evolved over time. It’s more than just a quality system like
TQM or ISO. It’s a way of doing business. As Tennant (2001) describes in his book
Six-Sigma: SPC and TQM in Manufacturing and Services: Six-Sigma is many
things, and it would perhaps be easier to list all the things that Six-Sigma Quality is
not. Six-Sigma can be seen as a vision; a philosophy; a symbol; a metric; a goal; and
a methodology.
2.3 Definition of Six Sigma
Statistically, the term sigma represents the standard deviation, the variation around
6

15. the process mean the objective of Six-Sigma is to achieve a quality of the at most
3.4 defect per million opportunities (DPMO) and the process capability is more than
or at least 2 (Cpk≥2). Six Sigma means that there are 6 standard deviations from the
process mean to the specification limits when normally distributed process is
centered (See Figure 2.1).
1Figure 2.1: Sigma variation shown in normal curve (Itil &ITSM World, 2003)
In the original definition of Six-Sigma, it was assumed that process could shift 1.5
sigma’s without detection. Therefore, a 1.5-sigma drift margin was built into the
standard definition of Six-Sigma. If a Six-Sigma process shifts 1.5 units from the
process mean to either side, the final products would be 99.97% detect free, having
3.4DPMO (See Figure 2.2).
2Figure 2.2: A process tends to shift 1.5 sigma units (Arnheiter, 2005)
However, over the past few years, Six-Sigma has evolved to be more than a simple
statistical definition. Arnheiter and Maleyeff (2005) noted that although the
Six-Sigma metric of reducing defects to only a few parts per million for a processes
still applies, Six-Sigma has become a complex quality improvement philosophy and
approach. It is an overall-term decision-making business strategy, incorporating a
quality management philosophy as well as a systematic methodology that aims to
measure defects, reduce variation and improve the quality of products, processes and
services.
According to Antony and Banuelas (2002), the Six-Sigma strategy originates
from two sources: total quality management (TQM) and the Six-Sigma metric
mentioned above, invented by Motorola Corporation in the mid-1980s. TQM
7

16. distributes the responsibility of quality management to everyone in an organization.
In other words, everyone, not only the quality control personnel, contributes to the
quality of goods and services. Also, TQM places emphasis on focus on customer
satisfaction and significant training in statistics and roots cause analysis methods
needed for problem solving. These problem solving methods are performed by
employing the “magnificent Seven” tools of quality: Control charts, histograms,
check sheets, scatter plots, cause-and-effect diagrams, flowcharts and Pareto charts.
These concepts and tools are adopted by the Six-Sigma strategy.
2.4 Six Sigma Tools and Techniques
Woo and Hong (2007) illustrated that the Six Sigma methodology integrates
statistical process control (SPC) tools and techniques, including the “Magnificent
Seven”, to solve problems and achieve continuous quality improvement in a
disciplined fashion. These tools are employed in various stages of the DMAIC
roadmap. The objectives of employing SPC tools are to bring the process in control
and to reduce variations due to special causes. SPC tools are widely used by industry
for the problem solving. The “Magnificent seven” is on-line processing monitoring
tool while the off-line techniques are Regression Analysis, Hypothesis Testing, and
Analysis of Variance (ANOVA) in DMAIC Roadmap.
Run Chart (Check Sheet): A run chart keeps track of process measurements
over time. It is used for a rough check of the process stability, and it is particularly
useful in identifying changes in the process mean and standard deviation. When
looking at runs charts, one pays attention to huge jumps in measurements, patterns
that occur over time (e.g. whether the measurement show an increasing trend), and
an increase in variance. A check sheet is similar to run chart, but it is used to keep
record of equipment over time.
Histogram: A histogram is a graphical display of measurement frequencies. It is
used to identify the shape and location of the distribution of measurement, but the
process must be in control for the identification of distribution to be accurate. A
histogram shows the proportion of measurements that fall into each bin. The number
and range of bins are determined by the constructor for the histogram. The mean and
variability of the process can be easily seen on the histogram. If the specification
limits are shown, the histogram can display the process capability.
Pareto Diagram: A Pareto diagram is similar to a histogram, but the bins show
attribute data instead of measurement ranges. Also, the values plotted are arranged in
descending order. This is due to Pareto’s Principle, which states that a small number
of causes contribute to the majority of problem.
Cause and Effect diagram: A cause and effect Diagram is used to identify and
8

17. analyze a problem in team setting. Teams brainstorm to generate categories such as
materials, machines, personnel, environment, etc. Within each category, the team
identifies causes that contribute to the effect (the problem). Cause and effect
diagram visually displays these causes, and help the team to locate the most
significant causes that lead to the problem.
Scatter Diagram: A scatter diagram is used to investigate the relation between
the two quality characteristics on the x and y axes, e.g. whether x values increase as
y values increase. However, note that correlation does not imply causality, e.g. one
cannot conclude that an increase an x causes and increase in y, even if x values
increase as y values increase.
Control Chart: The control chart is similar to a run chart, but it plots
measurements over time on a chart with control limits. The objective of a control
chart is to quickly identify the occurrences of special causes. When an occurrence is
indicated by the chart, e.g. if a measurement falls outside of the control limits, then
the process is stopped and the cause is identified, eliminated, and the process is
improved. One also looks for patterns on the control chart. If a pattern exists, it may
be an indication that the process is unstable. There are many types of control charts
(X-bar, R-bar, S, I, MR) that are used for different circumstances.
2.5 The DMAIC Improvement Roadmap
The DMAIC (Define-Measure-Analyze-Improve-Control) is the classic Six Sigma
problem-solving process. Traditionally, the approach is to be applied to a problem
with an existing, steady-state process or product and/or service offering. Variation is
the enemy –variation from customer specifications in either a product or process is
the primary problem. Variation can take on many forms. DMAIC resolves issues
of defects or failures, deviation from target, excess cost or time, and deterioration.
Six-Sigma reduces variation within and across the value-adding steps in process.
DMAIC identifies key requirements, deliverables, tasks, and standard tools for a
project team to utilize when tracking a problem.
Banuelas, Antony, and Brace (2005) stated that Six-sigma represents the
strategy combing the Six-Sigma statistical measure and TQM. The DMAIC
problem-solving methodology is particularly useful when: 1) The cause of the
problem is unknown or unclear, 2) The potential of significant savings exist, and 3)
The project can be done in 4-6 months. Table 2 lists the steps included in the phases
of the DMAIC roadmap (Banuelas, 2005):
9

18. 1Table 2: The DMAIC roadmap and the steps included in each phase
Phases
Steps included


Define the scope and boundaries of the project
Define team charter to identify process definition,
critical-to-quality parameters, benefit impact, key milestone
activities with dates, support required and core team members



Map process and identify process inputs and outputs
Establish measurement system capability
Establish data collection plan

Gather data

Perform cause and effect analysis to identify parameters that
most significantly affect the process

Select critical-to-quality parameters to improve
4, Improve



Screen potential causes that affect process
Discover variable relationships
Establish operating tolerances
5, Control

Develop a control plan to sustain improved quality
1, Define
2, Measure
3, Analyze
Arnheiter and Maleyeff (2005) pointed that with the Six-Sigma overall strategy; an
organization can not only achieve near perfect quality using DMAIC methodology,
but also attain superior availability, reliability, delivery performance, and
after-market service. All of these factors contribute to customer satisfaction. To
ensure the effectiveness of the Six-Sigma philosophy within an organization, formal
training programs, must be put in place and supported by management.
2.5.1 Define Phase
Define the problem and what the customer requires. Henderson and Evans (2000)
stated the define phase sets the expectation of the improvement of project and
maintenance of focus of Six-Sigma strategy on customers’ requirement. The quality
problem that requires break through solutions has to be defined in measurable terms.
The defining of the problem is the first and the most important step of any
Six-Sigma project because better understanding of the problem makes the job much
easier later on during analysis. The defining of the problem forms the backbone of
any Six-Sigma project. The objectives to define a problem are as numbered: 1) To
identify the process or product for improvement, 2) To identify the voice of
customer, 3) To identify the customer’s requirement and translate the customer
needs into CTQ’s. There is many tools used in Six-Sigma methodology for defining
10

19. the problem but the “High level process map –a SIPOC diagram” as shown in figure
2.3 is one of the best tools being used in defining a problem as it fulfills all the basic
objectives to define a problem. Kaushik and Khanduja (2008) stated that within
SIPOC diagram, the letters stand for: Supplier–The people or organization that
provides information, material and other resources to be worked on in the process;
Input–The information/material provided by suppliers that are consumed or
transformed by the process; Process–The series of steps that transforms the inputs;
Output–The product or Service used by the customer; and Customer–the people,
company or another process, that receives the output from the process ().
3Figure 2.3 SIPOC diagram
According to Pyzdek (2003) plan, in this phase, had to determine which
opportunities will provide the biggest payoff for the efforts. Part of task involves
describing the current state of various metrics. Ask several questions to determine,
such as: Are there important trends? Are the data relatively stable or are there
outliers? What do the statistical distributions look like? Are the distributions what
would expect from this process? Pyzdek, (2003) consider some tools and techniques
during the Define phase include the following: 1) Cause-and-Effect diagrams, 4)
seven management tools for quality control (7M) and 5) data mining–exploring
information, contained in the enterprise data warehouse using automated.
2.5.2 Measure Phase
According to Basu and Nevan (2003), Six-Sigma is based on measured data. The
measure phase identifies the defects in the product, gathers valid baseline
information about the process. There will be unfavorable consequence form analysis
using Six-Sigma tools if there is problem with measuring system. The observed
possible source of variation in a process, as shown in figure 2.4, is the actual process
variation and measurement variation. To address actual process variability, firstly it
is necessary to identify the variation due to measurement system and to separate it
out from the process. The goal of the statistically confident otherwise if there is
11

20. problem with measuring system, the process gets worse and the experiment will end
up failure. Therefore it is very important to secure a correct measuring system before
the project. Raisinghani (2005) stated that in the measure phase, a measurement
system analysis (MSA) is conducted which includes the Gauge R&R studies The
purpose of the Gauge R&R study is to ensure that the measurement system is
statistically sound. Gauge repeatability and reproducibility studies determine how
much of the observe process variation is due to the measurement system variation.
According to Pyzdek (2003), before trusting the information it is important to verify
that it is reliable and valid. To evaluate the reliability and validity of dimensional
measurement system, such as gauges, conduct a gauge repeatability and
reproducibility (R&R) study. Gauge R&R studies are scientifically designed to
quantify gauge error from a variety of sources. Six Sigma projects usually involve
metrics that are classifications rather than determinations of physical properties such
as length, width, color, etc. The classification can be binary (male/ female/ good/
bad, failed/ didn’t fail, meets requirements/ fails requirements, etc.), nominal
(red-blue-green, shipped by truck/ car/ train, etc.), or ordinal (good-better-best,
dissatisfied-satisfied-delighted). In this phase, summarize the results of the
measurement system used to evaluate attribute data. Pyzdek (2003) consider some
tools and techniques during the measure phase include the following: 1) Voice of the
process (7 quality tools); 2) Evaluate measurement system gauge R&R; 3) measure
the process capability (Cp); and 4) Select measures of performance (QFD), Quality
Function deployment is a method of defining what the customer needs and what is
critical to their business success and prioritizing performance measures to support
customers need.
OBSERVED PROCESS VARIATION
ACTUAL PROCESS VARIATION
MEASUREMENT VARIATION
VARIATION DUE TO GAUGE
VARIATION DUE TO OPERATOR
REPEATABILITY
REPRODUCIBILIT
4Figure 2.4 Possible source of variation (Kaushik and Y
Khanduja, 2008)
2.5.3 Analyze Phase
Kapur and Feng (2005) noted that the analyze phase examine the data collected in
12

21. order to generate a prioritized list of source of variation. Many statistical tools are
used to carry out the analyses which are explained as follows: 1) Run chart; 2)
Histogram; 3) Process capability analysis; 4) Fishbone Diagram; and 5) Bar chart.
According to Pyzdek (2003), in this phase of the Six Sigma project cycle, must
quantify the existing process to determine how best to achieve the process
improvement goals. Tools and techniques useful during the analyze phase: 1) Cause
& Effect; 2) Process capability analysis; 3) FMEA (Failure Mode Effective
Analysis); 4) Contingence analysis; and 5) Detailed process maps.
2.5.4 Improve Phase
Abbas, Li, Al-Tahat, and Fourd (2011) noted that to improve the process by
removing the cause of defects. The optimal solution for reducing mean is determined
and confirmed in improve phase. The gains from the improve phase are immediate
and are corrective in nature. Specific problem identified during analysis are attended
in improve phase. This stage involves: 1) To use of brain storming and action
workouts; 2) Process optimization and confirmation experiment; 3) Extracting the
vital few factors through screenings; 4) Understanding the co-relation of the few
factors. Pyzdek (2003) noted that there are some improvements in every phase of the
project. The work done in the Define, Measure, and Analyze phase all help better
determine what the customer wants, how to measure it, and what the existing
process can do to provide it. It is possible that, by the time the Improve phase has
been reached, so much improvement will already been made that the project goals
have been met. If so, the project may be concluded. However, if the process
performance still falls short of the project’s goals, then additional activities in the
improvement phase must be undertaken. Pyzdek (2003) considered some tools and
techniques during the improve phase include the following: 1) Prioritize
improvements –Tool commonly in uses are, Impact vs. Effort, Brainstorming,
Affinity diagrams, Solution selection matrix. These tools help define the best
method to meet the customer need (as defined in the QFD); 2) Tactical
implementation plans –Deliver improvements to reduce variation systematically i.e.
make a change, note the improvement and make the next improvement.
2.5.5 Control Phase
In the last phase of the Six Sigma methodology Mukhopadahyay (2007) proposed to
control the process to make sure that defects don’t recur i.e. removes the root cause
of the problem. The control phase is preventive in nature. All the possible related
problem of the specific identified problem from the analysis phase are tackled in
control phase: 1) It mainly defines control plans specifying process monitoring and
13

22. corrective action; 2) provides systematic re-allocation of resources to ensure the
process continues in a new path of organization; and 3) Ensures that new process
conditions are documented and monitored. Basu and Nevan (2003) stated the real
challenge of Six Sigma methodology is not in making improvements to the process
but in sustaining the optimized results. This requires standardization and constant
monitoring and control of the optimized process. In this phase of the Six Sigma, will
develop controls to ensure that keep hard-won gains. The objective is to remove
the root causes of process variation, management are only left with a few critical
input variables in the process that need controlling and not all inputs as before. Basu
and Nevan (2003) consider some tools and techniques during the define phase
include the following: 1) Recover, control plans, escalation process; 2) Prevent by
poke yoke (fool proof the process) to fundamentally remove the root causes of
process variation; and 3) Monitor, control charts, checklists, documentation and
standardization, to ensure that stable process is maintained and that the process does
not degrade.
2.6 DMADV Roadmap
DMADV stands for Define, Measure, Analyze, Design, and Verify. It is the
standard Six Sigma method for designing new processes or reengineering existing
processes. DMADV is a common framework that is used for Design for Six Sigma
(DFSS) and these are often used synonymously, although there are other frameworks
that can be used for DFSS. It is different from DMAIC, which is used for
incremental improvement. A DMAIC project may be revised as DMADV if it is
found that incremental improvements are insufficient or a complete redesign is
otherwise the best approach. DMADV may also be used when a process has
reached Entitlement, or that position where it cannot be improved further using
current technology, resources and methods. The steps involved in the DMADV
methodology have been outlined below:
Define: The function of define step is to establish clear definition of the project.
This includes product or process that will be improved or the needs that will be met,
and the scope of the project, with schedule, resources, and deliverables, much like a
project management plan. It also includes a management plan, identifying the
known and foreseeable risks in the project.
Measure: Understand, segment and prioritize customers and so
determine Critical to Customer (CTC) measures. From these derive Critical to
Quality (CTQ) measures, possibly using Quality Function Deployment (QFD). Also,
measure is appropriate for process capability, risk and product capabilities.
Analyze: The analysis focuses on identification of the different approaches that
14

23. could be used to meet customer or stakeholder requirements. Alternatives are
evaluated, and the effective alternative, based on the best parts of the best concepts,
is selected for Design. During the analyze stage, an estimate of the total life cycle
cost of the design is made, creation of the production system or process, ongoing
production, use of the product or service, disposal of the product or service and final
retirement of the process or production system.
Design: The design stage includes both high level and detailed design for the
selected alternative. Design elements are prioritized and a high level design is
developed. Following that, a more detailed model is prototyped. There is an effort to
identify where errors may occur and address them through modifications.
Verify: The final step involves piloting the new product or service, gathering
data and evaluating performance, satisfaction, or results. A plan is developed and
implemented to transition the product or service to a routine operation for the
organization and ensure that the change is maintained.
2.7 Summary
The strategic implementation of Six Sigma in steps (DMAIC) leads to an
optimization of some selected process parameters, thus resulting in substantial
saving in overall operational costs of a process industry. Through critical
investigation of Six Sigma and its statistical tools, the study illustrates certain
ground rules, which are required to be laid down before starting such an exercise
with same kind of tools. Use of these ground rules will make Six Sigma more
effective, more productive with less effort and less consumption.
Many view DMAIC as the foundation of Six Sigma. DMAIC is best used as an
iterative problem-solving method to combat variation in an existing, steady-state
process. Some of the past researchers were developed different roadmaps and
methods which may able to use in six sigma projects that satisfies Six Sigma
requirement in terms of the different area. Principally, the purpose of this study is to
enhance the current improvement roadmap, DMAIC is not a new improvement
roadmap in Six Sigma. After creation of DMAIC, there is many roadmaps were
created regarding different business field. But DMAIC is still most common used
roadmap in Six Sigma methodology for improvement of the process. In this faster
grooving world everything is continuously improving, so the DMAIC Improvement
Roadmap itself should be continuously improved too, to step with others. An
enhanced improvement roadmap will be proposed in the following chapter of this
study.
15

24. Chapter 3 Development of an Enhanced Improvement
Roadmap in Six Sigma Methodology
Literatures from past researchers, in the previous chapter of this study, are for
understanding the current status of the original DMAIC roadmap. As stated
previously, the Six Sigma is a problem-solving methodology. Specifically, the
process capability enhancements achievement will bring about successful
improvements for the process and product quality with the successful application of
the Six Sigma project. In order to satisfy the Six Sigma requirement, the process
capability has to be greater than or equal to two, Cpk≥2.
Antony, Kumar and Tiwari (2005)’s research work “An application of Six
Sigma methodology to reduce the engine-overheating problem in an automotive
company” that adopted Six Sigma DMAIC Roadmap for the improvement of
processes resulted a reduction jamming problem encountered in the cylinder head
and increased the process capability from Cpk=0.49 to Cpk=1.28. But the Six Sigma
adaptations did not reach the Six Sigma requirement of Cpk≥2 after the application
of DMAIC. Cpk=1.28 is not the Six Sigma requirement, it supposed to be at least
Cpk=2. From this case, the DMAIC improvement roadmap didn’t really reach the
Six Sigma requirement after its application. This study considered that there are
deficiencies in the phases of the DMAIC roadmap while reviewing the research
work above.
In order to solve the deficiencies of the DMAIC roadmap and achieve the
Six Sigma goal, this study seeks to enhance the existing roadmap with emphases on
the process capability to better insure improvement. Thus, this study is proposing an
enhanced improvement roadmap which can achieve process capability Cpk≥2 with
DMAIC roadmap in Six Sigma methodology.
3.1 Development of the Enhanced DMAIC Improvement Roadmap
The aim of this study is to enhance the existing improvement roadmap in Six Sigma
methodology to achieve its specified requirements. Due to deficiencies that rose in
the introduction, the DMAIC improvement roadmap is not fully guarantee to
achieve six sigma requirements. Therefore, the improvement roadmap tasks have to
be enhanced. Due to the Six Sigma objects to improve the process capability to
specified requirement, thus the enhancement of the DMAIC roadmap emphasis on
the statistical tools. DMAIC is 5 phases of roadmap for improvement of the existing
process. Every phase of DMAIC have different role. By enhancing the tasks of the
16

25. phases, the application of the DMAIC can reach the six sigma requirements.
Development of the enhanced improvement roadmap will bring a successful
application of Six Sigma. Basically, this study enhanced the original DMAIC by
different statistical and measurement tools to phases. In the first two phases
enhancement focuses on the voice of the customer and process (VOC&VOP) data
collection and measurement. Based on the identified and measured voice of the
customer and process relationship, the current process capability will clearly
described. All the voice of the customer and process data that defined and measured
will help to Design of Experiment (DOE) to analyze and improve the process
capability and the affected process variations. Argumentation to this statistical tools
enhancement is explained in section 3.2 of this study.
Basically, the enhancements to the DMAIC improvement roadmap are as
follows: Phase 1–the original DMAIC helps in defining customer requirement and
identifying the Voice of the Customer (VOC) whereas the enhanced DMAIC is
associated with defining Voice of the Process (VOP) and its applicability; VOP and
VOC relationship for Critical to Satisfaction (CTS); Phase 2–the original DMAIC is
measuring the customer requirements and specifications; gathers valid baseline
information about the process whereas the proposed DMAIC is enhanced to measure
the VOP to identify the process capability; Phase 3–In the original DMAIC, a
business process is analyzed to find the root cause of a defect or recurring problem,
but in the enhanced DMAIC, except finding the root causes of a defect, develop the
process capability and to identify the variations that are causing the process through
DOE ; Phase 4 –In the original DMAIC, improvements are made in the business
process for eliminating or reducing defects whereas in the enhanced DMAIC
Perform the DOE to improve the process capability and identify optimal setting of
process parameters to eliminate problem; and Phase 5 –In the enhanced DMAIC, is
sustaining the optimized process, and constant monitoring and controlling of the
optimized process via control charts. Table 3 is shown below is the enhanced
roadmap and the tasks that highlighted by bold format represents the enhancements
of the enhanced DMAIC improvement roadmap.
3.1.1 Voices of the Customer and Process Relationship Information
Measurement Enhancement
Define and Measure phases of the DMAIC are interrelated to each other. Main idea
of these two phases is to identify and measure the current process and customer
requirements. More specifically, define phase of DMAIC aims to define the scope
and goals of the improvement project in terms of customer requirements and
develop a process that delivers these requirements, while measure phase is
17

26. concerned with selecting one or more product characteristics, mapping the
respective process, making the necessary measurements, recording the results on
process control cards, and establishing a baseline of the process capability.
2Table 3: An enhanced DMAIC improvement roadmap and the tasks
Define
Phase
Measure
Phase
 Define the scope and boundaries of the project
 Define team charter to identify process definition
 Define initial voice of the customer (VOC), voice of the process
(VOP) and critical to satisfaction (CTS)
 Map process and identify process inputs and outputs
 Establish measurement system capability
 Establish data collection plan
 Measure voice of the process (VOP) and current performance
 Gather data
Analyze
Phase
Improve
Phase
 Perform cause and effect analysis
 Select critical-to-quality parameters to improve
 Develop process capability and analyze the variations that
causing the process via DOE
 Screen potential causes that affect process
 Discover variable relationships
 Establish operating tolerances
 Perform the DOE to improve the process capability and identify
optimal setting of process parameters to eliminate problem
Control
Phase
 Plot control charts to check Cpk≥2 ( if not, go to the Measure
Phase)
 Develop a control plan to sustain improved quality
In the define phase needed to identify the performance standards according to the
customer requirements. Then the measure phase can translate the customer needs
into measurable characteristics. Based on the specification limits, performance
standards for each process parameter would establish. Having established the key
process parameters and the critical to quality characteristics, it is essential to
establish the accuracy of the measurement system and the quality of the data.
Many researchers, such as Franza and Chakravotry (2009), Smith, Blakeslee
and Koonce (2002), were mentioning about to define the Voice of the Customer
(VOC) in this phase in order to understand what their needs are, but Furterer (2009)
and Stauffer (2009) suggested to define not only VOC, also Voice of the Process
(VOP). As noted Stauffer (2009), the match of these two voices is done via the
concept of process capability. Whereas the VOC communicates customer desires,
18

27. requirements, needs, specifications, and expectations, the VOP communicates
information about the performance of the process. The challenge for the process is to
use VOP information to better meet the customer needs as defined by the VOC. In
terms of VOP statements, during the application of DMAIC roadmap, have to
clearly define what the current process capability is, and how far it can go. The
DMAIC roadmap is the improvement tool for the existing process optimization. One
way to describe Six Sigma is that it is measurable process which compares the VOP
and VOC. Process improvement occurs to achieve the desired quality outcome and
reduce variability in the VOP until it is as least as good as the VOC. Also, make
charts of the process that should be improved. Therefore, this study is enhancing the
first two phases by VOP to identify the current status of the process to analyze
during Six Sigma application. Details of the enhancement explained in section 3.1.2.
3.1.2 Define and Measure the Initial VOC, VOP and Identify CTS
In the Define and measure phases, the focus is on collecting information from the
customer to understand what is important regarding the process. In the define phase,
the initial VOC data collection to understand the CTS criteria, which are the
elements of a process that significantly affect the output of the process. It is critical
to focus on the CTS throughout the phase of the DMAIC problem-solving process.
The VOC is a term used to “talk to the customer” to hear their needs and
requirements or their “voice”. Many mechanism can be used collect VOCs,
including interviews, focus groups, surveys, customer complaints and warranty data,
market research, competitive information, and customer buying patterns.
Montgomery and Woodall (2008) stated that the steps to identify the CTS are
shown as follows: 1) Gather appropriate VOC data from market research, surveys,
focus groups, interviews etc.; 2) Extract key verbatim from the VOC data collections,
identifying why a customer would do business with your organization; 3) Sort ideas
and find themes, develop an Affinity or Tree Diagram; 4) Be specific and follow up
with customers where needed; 5) Extract CTS measures and specifications from
customer information; and 6) Identify the missing data and fill in the gaps. Stauffer
(2009) pointed out the types of the quality. Type one is fairly easy to deal with.
Some specifications may be set, based on desired quality characteristics, and
machines or producing processes controlled to keep output consistent with those
specifications. Type two quality characteristics may also be measured as output of
systems. These measurements can be tracked using process behavior charts to
characterize the VOP. Type three is trickier; it requires that to tap into the VOC,
articulate that voice as a set of measurable characteristics, and then translate tose
CTS characteristics into process measures. Essentially, this translation should match
19

28. the two voices: VOC and VOP. The matching of these two voices is done via the
concept of process capability. This process capability concept is fundamental and
definitive in Six Sigma.
One way to describe Six Sigma is that it is a measurable process which
compares the VOP to VOC. Process improvement occurs to (a) achieve the desired
quality outcome and (b) reduce variability in the VOP until it is as least as good as
the VOC. York (2009) graphically illustrated and compares the VOP versus VOC in
Figure 3.1.
5Figure 3.1 Comparing the VOP vs. the VOC (York, 2009)
A VOP matrix, developed by Furterer (2004), can be used to achieve integration and
synergy between the DMAIC phases and the critical components of the process to
enhance problem solving. The VOP matrix includes the CTS, the related process
factors that impact the CTS, the operational definition that describes how the CTS
will be measured, the metric, and the target for the metric. A template for the VOP
matrix is shown in Figure 3.2 (Furterer, 2004).
CTS
Process Factors
Operational definition
Metric
Target
6Figure 3.2 VOP matrix template (Furterer, 2004)
The VOP can use man quality tools, such as bar charts, Pareto charts, run charts
control charts, cause and effect diagrams, and checksheets. A Pareto chart, that
shown in Figure 3.3, helps to identify critical areas causing most of the problems. It
provides a summary of the vital few rather than the trivial many. It helps to arrange
the problems in order of importance and focus on eliminating the problems in order
of highest frequency of occurrence. Following are the steps for creating for Pareto
chart (Furterer, 2009): 1) define the data categories, defects, or problem types; 2)
determine how to relative importance is defined; 3) Collect the data and compute the
cumulative frequency of the data categories; and 4) plot a bar, showing the relative
importance of each problem area in descending order. Identify the vital few to focus
20

29. on.
7Figure 3.3 Pareto Chart
A check sheet is graphical tool that can be used to collect data on the process and the
types of defects so that root causes can be analyzed in the analyze phase. The steps
create a check sheet are: 1) choose a characteristics to track, i.e., defect types; 2) set
up the data collection check sheet; and 3) collect data using the check sheet. A
Pareto chart can then be created from the data collected on a check sheet. A
histogram is a graphical tool that that provides a picture of the centering, shape, and
variance of the distribution of data. Minitab is commonly used to create a histogram.
It is important to graph the data in a histogram as the first step to understanding the
data. Mainly creating a histogram to measure how capable is the current process.
Statistics can be used to assess the VOP related to the metrics that measured. Once
the data are collected, they can be tested to see if the data distribution follows a
normal distribution using a test for normality. QFD is a method of defining what the
customer needs and what is to their business success and prioritizing performance
measures to support the customer need.
3.1.3 Enhancement of DOE for Process Capability Analysis and
Improvement
Main enhancement of the study is for the process capability improvement to require
Six Sigma goal. Main role of analyze and improve phases is to improve the process
capability regarding the particular analysis. Basically, analyze the root of defect and
cause of deviations; find out the factors that have to be improved and by reducing
the defected variations to improve the process capability and reach the Six Sigma
requirements. During the analyze phase process capability must be clearly described
for the improvement. Process capability is the ability of a process to produce
products capable of meeting the specifications set by the customer. Process
capability is based on the performance of individual products against specifications.
There are several steps for to perform process capability. In order to perform process
capability for the metrics that measure the CTS characteristics defined in define and
21

30. measure phase, collect the data on the process for metric and need to perform
graphical analysis (histogram). Due to analyzing the graphical data determine if the
process is in control and stable, using control charts. Sadraoui, Afef, and Fayza
(2010) noted that if the process capability is stable the estimate the process mean
and standard deviation; and calculate the capability indices, Cp and Cpk. But if the
process capability is not stable, then what would be the next? For some processes
several rounds of improvement may be required to achieve desired process
capability.
Due to Six Sigma requirement, the process capability has to be greater or
equal to two. If the process capability is greater than or at least 2 (Cp≥2), then go for
the Control stage, but if the process capability is less than 2 (Cp<2), which is not the
Six Sigma requirement, then redesign the material, product, and process as required
as Dahlgaard, (2006) was mentioned. Therefore, this research work is proposing the
Design of Experiment (DOE) to analyze all process variations more clearly for
improvement in order to identify defects that affecting the process. Basically, DOE
will define why the process is not capable and investigate all variations which might
affect the process that will identify the process that needs improvement. And then
DOE will determine process input and output to measure why the defect has
occurred and where the failure that makes the process incapable is. During this
period the process flow and establishment of the performance baseline will be
created. Since the performance baseline is created, then reanalyze the process
variations that has defects. The process capability identification will be analyzed in
order to identify the defected variations for improvement. Using the DOE, kill the
special causes that affecting the process and process capability will be enhanced.
The reduction of the variations brings the process within the specification limits.
Since process variation within the specification limits the process is more capable.
But not only improving the process, DOE will verify and control the variation via
control charts that improvement is sustaining. After verification of the process,
documentation needed to be made for next phase. Details of the enhancement
explained in section 3.1.4.
3.1.4 Development of the Process Capability via DOE
Six-Sigma represents a stretch goal of six standard deviations from the process mean
to the specification limits when the process is centered, but also allows for a 1.5
sigma shift toward either specification limit and this represent a quality level of 3.4
defects per million.
Moran and Duffy (2009) noted that to determine if a process is capable of satisfying
its customer, two most commonly used indices (Cp and Cpk) are: Cp, which
22

31. measures the variation in a process or how well data fits within the upper and lower
specification limits (USL, LSL). This measure is the width of the process
distribution relative to a set of limits and is sometimes referred to as the process
potential. The Cp should be as high as possible since the higher the Cp the lower the
variability. One problem with Cp is a process may have a high Cp but is producing
many defects since the actual spread does not coincide with the allowable spread of
the specification limits. This why there is a need of the second index Called Cpk.The
Cpk index measures the central tendency of the process. The Cpk measures how
close a process is performing to its specification limits and how centered the data is
between those limits. It is an indicator of the ability of a process to create product
within specification. Basically, Cp is for the measurement index for the new process
design while Cpk is the measurement index for the current process. The Figure 3.4
shows the 1.5 sigma shift from the mean which is Cpk measurement concept. The
greater number of sigma level, the smaller the variation (the tighter the distribution)
around the average. Figure 3.5 shows a Sigma-to-DPMO conversion. DPMO is
calculated as (Brassard and Ritter, 2001): DPMO=Defects*100000/Units *
Opportunities.
8Figure 3.4 Sigma to DPMO-conversion, assuming 1.5 sigma shift
9Figure 3.5 DPMO representing a Six Sigma quality level, allowing 1.5 sigma shift
average
23

32. Process capability is the ability of a process to produce products or provide services
capable of meeting the specifications set by the customer. Process capability is based
on the performance of individual products or service against specifications.
According to the central limit theorem, the spread or variation of the individual
values will be greater than the spread of the averages of the values. The steps for
performing a process capability study are: 1) define the metric or quality
characteristics. Perform the process capability study for the metrics that measure the
CTS characteristics defined in the define and measure phase; 2) collect data on the
process for the metric; 3) perform a graphical analysis (histogram); 4) perform a test
for normality; 5) determine if the process is in control and stable, using control
charts. When the process is stable continue to step6; 6) estimate the process mean
and standard deviation; 7) calculate the capability indices, Cp and Cpk (Summers,
2006):
Cp= (Upper specification limit –Lower specification limit)/6 sigma
Cpk= /Minimum of CPU, CPL/
Where:
CPU= (Upper specification limit –Process mean)/3 sigma
CPL= (Process mean –Lower specification limit)/3 sigma
The purpose of this study is to enhance the improvement roadmap in Six Sigma
methodology with focus on improving the process capability. In order to improve
the process capability, this study seeks to analyze all the data variation through the
DOE for the improvement. DOE help to identify which variations in the process
need a reduction in order to enhance the process capability. DOE does analysis and
improvement based on the data gathered from the measured VOP and VOC. The
DOE defines why the process is not capable enough and investigate all variations,
which might be affecting the process, which needs improvement. Basically, DOE
identifies the factor’s shift from the average (A1&A2); the factors which affect
variation (B1&B2); the factors which shift the average and affect variation (C1&C2);
and also the factors which have no effect (D1=D2).
10Figure 3.6 DOE identification of the variation factors
The variation factors needs to be identified for the characteristics of the process that
24

33. are critical to the requirements for quality performance and which contribute to
customer satisfaction. The DOE will identify process input and output as shown in
Figure 3.7 and to measure why the defect has occurred and where is the failure that
makes the process incapable. Hence, establish the performance baseline for analysis.
11Figure 3.7 DOE establishment of the performance baseline
After measurement, analyze the process variation that causing the process. DOE
identifies defected variation during the analysis that shown in Figure 3.8. Process
capability analysis supports to expose the defected variations. So in the improve
phase, those variations that causing the process needed to be improved via DOE.
12Figure 3.8 DOE process capability analysis and exposed defected variations for
improvement
After analyzing the variations improve the process by killing those special causes
that are affecting the process. After optimization of the process by killing the causes,
25

34. the variation should be reduced within the specification limit as shown in Figure 3.9.
13Figure 3.9 DOE optimized process variations and improved performance baseline
The real challenging of Six Sigma is not in making improvements to the process but
in sustaining the optimized results. This requires standardization and constant
monitoring and control of the optimized process. Control the process deviations to
meet customer needs. As shown in Figure 3.10, DOE will check the process status
due to the required process capability (Cpk=2) achievement, and sustain the
optimized result. But if the application of DMAIC is not reach the Cpk=2, then go to
the measure phase to cycle again in order to reach the required Cpk.
14Figure 3.10 DOE monitoring and verification procedure for optimized variations
26

35. Proper monitoring of the process helped to detect and correct out-of-control signals
before they resulted in customer dissatisfaction. Montgomery and Woodall (2008)
suggested using statistical process control (SPC) charts to monitor and control
process, and ensure that the process is not out of control. SPC charts are a graphical
for monitoring the activity of an ongoing process. The most commonly used control
charts are also referred to as Shewhart control charts.
3.2 Argumentation to the Enhancements of the Improvement
Roadmap
3.2.1 Enhancement Focus
The object of the enhancement of the improvement roadmap is to achieve required
process capability (Cp≥2) in Six Sigma methodology. The reason why this study is
necessary is because some researchers, Antony, Kumar and Tiwari (2005), who
adapt the Six Sigma DMAIC improvement roadmap for the improvement of the
processes did not reach the Six Sigma requirement (Cp=2). In achieving Six Sigma
requirement, an enhanced improvement roadmap enhances some statistical and
technical tools (DOE, VOP and more) that guarantee to reach the Cp≥2 after the
application of Six Sigma.
3.2.2 Argument Responds to the Enhancements
From the view of table 3, there are certain differences between the original DMAIC
improvement roadmap and the enhanced DMAIC improvement roadmap. Based
upon the past literatures, the present author postulates the following arguments in
response to the deficiencies which were found in the existing roadmap. The DMAIC
improvement roadmap is five phases of roadmap. In terms of the role of each phase,
there are certain arguments raised. Since Six Sigma is statistical methodology for
improvement, this study enhanced the DMAIC roadmap by statistical tools (i.e.,
VOP and DOE).
Pyzdek (2003) noted that an argument can be made for asserting that quality
begins with measurement. Only when quality is quantified can meaningful
discussion about improvement begin. Conceptually, measurement is quite simple:
measurement is assignment of numbers to observed phenomena according to certain
rules. Define phase of the DMAIC roadmap focuses on the expectation of
improvement of the project and maintenance of focus of Six Sigma strategy on
customer requirements and process performance whereas measure phase of
identifies the defects in the process or product, gather valid baseline information
about the process. Breyfogle (1999) stated that the defining and measuring the
problem is the first and most important phases of any Six Sigma project because
27

36. better understanding of the problem makes the job much easier later on during
analysis. Pepper and Spedding (2010) mentioned that the Six Sigma is based on
measured data. Ramamoorthy (2003) stated that measure phase is for identification
of the characteristics of product or process that are critical to the customer’s
requirements for quality performance and which contributed to customer satisfaction.
Woo and Wong (2007) pointed that the objective of the define phase is to identify
the process or product improvement, Voice of the Costumer (VOC), customer’s
requirement and translate the customer needs into CTS’s. But Furterer (2009) and
Stauffer (2009) were mentioned that DMAIC is the improvement tool for existing
process, defining only the customer requirement VOC will not clearly describe what
the status of the current process is, therefore the VOP needed to identified as well as.
Furterer (2009) noted that the purpose of the measure phase is to understand and
document the current state of the processes to be improved, baseline the current state
(VOP), and validate measurement system. Due to the Breyfogle (1999) statement, in
the define and measure phases everything must be clearly identified for analysis, so
at the same time understanding VOC and VOP would be excellent choice to
understand customer requirement and current process status. Larry (2009) noted that
the VOP is the statistical data from or out of a process that indicates the process
stability or capability that provides feedback to process performers as a tool for
continual improvement. Furterer (2009) suggested to using VOP matrix to achieve
integration and synergy between the DMAIC phases and the critical components of
the process to enhance problem solving. Furterer (2009) and Stauffer (2009) stated
that the Pareto chart, histogram, statistics, check sheet and VOP matrix are the
excellent tools for the measurement of the VOP.
Another argument rose due to the enhancement of the analyze phase and
improve phase, and its importance of the improving the process capability. Within
these two phases the process capability will be analyzed and optimized via statistical
tools. According to Kitchaiya (2006) the analyze phase is for evaluating the
current operation of the process to determine the potential sources of variation for
critical performance parameters and improve phase is to select those process
characteristics which must be improved to achieve the goal. Wang (2008) illustrated
that analyze phase examine the data collected in order to generate a prioritized list of
source of variation and then improve the process to remove cause of defects. Pyzdek
(2003) noted that in those phases of the Six Sigma project cycle, must quantify the
existing process to determine how best to achieve the process improvement goals.
Furterer (2009) mentioned that first need to analyze the data related to the VOC and
VOP to identify the root causes of the process problems, and the process capability
(Cpk); and improve the process. Main role of those phases are to analyze the data
28

37. gathered in first two phases for improvement and improve it by reducing the
variation and defects. More specifically, analyze the root of defect and cause of
deviation and find out the factors that to be improved, and develop the process
capability. During the analyze phase process capability must be clearly described for
the improvement. Process capability is the ability of a process to produce products
capable of meeting the specifications set by the customer. According to the Bewoor
and Pawar (2010), in order to perform process capability for the metrics that
measure the CTS characteristics defined in define and measure phase, collect the
data on the process for metric and need to perform graphical analysis (histogram).
Due to analyzing the graphical data determine if the process is in control and stable,
using control charts. Tonini, Spinola, and Laurindo (2006) noted that if the process
capability is stable the estimate the process mean and standard deviation; and
calculate the capability indices, Cp and Cpk. Due to Six Sigma requirement, the
process capability has to be greater or equal to two. There are many statistical tools
for process capability improvement. Caleb Li, Al-Refaire and Yang (2008) suggested
using the Taguchi method to improve capability of the process. But Furterer (2009),
Pyzdek (2003), Henderson (2006) strongly recommended to use Design of
Experiment to improve the process capability. Henderson (2006) mentioned that the
DOE can optimize the process first, minimizing variation by maximizing the signal
to noise ratios of the controllable factors that affect variation; second, selecting the
levels of the tuning factors that affect the mean to adjust the mean in the desired
direction (toward the target value). JMP (2005) stated that DOE is a very powerful
analytical method that multiple process variables can be studied at the same time
with these efficient design, instead of in a hit and miss approach, proving very
reproducible. Furterer (2009) noted that due to the statistical balance of the designs,
thousands of potential combinations of numerous variables can be evaluated for the
best overall combination, in very small number of experiments. Even the Taguchi
method, Caleb Li, Al-Refaire and Yang (2008) were suggested was itself using DOE
to improve the capability. Due to the many reason and argument above, this study
proposing to enhance the DMAIC roadmap by DOE to analyze and improve the
process capability.
3.3 Summary
The aim of this study is to enhance the existing improvement roadmap in Six Sigma
methodology to achieve its specified requirements, as stated in beginning of this
chapter. The object of the enhancement of the improvement roadmap is to achieve
required process capability (Cp≥2) via statistical tools (DOE&VOP) in Six Sigma.
In first two phases all the data related to the process and customer needs identified
29

38. through VOC and VOP. According to Stauffer (2009) the matches of the VOC and
VOP is done via the concept of process capability. This process capability concept is
fundamental and definitive in Six Sigma; the term Six Sigma came from capability
concept and studies, and process metrics such as DPMO and the process sigma come
directly from process capability concepts. DOE helps to analyze all the data
gathered from the first two phases through VOC and VOP relationship and to
improve the process capability by reducing the variation. The VOC, VOP and DOE
relationships can effectively reach the Six Sigma goal. An enhanced roadmap will
conduct to case study to demonstrate feasibility and effectiveness of the proposed
roadmap in following chapter.
30

39. Chapter 4 Case Study
The main portion of the thesis is dedicated to the development of an enhanced
DMAIC improvement roadmap in Six Sigma methodology. This section of the thesis
is devoted to a case study centering on the application of the enhanced DMAIC
improvement roadmap. The aim of this section is to prove the proposed roadmap
and reach the Six Sigma requirement (Cpk=2) by applying it to the case study
through several steps. Particular steps have to be followed to reach required process
capability in Six Sigma. First step is to identify what are the current status of the
process and its capability, and then the application of the original DMAIC roadmap
will improve that current process. If the process capability doesn’t reach to the
required amount, the enhanced improvement roadmap will be applied to reach to the
goal. The application of the enhanced DMAIC roadmap completes the missing tasks
of the applied original DMAIC roadmap tasks. After the enhanced roadmap
application, if the process capability improved to reach the required value, then
implement the second round of the analysis and improvement will achieve the
required goal.
The following sections of the thesis present the case study in detail. The current
status of the process identification of the example case will be given at first. The
action taken and the insufficient findings for each of the steps will be described, and
an analysis will be provided based on the study and statistical analysis. Minitab will
be used for the statistical analysis. The conclusion will discuss some of the
difficulties encountered during the course of the work as well as the effectiveness of
the proposed Six Sigma DMAIC roadmap. The limitations of this research mainly
consist of time where partnering with a company is not feasible and other minor
geographical factors impose restraints that creates a lean towards a case study that is
executable, appealing, and practical.
4.1 Case Description
Consider a financial administration process in a business entity where the goal of the
project is to streamline the payroll process and subsequently reduce its cycle time.
The financial unit realizes that the current process, with respect to the process before
the enhanced improvement roadmap is implemented, is insufficient, error-prone,
lengthy, and have extensive number of non-value added steps. The customers are for
the payroll process are employees that receive withholding payments and reports.
The entire payroll reporting and withholding payment process takes between 50 to
70 employee hours depending on whether processing problems occur. Payments to
31

40. employees are frequently late. Multiple invoices for the same payment are
frequently received and must be reviewed to determine if they have paid. The
estimated average and range of the processing time is displayed in Table 4. 1.
3Table 4.1 Estimated processing time and summary of process capability of each
processes
Estimated processing
Payroll
Estimated average
time range
Process
processing time
50 to 70 hours
Std. Dev.
Cpk
3.91937
0.84
60 hours
The payroll processing time was measured from 20 samples. It was not performing
capably according to the Cpk values obtained. The current process capability is
Cpk=0.84 for payroll as shown in Table 4.2.
4Table 4.2 Raw data of the payroll process
Labels
Payroll
Process
Labels
Payroll
Process
Labels
Payroll
Process
Labels
Payroll
Process
1
55
6
57
11
53
16
56
2
59
7
50
12
56
17
55
3
55
8
52
13
50
18
57
4
52
9
50
14
57
19
54
5
55
10
52
15
56
20
56
4.2 Application of the original Improvement Roadmap
5 phases of the DMAIC improvement roadmap is applied for improvement of the
payroll process. Successful implementation of the roadmap will be measured by the
reduction of process inefficiencies, the reduction of the time it takes to process the
payroll transactions, and the assignment of appropriate staffing levels to handle the
workload. Each phase of the DMAIC have different roles.
4.2.1 Definition and Measurement of the Current Process
Define phase defines the need of the improvement for the payroll process. In this
case, the need for improving the payroll process is to reduce the process time and
inefficiencies. The different inefficiencies are the following:
 Additional stuff needed to complete works
 Late payments to employees
 Inefficient processing and depositing
Another task was to develop a team charter to help team members clearly
understand the scope and boundaries of the project, project objectives, project
32

41. duration, resources, roles of the team members, estimated financial gains from the
project, etc. The SIPOC describes the scope of the payroll process improvement
project as shown in Figure 4.1.
Supplier
-Payroll
Input
Process
-Time reports
-Payroll
Output
-Checks,
Clerks
Customer
reports,
-Employees
taxes paid
15Figure 4.1 SIPOC diagram
The goal of measure phase is to understand and document the current state of the
process to be improved and identify the process problems that are causing
inefficiencies and errors and their root causes.
4.2.2 Analysis and Improvement of the DMAIC Application
The analyze phase is to analyze the problems and process inefficiencies and define
improvement opportunities. Using cause and effect analysis to identify root causes
related to people, methods, information technology, and hardware as presented in
Figure 4.2. It’s better to compare the identified gaps of the current state process to
practice a better payroll process.
16Figure 4.2 Cause and effect diagram
In the analyses phase it is important to identify the improvement opportunities and
develop an improvement plan. In this implementation, the study suggests that the
payroll unit develop standardized process and procedure. Another improvement area
is to use an excel spreadsheet to standardize batch calculations for matching, and
dividing invoices amounts across different account numbers. A recommendation to
the clerks who uses the payroll system is to get training for the software specifically
to their streamlined payroll process. Another recommendation is to standardize the
time sheets across all of the units to help reduce payroll data entry errors. Also for
payroll clerks to use timesheets in excel spreadsheets to calculate the total timesheet
hours by department to compare the payroll reports, instead of a calculator.
33

42. The goal of the improve phase is to implement the improvements, measure the
impact of the improvements and document the procedure and train employees on the
improved procedures. Validate the feasibility of the process improvement ideas in
the analyze phase and implement the plan regarding the improvement suggestion for
the payroll. Measure the impact of the improvements after the improvement is
implemented for payroll process. It was found that the payroll processing time was
reduced by 10%. The improvement is shown in table 4.3. The average time of
payroll process was 60 hours and the process capability Cpk= 0.84 before
improvement, after improvement the time was reduced to 54 hours, and the process
capability becomes Cpk=1.21. The histogram of the observed data is shown in figure
4.3. However, this is not the Six Sigma required achievement, Six Sigma requires
the Cpk=2. The next section will present the enhancement of Cpk to the required
achievement with the proposed improvement roadmap.
5Table 4.3 Summary of the improved process capability and process time
Process
Payroll
Estimated average processing time
54 hours
Std. Dev.
Cpk
2.5626
1.21
17Figure 4.3 Histogram of payroll process before (left) and after (right) improvement
4.3 Application of the Enhanced Improvement Roadmap
With the application of the original DMAIC roadmap the current process improved
from Cpk=0.84 to Cpk=1.21 but did not reach the required goal, because Six Sigma
requires the Cpk=2. In order to reach the Six Sigma requirement, the study applies
the proposed enhanced improvement roadmap in this section. The object of the
research is to provide an improvement roadmap which reaches the Six Sigma
requirement of Cpk=2.
In this section, the application of the enhanced improvement roadmap is not
going to repeat the application of the original DMAIC roadmap tasks but implement
the proposed tasks to complete the project. During the application of the Six Sigma
methodology, it is important to understand what your customer wants and what the
34

43. current capability of the process is by measuring the VOC and VOP.
4.3.1 Define and measure the VOC and VOP
VOC communicates customer desires, requirements, needs, specifications, whereas
the VOP communicates information about the performance of the process. Because
there was no process measurement system in place to assess the CTS criteria related
to cycle time, accuracy and customer satisfaction, the data collection plan is critical
to help provide a way to measure the CTS. The data collection plan is shown in
Table 4.4. In order to understand the VOC, customer survey was developed to assess
VOC requirements for employees regarding the payroll process. There are 6
questions assessing the employee survey:
1. I receive my pay paycheck in a timely manner,
2. I receive an accurate paycheck,
3. If I call or see the payroll unit for service, I get prompt service,
4. If I call or see the payroll unit for service, I receive friendly service,
5. If I call or see the payroll unit for service, my problem gets solved
completely at first time,
6. Please provide ideas for how to improve customer satisfaction with the
payroll unit.
The employee gives a survey by choosing numbers, such as strongly agree (1),
disagree (2), neutral (3), agree (4), and strongly agree (5).
6Table 4.4 Payroll process data collection plan
CTS
Cycle time
Metric
mechanism
mechanism
Track for two
payroll
payroll cycles
Type and number
the process
of defects
Satisfaction
Analysis
Time to process
Accuracy of
Customer
Data collection
Employees
Check sheet
Survey
Mean, range
Pareto chart
Sampling plan
Time for 2
payroll months
Defects for one
month
Statistical
Survey company
analysis
units
The VOP matrix helps to link the CTS criteria to the metrics, targets and potential
process factors that affect the CTS. The VOP matrix is used to summarize the VOP
(Table 4.5). The CTSs were defined as cycle time, accuracy of the process, and
customer satisfaction. The cycle time was defined to be measured. The accuracy of
the process would be potentially impacted by training in procedures and payroll
software would be measured by assessing number and types of defects in the process.
35

44. Customer satisfaction could be impacted by whether there was repeatable process
and whether the company would collect and measure VOC information. The VOC
could be measured through surveys. The proposed target for each of the metrics is
also included in the matrix.
7Table 4.5 VOP Matrix for payroll process
CTS
Process factors
Operational
definition
Metric
Target
-Standard procedure
exist
Cycle time
-Streamline processes
Measure each
Paid on time
Paid on
-training
process time
per schedule
time
-Volume of invoices
and paychecks
Accuracy of
-Training in procedure
the process
and software
Measure each
process and
defect types
Measure
Customer
satisfaction
-Repeatable process
-Collect and assess
VOC
customer
satisfaction
through
customer
surveys
Defects by
process type
% of positive
responses for
identified
survey
question
100%
80% of
responses
are rated
4 or 5 for
identified
questions
4.3.2Analysis and Improvement via DOE
The aim of the project is to enhance the process capability by reducing variation in
the process. Moreover, it is also important to understand the causes for the poor
process capability. DOE helps to identify the possible sources of variation that affect
the process, and by reducing the variation improve the process capability. Analyzing
the gathered data, from the VOC and VOP relation, DOE will easily find out the
problem and improve it. Payroll process by resolution category is shown in Figure
4.4 with help of a Pareto chart. The Pareto chart is developed to understand the
process problems. Pareto analysis helps to identify employee training and
knowledge gaps of the payroll information system.
At this point, it is imperative to identify the parameters that are significant to the
process so that they can be brought under statistical control. A simple regression
analysis is performed to determine the significance of the process parameters. It is
concluded form the regression analysis that the variables with P values less than
0.05 to 0.01 are statistically significant for further study.
36

45. Percentage
Resolution category
18Figure 4.4 Pareto chart for information system problems
DOE capabilities provide methods for simultaneously investigating the effects of
multiple variables on an output variable. These experiments consist of a series of
runs, or tests, in which purposeful changes are made to input variables or factors,
and data are collected at each run. Quality professionals use DOE to identify the
process conditions and product components that influence quality then determine the
input variable settings that maximize results. Payroll process inefficiency is
according the p value; it’s needed to be optimized. Full factorial DOE investigated
what affects the process time. The reason a full factorial DOE is chosen for
simplicity in collecting data since it is currently difficult to obtain. As well it reduces
the number of possible combinations the experiment must perform. The optimization
of the parameter yields an optimum response. Figure 4.5 illustrates the DOE
graphical analysis of the payroll process.
DOE Graphical analysis of the payroll process
Individual Value Plot of Payroll process
Scatterplot of Payroll process
50
55
Afternoon
58
60
Evening
65
60
Payroll 1
Payroll 2
56
54
55
Morning
65
60
52
55
50
50
A Attribute
B Attribute
C Attribute
55
Histogram of Payroll process
Histogram of Payroll process
Normal
Normal
0,4
Mean
54,71
52,86
55,67
0,2
0,1
StDev
2,984
2,734
1,033
N
7
7
6
A Attribute
54 56 58
B Attribute
C Attribute
3,0
1,5
0,0
48
50
52
54
56
58
60
60
3,0
1,5
Frequency
Density
48 50 52
Center
A Attribute
B Attribute
C Attribute
0,3
0,0
60
Panel variable: Center
48
50 52 54
0,0
A Attribute
Mean 54,71
StDev 2,984
N
7
B Attribute
Mean 52,86
StDev 2,734
N
7
C Attribute
Mean 55,67
StDev 1,033
N
6
56 58 60
Panel variable: Center
19Figure 4.5 DOE graphical analysis of the payroll process
DOE is conducted using the payroll process parameters. The process parameter is
studied at two levels in order to keep the size of the experiment to a minimum, as
well as to meet time. Full factorial design is chosen so that both main effects and
37

46. interaction effects, the trail condition were replicated twice. As the object of the
experiment is to minimize the time and inefficiency of the process, the first object of
the analysis is to determine the effect of the process parameters and to understand
the presence of interactions, if present. The types of analysis that can be done with
DOE’s include Pareto charts and normal probability plots which quickly display
what combination of factor is significant. Another way to evaluate the main effects
is whether p-values of the combinations is less than 0.05, if this is true it means the
factor or the combination of the factors are significant. Other plots available for
analysis are main effects plot which shows what effect changing one factor has the
response and interaction plot which shows what impact changing one factor has on
another factor that is kept unchanged. Figure 4.6 illustrate the main effect plots and
interactions plot. In order to determine statistical significance of both main
interaction effects, it is decided to construct normal probability plot of effect. The
detailed display descriptive statistics result of the DOE for the payroll process and
DOE analysis of variance (ANOVA) performance can be found in Appendix A.
Main Effects Plot for Hours
Interaction Plot for Hours
Data Means
Data Means
A
Current
New
56
A
55,5
B
55,0
Mean
Mean
55
54,5
54
54,0
53
53,5
A
B
Current
B
Normal Plot of the Standardized Effects
New
B
Pareto Chart of the Standardized Effects
(response is Hours, Alpha = ,20)
(response is Hours, Alpha = ,20)
99
1,337
Effect Type
Not Significant
Significant
90
Factor
A
B
B
B
B
Term
50
Name
A
A
B
Factor
Name
A
B
Percent
A
A
A
10
1
AB
-2
-1
0
1
Standardized Effect
2
0,0
0,5
1,0
Standardized Effect
1,5
2,0
20Figure 4.6 DOE of the payroll process
The following recommendations are proposed based on the analysis from the VOC
and VOP with use of DOE. There are certain unnecessary steps in the payroll
process, such as printing lengthy reports that were never used. The research work
encourage either not printing the reports at all, or printing them to an electronic file,
which took seconds, instead of hours. And also, the use of the new accounts
receivable technology that automatically transferred journals entries, instead of
requiring redundant data. For the payroll process, direct deposit is an important
opportunity to eliminate printing of payroll cheques. A payroll process is better to
38