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

i

An Enhanced Process Improvement Roadmap in Six Sigma

Methodology

研 究 生：艾明佳 Student: Mungunshagai Enkhbol

指導教授：陳啟光 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

ii

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.

iii

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

iv

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

v

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

vi

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

vii

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

1

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

2

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.

3

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

4

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.

5

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

6

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

7

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).

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).

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

1Figure 2.1: Sigma variation shown in normal curve (Itil &ITSM World, 2003)

2Figure 2.2: A process tends to shift 1.5 sigma units (Arnheiter, 2005)

8

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

9

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):

10

1Table 2: The DMAIC roadmap and the steps included in each phase

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

Phases Steps included

1, Define

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

2, Measure

Map process and identify process inputs and outputs

Establish measurement system capability

Establish data collection plan

3, Analyze

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

11

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 ().

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

3Figure 2.3 SIPOC diagram

12

OBSERVED PROCESS VARIATION

ACTUAL PROCESS VARIATION MEASUREMENT VARIATION

VARIATION DUE TO GAUGE VARIATION DUE TO OPERATOR

REPEATABILITY REPRODUCIBILIT

Y

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.

2.5.3 Analyze Phase

Kapur and Feng (2005) noted that the analyze phase examine the data collected in

4Figure 2.4 Possible source of variation (Kaushik and Khanduja, 2008)

13

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

14

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

15

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.

16

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

17

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

18

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

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,

Define

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)

Measure

Phase

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

Analyze

Phase

Gather data

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

Improve

Phase

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

19

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

20

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.

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).

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

CTS Process Factors Operational definition Metric Target

6Figure 3.2 VOP matrix template (Furterer, 2004)

5Figure 3.1 Comparing the VOP vs. the VOC (York, 2009)

21

on.

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

7Figure 3.3 Pareto Chart

22

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

23

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.

9Figure 3.5 DPMO representing a Six Sigma quality level, allowing 1.5 sigma shift

average

8Figure 3.4 Sigma to DPMO-conversion, assuming 1.5 sigma shift

24

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).

The variation factors needs to be identified for the characteristics of the process that

10Figure 3.6 DOE identification of the variation factors

25

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.

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.

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,

11Figure 3.7 DOE establishment of the performance baseline

12Figure 3.8 DOE process capability analysis and exposed defected variations for

improvement

26

the variation should be reduced within the specification limit as shown in Figure 3.9.

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.

13Figure 3.9 DOE optimized process variations and improved performance baseline

14Figure 3.10 DOE monitoring and verification procedure for optimized variations

27

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

28

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

29

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

30

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.

31

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

32

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

Process Estimated processing

time range

Estimated average

processing time Std. Dev. Cpk

Payroll 50 to 70 hours 60 hours 3.91937 0.84

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

33

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 Input Process Output Customer

-Payroll

Clerks

-Time reports -Payroll -Checks, reports,

taxes paid

-Employees

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.

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.

16Figure 4.2 Cause and effect diagram

34

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 Estimated average processing time Std. Dev. Cpk

Payroll 54 hours 2.5626 1.21

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

17Figure 4.3 Histogram of payroll process before (left) and after (right) improvement

35

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

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.

CTS Metric Data collection

mechanism

Analysis

mechanism Sampling plan

Cycle time Time to process

payroll

Track for two

payroll cycles Mean, range

Time for 2

payroll months

Accuracy of

the process

Type and number

of defects Check sheet Pareto chart

Defects for one

month

Customer

Satisfaction Employees Survey

Statistical

analysis

Survey company

units

36

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

Cycle time

-Standard procedure

exist

-Streamline processes

-training

-Volume of invoices

and paychecks

Measure each

process time

Paid on time

per schedule

Paid on

time

Accuracy of

the process

-Training in procedure

and software

Measure each

process and

defect types

Defects by

process type 100%

Customer

satisfaction

-Repeatable process

-Collect and assess

VOC

Measure

customer

satisfaction

through

customer

surveys

% of positive

responses for

identified

survey

question

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.

37

Resolution category

Percen

tage

DOE Graphical analysis of the payroll process

C AttributeB AttributeA Attribute

58

56

54

52

50

Pa

yro

ll 2

605550

65

60

55

605550

65

60

55

Afternoon

Pa

yro

ll 1

Evening

Morning

60585654525048

0,4

0,3

0,2

0,1

0,0

De

nsi

ty

54,71 2,984 7

52,86 2,734 7

55,67 1,033 6

Mean StDev N

A Attribute

B Attribute

C Attribute

Center 60585654525048

3,0

1,5

0,0

60585654525048

3,0

1,5

0,0

A Attribute

Fre

qu

en

cy

B Attribute

C Attribute

Mean 54,71

StDev 2,984

N 7

A Attribute

Mean 52,86

StDev 2,734

N 7

B Attribute

Mean 55,67

StDev 1,033

N 6

C Attribute

Individual Value Plot of Payroll process

Panel variable: Center

Scatterplot of Payroll process

Normal

Histogram of Payroll process Normal

Panel variable: Center

Histogram of Payroll process

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 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

18Figure 4.4 Pareto chart for information system problems

19Figure 4.5 DOE graphical analysis of the payroll process

38

BA

56

55

54

53

B

Me

an

Current

New

A

NewCurrent

55,5

55,0

54,5

54,0

53,5

BA

A

Me

an

B

210-1-2

99

90

50

10

1

Standardized Effect

Pe

rce

nt

A A

B B

Factor Name

Not Significant

Significant

Effect Type

AB

A

B

2,01,51,00,50,0

Te

rm

Standardized Effect

1,337

A A

B B

Factor Name

B

A

Interaction Plot for HoursData Means

Main Effects Plot for HoursData Means

Normal Plot of the Standardized Effects(response is Hours, Alpha = ,20)

Pareto Chart of the Standardized Effects(response is Hours, Alpha = ,20)

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.

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

20Figure 4.6 DOE of the payroll process

39

have direct deposit contest between company units to encourage use of the direct

deposit process. And finally, the research work recommends extensive information

technology improvements that further streamlined the process, and eliminate

redundant data entry.

Validate the feasibility of the process improvement ideas in the analysis with

DOE 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 28%.

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 43 hours, and

the process capability becomes Cpk=1.73 as shown in Figure 4.7. However, this is

not the Six Sigma required achievement, Six Sigma requires the Cpk=2. The next

section will present the Cpk required achievement by reanalyzing the process for

improvement.

4.4 The Second Analysis and Improvement of the Payroll Process

The result of the employee survey analysis is shown in Table 4.6. The areas of

opportunities for the payroll process are related to receiving friendly service.

8Table 4.6 Employee VOC survey results summary for reanalysis

Survey questions % negative

(1,2)

%positive

(3,4,5)

1. I receive my pay paycheck in a timely manner 80% 20%

21Figure 4.7 Improved process capability with enhanced improvement roadmap

54514845423936

LSL Target USL

LSL 35

Target 45

USL 55

Sample Mean 43,565

Sample N 20

StDev (Within) 1,65174

StDev (O v erall) 2,43425

Process Data

C p 2,02

C PL 1,73

C PU 2,31

C pk 1,73

Pp 1,37

PPL 1,17

PPU 1,57

Ppk 1,17

C pm 1,17

O v erall C apability

Potential (Within) C apability

PPM < LSL 0,00

PPM > USL 0,00

PPM Total 0,00

O bserv ed Performance

PPM < LSL 0,11

PPM > USL 0,00

PPM Total 0,11

Exp. Within Performance

PPM < LSL 216,96

PPM > USL 1,32

PPM Total 218,28

Exp. O v erall Performance

Within

Overall

Process capability of payroll process

40

2. I receive an accurate paycheck 15% 85%

3. If I call or see the payroll unit for service, I prompt

service 10% 90%

4. If I call or see the payroll unit for service, I receive

friendly service 80% 20%

5. If I call or see the payroll unit for service, my

problem gets solved completely at first time 55% 45%

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

revised VOP (Table 4.7). The accuracy of the process would be potentially impacted

by training in procedures and payroll software is targeted to minimize the variation

in the process. Because the purpose of reanalyzing the process is to center the

process distribution in order to achieve the Six Sigma requirement, Cpk=2.

Customer satisfaction could be impacted by whether there was a repeatable process

and whether the company would collect and analyze VOC information. The VOC

could be analyzed through measured surveys. This time 60 % of responses from the

survey targeted 4 and 5 for improvement.

9Table 4.7 Revised VOP matrix

CTS Process factors Operational

definition Metric Target

Cycle time

-Standard procedure exist

-Streamline processes

-training

-Volume of invoices and

paychecks

Measure each

process time

Paid on time

per schedule Paid on time

Accuracy of the

process

-Training in procedure

and software

Measure each

process and

defect types

Defects by

process type

No variation

where

assignable

cause cannot

be found

Customer

satisfaction

-Repeatable process

-Analyze the VOC

Measure

customer

satisfaction

through

customer

surveys

% of positive

responses for

identified

survey

question

60% of

responses are

rated 4 or 5 for

identified

questions

DOE further improve the time and inefficiency of the payroll process. After

41

5,02,50,0-2,5-5,0

99

90

50

10

1

Residual

Pe

rce

nt

44,544,043,543,042,5

4

2

0

-2

Fitted Value

Re

sid

ua

l

43210-1-2-3

4,8

3,6

2,4

1,2

0,0

Residual

Fre

qu

en

cy

2018161412108642

4

2

0

-2

Observation Order

Re

sid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for Hours

BA

44,5

44,0

43,5

43,0

42,5

B

Me

an

Current

New

A

NewCurrent

44,5

44,0

43,5

43,0

BA

A

Me

an

B

210-1-2

99

90

50

10

1

Standardized Effect

Pe

rce

nt

A A

B B

Factor Name

Not Significant

Significant

Effect Type

A

AB

B

1,51,00,50,0

Te

rm

Standardized Effect

1,337

A A

B B

Factor Name

Interaction Plot for HoursData Means

Main Effects Plot for HoursData Means

Normal Plot of the Standardized Effects(response is Hours, Alpha = ,05)

Pareto Chart of the Standardized Effects(response is Hours, Alpha = ,20)

reanalyzing and evaluating many potentially important factors, the study investigates

two factors that may improve the time and decrease the inefficiency of the payroll

process. The study is experimenting with a new process system to determine if it

will speed up process procedure. The payroll unit also has different procedures and

need to investigate which one is more efficient. The study conducts full factorial

DOE to find out which combination of factors results in the shortest time to process.

The result of experiment will help to make decisions about the process and improve

the process capability as required. Figure 4.8 shows the DOE probability analysis

and Figure 4.9 shows the DOE analysis of variance. The detailed display descriptive

statistics result of the DOE for the payroll process and DOE analysis of variance

performance can be found in Appendix B.

22Figure 4.8 DOE reanalysis for the payroll process

23Figure 4.9 DOE analysis of variation

42

39363330272421

LSL Target USL

LSL 20

Target 30

USL 40

Sample Mean 30,05

Sample N 20

StDev (Within) 1,63307

StDev (O v erall) 1,23438

Process Data

C p 2,04

C PL 2,05

C PU 2,03

C pk 2,03

Pp 2,70

PPL 2,71

PPU 2,69

Ppk 2,69

C pm 2,70

O v erall C apability

Potential (Within) C apability

PPM < LSL 0,00

PPM > USL 0,00

PPM Total 0,00

O bserv ed Performance

PPM < LSL 0,00

PPM > USL 0,00

PPM Total 0,00

Exp. Within Performance

PPM < LSL 0,00

PPM > USL 0,00

PPM Total 0,00

Exp. O v erall Performance

Within

Overall

Improved process capability of payroll process

The improvement idea is to reduce the batch size of the payroll process. This would

help the employees get their payments quicker by processing smaller batches more

frequently. This is also dependent upon other improvements for the payroll process,

so the batches could be process quickly. It is better to propose a daily batching,

instead of holding them for a week. This would increase the efficiency.

Validation of the feasibility of the process improvement ideas in the reanalysis

with DOE and implementation of the plan regarding the improvement suggestion for

the payroll process will be introduced. It was found that the payroll processing time

was reduced by 50%. 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 30 hours, and the process capability becomes Cpk=2.03 as shown in

Figure 4.10.

4.5 Summary

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 identified what are the current status of the process and its

capability, and then the application of the original DMAIC roadmap improved the

current process capability to a certain amount, Cpk=0.84 to Cpk=1.21 by

implementing the suggested recommendation to the payroll unit to develop

standardized process and procedure. But according to the Six Sigma requirement it’s

not the required result. Since the process capability hasn’t reached to required

24Figure 4.10 Improved process capability of payroll process with the

enhanced improvement roadmap

43

amount, the enhanced improvement roadmap applied to reach to the goal. The

application of the enhanced DMAIC roadmap completes the missing tasks of the

applied original DMAIC roadmap tasks. The improvement recommendation in this

step was either not printing the reports at all, or printing them to an electronic file,

which took seconds, instead of hours. After the enhanced roadmap application, the

process capability improved from Cpk=1.21 to Cpk=1.73. But the process capability

has to reach the required value. Implementing the second round of the analysis and

improvement will achieve the required goal. In this step, the improvement idea is to

reduce the batch size of the payroll process. This helped the employees get their

payments quicker by processing smaller batches more frequently. This is also

dependent upon other improvements for the payroll process, so the batches could be

process quickly. It is better to propose a daily batching, instead of holding them for a

week. This increased the efficiency. Finally, the process capability will improve

from Cpk=1.73 to Cpk=2.03 or more with the continued cycling of the proposed

improvement roadmap.

44

Chapter 5 Conclusions and Suggestion

5.1 Conclusions

This section presents the conclusions on the effectiveness of the proposed

improvement roadmap of the Six Sigma. 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 hypothetical case

study that is executable, appealing, and practical.

The Six Sigma methodology has been widely publicized in recent years as a

powerful methodology to combat quality-related problems and to achieve customer

satisfaction and process improvement. It has been considered as a strategic approach

to improve business probability and to achieve operational excellence through

effective application of both statistical and non-statistical tools/techniques. The

Six-Sigma methodology requires the process capability to be greater or equal to two,

Cpk≥2, for its successful application. The DMAIC improvement roadmap is the

most commonly used roadmap in Six Sigma after all. It is a Six Sigma method for

improvement of an existing process.

The present research work enhanced the original improvement roadmap by

significant statistical tools (VOP&DOE) with emphases on the process capability to

better insure improvement in order to solve the deficiencies that are introduced in

previous chapters. 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. Because in these 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. 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. 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 are defined and measured will help the

Design of Experiment (DOE) to analyze and improve the process capability and the

affected process variations. 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. DOE is a very powerful

analytical method where multiple process variables can be studied at the same time

with these efficient design, instead of in a hit and miss approach, proving very

reliable. The DOE optimizes the process, at first, minimizing variation by

45

maximizing the signal to noise ratios of the controllable factors that affect variation;

and second, selecting the levels of the tuning factors that affect the mean to adjust

the mean in the desired direction (toward the target value).

This research presented a case study illustrating the effective use of the

enhanced improvement roadmap to reduce waste in a continuous process. Particular

steps were taken during the application of the proposed roadmap to reach the

required process capability in Six Sigma. Through implementation of the enhanced

process improvement roadmap in Six Sigma methodology, the organization was able

to significantly reduce the time to payroll processing. Payroll processing time was

reduced by 50%. 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 30 hours, and the process capability becomes Cpk=2.03. Basically, with the

original DMAIC application the process capability improved from Cpk=0.84 to

Cpk=1.2 by implementing the suggested recommendation to the payroll unit to

develop standardized process and procedure, then the enhancements were applied to

improve the process capability as Six Sigma requires. The improvement

recommendation was either not printing the reports at all, or printing them to an

electronic file, which took seconds, instead of hours. It improved the process

capability from Cpk=1.2 to Cpk=1.7. The second analyses of the enhanced

improvement roadmap were conducted to improve the process capability and it

improved the process capability Cpk=1.7 to Cpk=2.03. In this step, the improvement

idea is to reduce the batch size of the payroll process. This helped the employees get

their payments quicker by processing smaller batches more frequently. This is also

dependent upon other improvements for the payroll process, so the batches could be

process quickly. It is better to propose a daily batching, instead of holding them for a

week. This increased the efficiency. The process capability improvement result by

orders is shown in Table 5.

10Table 5: Payroll process improvements result

Payroll Process Average processing time Cpk

Initial result 60 hours 0.84

Improved result with original DMAIC 54 hours 1.21

Improved result with an enhanced roadmap 43 hours 1.73

Improved result with the second analysis of

the enhanced roadmap 30 hours 2.03

5.2 Suggestion

An enhanced improvement roadmap of the Six Sigma methodology provides an

46

excellent way to improve the process and quality of the organization. In the face of

overwhelming claims about the merits and power of enhanced improvement

roadmap of Six Sigma, the fact remains that it is most effective when an

organization already has a firm idea of what forms of products and services are in

alignment with the organization’s goals and customer expectations. It is suited to

problems where output can be readily measured.

This research is very helpful for the research work or case study that adopt the

Six Sigma DMAIC roadmap to their process improvement but didn’t reach the

required process capability Cpk=2. By applying the proposed enhanced process

improvement roadmap to the unreached implementation with the original DMAIC

roadmap the required Six Sigma goal can be reached. The outputs of this research

are the statistical tools that could be used at any stage of the Six Sigma

implementation. The enhancement of the statistical tools has the capability to

communicate among current and former project team members within any group,

division, or facility within the organization. By way of the enhancement and the data

analysis capability, the statistical tools implemented to this research will give

managers powerful tools that will help in their quest to achieve the Six Sigma goal.

An enhanced improvement roadmap of the Six Sigma methodology can be applied

to almost all every day process ranging from the manufacturing industry to service,

transactional, administrative, R&D, sales and marketing, healthcare, and

software-development industries.

47

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51

Appendix A –Full Factorial Design of Experiment for

Payroll Process with Enhanced Improvement Roadmap

Full Factorial Design

Factors: 2 Base Designs: 2; 4

Runs: 20 Replicates: 5

Blocks: 1 Center pts. (Total): 0

All terms are free from aliasing.

Factorial Fit: Hours versus A; B

Estimated Effects and Coefficients for Hours (coded units)

Term Effect Coef SE Coef T P

Constant 54. 3500 0. 5420 100. 28 0.000

A -1. 9000 -0. 9500 0. 5420 -1. 75 0.099

B 1. 9000 0. 9500 0. 5420 1. 75 0.099

A*B -0. 3000 -0. 1500 0. 5420 -0. 28 0.786

S = 2. 42384 PRESS = 146.875

R-Sq = 28. 00% R-Sq (pred) = 0. 00% R-Sq (adj) = 14. 50%

Analysis of Variance for Hours (coded units)

Source DF Seq SS Adj SS Adj MS F P

Main Effects 2 36.100 36. 1000 18. 0500 3. 07 0.074

2-Way Interactions 1 0.450 0. 4500 0. 4500 0. 08 0.786

Residual Error 16 94.000 94. 0000 5. 8750

Pure Error 16 94.000 94. 0000 5. 8750

Total 19 130.550

Estimated Coefficients for Hours using data in uncoded units

Term Coef

Constant 54. 3500

A -0. 950000

B 0.950000

A*B -0. 150000

One-way ANOVA: Payroll Process versus Center

Source DF SS MS F P

Center 2 26. 93 13. 47 2. 21 0.140

Error 17 103. 62 6. 10

52

C AttributeB AttributeA Attribute

58

56

54

52

50

Center

Pa

yro

ll P

ro

ce

ss

C AttributeB AttributeA Attribute

58

56

54

52

50

Center

Pa

yro

ll P

ro

ce

ss

Individual Value Plot of Payroll Process vs Center Boxplot of Payroll Process

Total 19 130. 55

S = 2.469 R-Sq = 20. 63% R-Sq(adj) = 11.29%

Individual 95% CIs for Mean Based on

Pooled StDev

Level N Mean StDev ------+---------+---------+---------+---

A Attribute 7 54.714 2.984 (---------*--------)

B Attribute 7 52.857 2.734 (---------*---------)

C Attribute 6 55.667 1.033 (---------*----------)

------+---------+---------+---------+---

52. 0 54. 0 56. 0 58. 0

Pooled StDev = 2.469

Tukey 95% Simultaneous Confidence Intervals

All Pairwise Comparisons among Levels of Center

Individual confidence level = 98. 00%

Center = A Attribute subtracted from:

Center Lower Center Upper --------+---------+---------+---------+-

B Attribute -5.244 -1.857 1.530 (---------*--------)

C Attribute -2.573 0.952 4.478 (---------*---------)

--------+---------+---------+---------+-

-3. 5 0. 0 3. 5 7. 0

Center = B Attribute subtracted from:

Center Lower Center Upper --------+---------+---------+---------+-

C Attribute -0.716 2.810 6.335 (---------*---------)

--------+---------+---------+---------+-

-3. 5 0. 0 3. 5 7. 0

53

5,02,50,0-2,5-5,0

99

90

50

10

1

Residual

Pe

rce

nt

56555453

5,0

2,5

0,0

-2,5

-5,0

Fitted Value

Re

sid

ua

l420-2-4

8

6

4

2

0

Residual

Fre

qu

en

cy

2018161412108642

5,0

2,5

0,0

-2,5

-5,0

Observation Order

Re

sid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for Payroll Process

54

Appendix B –Full Factorial Design of Experiment for

Payroll Process, the Second Analysis and Improvement

11Table A2: Data for the Second full factorial DOE

Labels Payroll

Hours Labels

Payroll

Hours Labels

Payroll

Hours Labels

Payroll

Hours

1 41.0000 6 41.5000 11 43.5000 16 42.0000

2 43.0000 7 41.9000 12 43.0000 17 45.0000

3 40.3000 8 42.3000 13 44.3000 18 47.0000

4 40.7000 9 48.0000 14 44.7000 19 48.0000

5 41.1000 10 42.0000 15 46.0000 20 46.0000

Full Factorial Design

Factors: 2 Base Designs: 2; 4

Runs: 20 Replicates: 5

Blocks: 1 Center pts (total): 0

All terms are free from aliasing.

Factorial Fit: Hours versus A; B

Estimated Effects and Coefficients for Hours (coded units)

Term Effect Coef SE Coef T P

Constant 43. 5650 0. 5605 77. 73 0.000

A 0. 1900 0. 0950 0. 5605 0. 17 0.868

B -1. 5100 -0. 7550 0. 5605 -1. 35 0.197

A*B 0. 3100 0. 1550 0. 5605 0. 28 0.786

S = 2. 50654 PRESS = 157.069

R-Sq = 10. 71% R-Sq(pred) = 0.00% R-Sq(adj) = 0.00%

Analysis of Variance for Hours (coded units)

Source DF Seq SS Adj SS Adj MS F P

Main Effects 2 11.581 11.581 5. 7905 0. 92 0.418

2-Way Interactions 1 0.480 0.480 0. 4805 0. 08 0.786

Residual Error 16 100.524 100.524 6. 2828

Pure Error 16 100.524 100.524 6. 2828

Total 19 112.586

Estimated Coefficients for Hours using data in uncoded units

Term Coef

55

Constant 43. 5650

A 0.095000

B -0. 755000

A*B 0.155000

One-way ANOVA: Payroll 3 versus Center

Source DF SS MS F P

Center 2 61. 78 30. 89 10. 34 0.001

Error 17 50. 80 2. 99

Total 19 112. 59

S = 1.729 R-Sq = 54. 87% R-Sq (adj) = 49. 57%

Level N Mean StDev

A Attribute 7 41.357 0.890

B Attribute 7 43.971 2.030

C Attribute 6 45.667 2.066

Individual 95% CIs for Mean Based on Pooled StDev

Level +---------+---------+---------+---------

A Attribute (------*------)

B Attribute (------*------)

C Attribute (------*-------)

+---------+---------+---------+---------

40. 0 42. 0 44. 0 46. 0

Pooled StDev = 1.729

Tukey 95% Simultaneous Confidence Intervals

All Pairwise Comparisons among Levels of Center

Individual confidence level = 98. 00%

Center = A Attribute subtracted from:

Center Lower Center Upper ----+---------+---------+---------+-----

B Attribute 0.242 2.614 4.986 (-------*-------)

C Attribute 1.841 4.310 6.778 (-------*--------)

----+---------+---------+---------+-----

-3. 0 0. 0 3. 0 6. 0

Center = B Attribute subtracted from:

Center Lower Center Upper ----+---------+---------+---------+-----

C Attribute -0.773 1.695 4.164 (--------*-------)

----+---------+---------+---------+-----

-3. 0 0. 0 3. 0 6. 0

56

C AttributeB AttributeA Attribute

48

46

44

42

40

Center

Pa

yro

ll 3

C AttributeB AttributeA Attribute

48

46

44

42

40

Center

Pa

yro

ll 3

Boxplot of Payroll 3 Individual Value Plot of Payroll 3 vs Center