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Achieving Quality Improvement in the
Mask Manufacturing Industry by Using
Six Sigma Technique
Submitted to:
Science and Engineering Faculty
School of Chemistry, Physics and Mechanical Engineering
Queensland University of Technology
Submitted by: Wei-Fen Chiu
Research student
Queensland University of Technology
4th April 2012
I
Acknowledgements
Time flies, and the life of researching seems to be a challengeable but impressive
journey. I had a great time within the period of time since I have not only absorbed
and comprehended more in the particular area of knowledge but made friends with
some wonderful people who helped and supported me to accomplish my thesis.
First of all, I would like to offer my gratitude to my three supervisors Associate
Professor YuanTong Gu, Dr Azharul Karim and Professor Lin Ma. Thank you for
supporting and believing in me from beginning to end with your passion and
dedication. I also wish to thank you for always encouraging me to express my ideas
into my thesis with constructive feedback and positive praise. I am delighted with
having a good relationship with these two supervisors. They are not only my
supervisors but also my good friends inasmuch as they let me have absolute liberty
during the time and we would chat about everything like friends.
Secondly, I would like to acknowledge my lovely parents, Shaw-Kou Chiu and Pao-
Chao Yu, and my three sisters, who are Wei-Yi Chiu, Wei-Hsuan Chiu, and Wei-Chih
Chiu. I appreciate them supporting and encouraging me spiritually and practically
with their constant love and wisdom. To satisfy my material requirements, Dad has
been working very hard overseas, and thereby, Mom has been flying laboriously
between two countries every two months in order to take care of us physically and
psychologically. Thank you for my three beautiful sisters who make my research life
interesting and happy with their smiles and thoughtfulness.
Thirdly, I would like to thank my friends in the research office. Thank you for
providing considerable and useful information and generous friendships. It is my
fortune to have met all my excellent researching friends. Finally, thank you
Queensland University of Technology for providing a marvellous researching
environment and also the staff at the Research Support Office for always helping me
when I needed it.
II
Abstract
The Six Sigma technique is one of the quality management strategies and is utilised for improving the quality and productivity in the manufacturing process. It is inspired by the two major project methodologies of Deming’s “Plan – Do – Check – Act (PDCA)” Cycle which consists of DMAIC and DMADV. Those two methodologies are comprised of five phases. The DMAIC project methodology will be comprehensively used in this research. In brief, DMAIC is utilised for improving the existing manufacturing process and it involves the phases Define, Measure, Analyse, Improve, and Control. Mask industry has become a significant industry in today’s society since the outbreak of some serious diseases such as the Severe Acute Respiratory Syndrome (SARS), bird flu, influenza, swine flu and hay fever. Protecting the respiratory system, then, has become the fundamental requirement for preventing respiratory deceases. Mask is the most appropriate and protective product inasmuch as it is effective in protecting the respiratory tract and resisting the virus infection through air. In order to satisfy various customers’ requirements, thousands of mask products are designed in the market. Moreover, masks are also widely used in industries including medical industries, semi-conductor industries, food industries, traditional manufacturing, and metal industries. Notwithstanding the quality of masks have become the prioritisations since they are used to prevent dangerous diseases and safeguard people, the quality improvement technique are of very high significance in mask industry. The purpose of this research project is firstly to investigate the current quality control practices in a mask industry, then, to explore the feasibility of using Six Sigma technique in that industry, and finally, to implement the Six Sigma technique in the case company to develop and evaluate the product quality process. This research mainly investigates the quality problems of musk industry and effectiveness of six sigma technique in musk industry with the United Excel Enterprise Corporation (UEE) Company as a case company. The DMAIC project methodology in the Six Sigma technique is adopted and developed in this research. This research makes significant contribution to knowledge. The main results contribute to the discovering the root causes of quality problems in a mask industry. Secondly, the company was able to increase not only acceptance rate but quality level by utilising the Six Sigma technique. Hence, utilising the Six Sigma technique could increase the production capacity of the company. Third, the Six Sigma technique is necessary to be extensively modified to improve the quality control in the mask industry. The impact of the Six Sigma technique on the overall performance in the business organisation should be further explored in future research.
III
Certification of Thesis The work contained in this thesis has not been previously submitted for a degree or
diploma at any other higher education institution. To the best of my knowledge and
belief, this thesis contains no material previously published or written by another
person except where due reference is made.
Wei-Fen Chiu 4th April 2012
IV
PREFACE During the course of this project, one journal paper has been published and another journal paper is being submitted for publication. They are listed here for reference. JOURNAL PUBLICATIONS
1. WeiFen Chiu, YuanTong Gu, M.A.Karim and Lin MA, A modified quality control method for manufacturing process in mask industry, Advanced Materials Research Vols. 139-141 (2010) pp 1843-1846 (ERA ranking –B)
JOURNAL PAPER UNDER PREPERATION
2. WeiFen Chiu, YuanTong Gu, M.A.Karim and Lin MA, Improving Quality Control methodology in the Mask Industry by implementing the Six Sigma Technique, Advanced Materials Research (ERA ranking –B)
3. WeiFen Chiu, YuanTong Gu, M.A.Karim and Lin MA, The Enhanced Quality
Control for Six Sigma Technique in Mask Industry, publish with InTech in the book project under the working title "Manufacturing System"
V
Contents
Page Number
1. THESIS TITLE ....................................................................................................... IX
2. PROJECT SUPERVISORS ................................................................................... IX
CHAPTER 1 INTRODUCTION ................................................................................ - 1 -
1.1 RESEARCH FRAMEWORK AND BACKGROUND ...................................................... - 1 - 1.2 PROBLEM STATEMENT, RESEARCH QUESTION AND RESEARCH OBJECTIVE .................... - 2 - 1.3 RESEARCH METHOD .................................................................................... - 5 - 1.4 OUTLINE OF THIS THESIS .............................................................................. - 6 -
CHAPTER 2 LITERATURE REVIEW ......................................................................... - 8 -
2.1 THE HISTORY OF THE SIX SIGMA TECHNIQUE ..................................................... - 8 - 2.1.1 The Six Sigma technique in practise................................................... - 9 -
2.2 THE QUALITY MANAGEMENT SYSTEMS ............................................................ - 12 - 2.2.1 Total Quality Management (TQM) .................................................. - 12 - 2.2.2 The difference between the Six Sigma technique and the Total Quality Management (TQM) ................................................................................... - 13 - 2.2.3 Basics for Six Sigma technique ........................................................ - 15 - 2.2.4 The Six Sigma technique principles .................................................. - 17 -
2.3 THE SIX SIGMA TECHNIQUE METHODS ........................................................... - 18 - 2.3.1 The DMAIC method for the Six Sigma technique.............................. - 18 - 2.3.2 The DMADV method for the Six Sigma technique ............................ - 19 - 2.3.3 The Comparison between two methods .......................................... - 20 -
2.4 IMPLEMENTATION ROLES FOR THE SIX SIGMA TECHNIQUE ................................... - 21 - 2.5 USEFUL TOOLS AND METHODOLOGIES FOR THE SIX SIGMA TECHNIQUE ................... - 24 -
2.5.1 Failure Mode and Effects Analysis (FMEA) ....................................... - 24 - 2.5.2 Fault Tree Analysis (FTA) ................................................................. - 25 - 2.5.3 Flow Chart ...................................................................................... - 26 - 2.5.4 Histogram ....................................................................................... - 27 - 2.5.5 Pareto Diagrams ............................................................................. - 28 - 2.5.6 Cause and Effect Diagrams ............................................................. - 29 - 2.5.7 Control Chart .................................................................................. - 30 -
2.6 METHODS FOR OBTAINING THE DATA ............................................................. - 31 - 2.7 THE SIX SIGMA TECHNIQUE IN MASK INDUSTRY ................................................ - 34 - 2.8 CONCLUSION ........................................................................................... - 35 -
CHAPTER 3 QUALITY PROBLEMS IN THE MASK INDUSTRY – A CASE STUDY ...... - 36 -
3.1 INTRODUCTION ........................................................................................ - 36 - 3.2 COMPANY BACKGROUND ............................................................................ - 36 -
3.2.1 Product Background........................................................................ - 37 - 3.3 PRODUCTION PROCESS IN CASE ORGANISATION ................................................ - 39 - 3.4 QUALITY CONTROL IN UEE .......................................................................... - 50 -
3.4.1 Quality control issues ...................................................................... - 50 -
VI
CHAPTER 4 ROOT CAUSES OF QUALITY PROBLEMS IN CASE ORGANISATION .... - 54 -
4.1 INTRODUCTION ........................................................................................ - 54 - 4.2 SURVEY OF UEE MANAGEMENT AND EMPLOYEES ............................................. - 56 - 4.3 USE OF SIX SIGMA TOOLS TO IDENTIFY CAUSES OF QUALITY PROBLEMS .................... - 58 -
4.3.1 Cause and effect diagram ............................................................... - 59 - 4.3.2 Pareto chart .................................................................................... - 61 -
4.4 PRODUCTION DATA ANALYSIS ....................................................................... - 63 - 4.5 CONCLUSION ........................................................................................... - 73 -
CHAPTER 5 IMPROVING QUALITY USING THE SIX SIGMA TECHNIQUE .............. - 74 -
5.1 EMPIRICAL FINDINGS ................................................................................. - 74 - 5.2 STEP OF IMPLEMENTATION THE SIX SIGMA TECHNIQUE ....................................... - 76 - 5.3 THE SIX SIGMA TEAM IN THE UNITED EXCEL ENTERPRISE (UEE) CORPORATION........ - 78 - 5.4 RESULTS OF CASE IMPROVEMENT .................................................................. - 80 - 5.5 SUMMARY .............................................................................................. - 89 -
CHAPTER 6 CONCLUSION................................................................................... - 90 -
6.1 SUMMARY OF THE RESEARCH ....................................................................... - 90 - 6.2 CONCLUSIONS ABOUT RESEARCH QUESTIONS ................................................... - 93 - 6.3 CONCLUSIONS REGARDING THE RESEARCH PROBLEM .......................................... - 96 - 6.4 RESEARCH EVALUATION FOR THE MASK INDUSTRY .............................................. - 98 - 6.5 RESEARCH LIMITATIONS .............................................................................. - 99 - 6.6 RECOMMENDATION AND FUTURE RESEARCH .................................................. - 100 -
REFERENCES .................................................................................................... - 102 -
APPENDIX A - THE SYMBOL OF MASK PRODUCTION ....................................... - 113 -
APPENDIX B - SAMPLING CONTROL METHOD ................................................. - 115 -
APPENDIX C - SAMPLE OF INTERVIEWS ........................................................... - 116 -
VII
List of Figures
Page Number
Figure 1: The six sigma diagram ................................................................ - 17 -
Figure 2: DMAIC cycle ............................................................................... - 19 -
Figure 3: DMADV cycle ............................................................................. - 20 -
Figure 4: Levels of roles ............................................................................ - 23 -
Figure 5: FTA symbols ............................................................................... - 26 -
Figure 6: Flow chart symbols .................................................................... - 27 -
Figure 7: Example of histogram................................................................. - 28 -
Figure 8: Example for Pareto Diagram ........................................................ - 29 -
Figure 9: Example for Cause and Effect Diagram ....................................... - 30 -
Figure 10: Example of a Control Chart ........................................................ - 31 -
Figure 11: raw material Input process.......................................................... - 41 -
Figure 12: The process linking the company with its customers ................... - 42 -
Figure 13: Simplified depiction of output process ........................................ - 43 -
Figure 14: The process between purchase department and customers........ - 45 -
Figure 15: The whole production process for the mask company ................ - 47 -
Figure 16: Process for manufacturing masks ................................................ - 49 -
Figure 17: Theoretical Model for this thesis ................................................. - 55 -
Figure 18: Fishbone diagram for identifying defective products. .................. - 60 -
Figure 19: A Pareto chart of the main causes of defects............................... - 62 -
Figure 20: The p chart for finished goods in July 2009 ................................. - 68 -
Figure 21: The p chart for semifinished goods in July 2009 .......................... - 69 -
Figure 22: The P chart of total production in July 2009. ............................... - 72 -
Figure 23: Empirical Findings and Analysis ................................................... - 75 -
Figure 24: The lifecycle for implementing the Six Sigma technique .............. - 76 -
Figure 25: The Six Sigma deployment model ............................................... - 77 -
Figure 26: The p values for finished goods after improvement..................... - 83 -
Figure 27: The semi finished goods data after improvement. ...................... - 84 -
Figure 28: The total goods after improvement ............................................. - 85 -
VIII
List of Table
Page Number
Table 1: The sigma scale ............................................................................... - 16 -
Table 2: Comparison of DMAIC and DMADV................................................ - 21 -
Table 3: FMEA calculation diagram............................................................... - 24 -
Table 4: The classification of quality level for product quality. ..................... - 44 -
Table 5: Weekly data for finished goods in July 2009 .................................... - 64 -
Table 6: Weekly data for semifinished goods in July 2009 ............................ - 64 -
Table 7: The proportion of finished goods in July 2009 ................................. - 65 -
Table 8: The proportion of semifinished goods in July 2009 ......................... - 66 -
Table 9: The CL, UCL and LCL for finished goods in July 2009. ....................... - 67 -
Table 10: The CL, UCL and LCL for semifinished goods in July 2009. ............ - 68 -
Table 11: Summary of July production in 2009 ........................................... - 71 -
Table 12: The finished goods after improvement in July 2010 .................... - 81 -
Table 13: The semi finished goods after improvement in 2010. .................. - 81 -
Table 14: Summary of production after improvement in July of 2010 ........ - 86 -
Table 15: Comparison of total goods data .................................................. - 87 -
Table 16: The comparison for the case study. ............................................. - 88 -
Table 17: Summary of results in the case ................................................... - 92 -
IX
1. Thesis Title
Achieving Quality Improvement in the Mask Manufacturing Industry by Using the
Six Sigma Technique
2. Project Supervisors
Principal Supervisor: Associate Professor YuanTong Gu
School of Engineering Systems
Faculty of Built Environment and Engineering
Queensland University of Technology (QUT)
Associate Supervisor: Dr. Azharul Karim
School of Engineering Systems
Faculty of Built Environment and Engineering
Queensland University of Technology (QUT)
Associate Supervisor: Professor Lin Ma
School of Engineering Systems
Faculty of Built Environment and Engineering
Queensland University of Technology (QUT)
- 1 -
CHAPTER 1 INTRODUCTION
In this chapter, the research framework is discussed first. The research problem,
research question and research objective are then explained. The next section
presents the research methodology. The outline of this thesis is given at the end of
chapter.
1.1 Research framework and background
For industry, quality has been an essential issue since World War II, and therefore,
improving quality has become an important business tactic for many organisations
including those involved in manufacturing, distribution, transportation, financial
services , health care, and government (Amasaka, 2000; Wienclaw, 2008c). In
engineering and manufacturing organisation, quality control and quality
management techniques are used to ensure products or services meet or exceed
customer requirements.
The most important factor affecting a business’s performance is the quality of its
products and related services. Companies with superior quality products are more
competitive and are likely to have a larger market share (Azis & Osada, 2010).
Gradually, the demand for higher quality products is increasing because of a
competitive environment and rapidly improving technologies (Anil, Joe, & Jean,
2009).
Quality products need to be made economically so that they can compete in the
market. End products or services need to meet or exceed company goals (McCuiston
& DeLucenay, 2010). Producing high-quality products is also a competitive tool that
can result in considerable advantage to organisations. A business that can delight
customers by improving and controlling quality has the potential to dominate its
competitors. Developing an effective quality strategy is a factor in long-term
business success (Mast, 2004; Mast, Schippers, Does, & Heuvel, 2000).
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The mask manufacturing industry has become an important sector due to the
spread of diseases like Severe Acute Respiratory Syndrome (SARS), bird flu, swine flu
and influenza. Covering the mouth is based on the need to ensure the prevention of
respiratory diseases (Centre for Disease Control, 2011; Organization, 2011). Masks
have been widely utilised both in industrial and domestic environments. In industry,
the product is essential for employees who perform tasks in environments which
involve potential hazards from inhaling harmful substances. Types of masks differ in
the materials they are made from, and in techniques of manufacturing. Producing
appropriate quality masks for customers helps protect people’s health.
The applications for different types of masks can number in thousands. Clients need
to choose the masks which are most appropriate to their working environments. For
example, employees who work in hospitals select masks with high chemical and
bacterial resistance, whereas for workers on construction sites, need masks with
high protection from dust are needed.
Quality control is a key concern in mask industry. In recent decades, many types of
quality control methodologies have been developed, investigated and implemented.
They include the Seven basic Quality Tools, Total Quality Management (TQM), the
International Standards Organization (ISO) documentation, Statistical Process
Control (SPC), lean manufacturing, just in time (JIT), quality function deployment
(QFD) and the Six Sigma technique (Wienclaw, 2008b). However, many of these
tools, particularly six sigma techniques have not been used in musk industry.
This research will investigate the quality control methodologies used in the mask
manufacturing industry.
1.2 Problem statement, research question and research objective
As discussed before, the purpose of quality control tools is to support the
manufacturing process, improve product quality and reduce the numbers of product
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defects. Quality control is an important element in manufacturing management
(Wienclaw, 2008e). To choose and utilise good quality control tools is an important
task for businesses and manufacturing managers.
In the recent time several quality control (QC) techniques and tools have been
developed and applied. These techniques include Seven basic Quality Tools, Total
Quality Management (TQM), the International Standards Organization (ISO)
documentation, Statistical Process Control (SPC), lean manufacturing, just in time
(JIT), quality function deployment (QFD) and the Six Sigma technique. The ultimate
goal of these tools is to improve operational performance and obtain higher
customer satisfaction (Jones, Parast, & Adams, 2010; Moosa & Sajid, 2010).
The Six Sigma technique is one of quality management strategy and is utilised
improving the productivity and the profitability in the manufacturing process. Sigma
(σ) is original from Greek letter which is a symbol of standard deviation in the
statistical analysis (Ayad, 2010). However, it represents the variability level of
products and the process of observation in the six sigma technique. Specifically, the
maximum number of effects is 3.4 per million opportunities at Six Sigma level and
the higher level of sigma represents the lower level of defective goods (Ayad, 2010;
Kumar, Saranga, Ramírez-Márquez, & Nowicki, 2007).
The Six Sigma management program is a project framework and it involves two
possible approaches (Ali, 2005; Jones, 2004). One is DMAIC which stands for “define
measure, analyse, improve and control”. Another is DMADV which stands for “define,
measure, analyse, design and verify”.
Majority of the Mask Industries are still using the traditional quality control
methodologies to minimise quality problems. For example, the total examination
and the random inspection are the two common quality control methodologies in
the Mask Industry. However, some manufacturing managers in the Mask Industry
are facing quality problems mainly because of the traditional quality control
- 4 -
techniques. Therefore, selecting a appropriate quality control technique is the prime
priority for those manufacturing managers in mask industry in today’s society.
Quality strategies in mask industry have not been thoroughly investigated in the
past owing to the mask industry is an emerging but burgeoning manufacturing
industry in the market and therefore right quality technique for the industry has not
been identified. Although six sigma technique has been successfully applied in many
industries, it has not been implemented in mask industry. Therefore, the purpose of
this thesis is to address the research problem:
Is the Six Sigma technique an appropriate quality control methodology to improve
the entire performance in the mask industry?
To answer the research question, the following research questions were designed to
investigate and evaluate the performance of the six sigma technique in the mask
industry as flows:
Research question 1: What is the quality control (QC) process in a mask company?
Research question 2: What are the possible root causes of defective products?
Research question 3: How could these root causes be addressed?
Research question 4: What quality control tools and software packages are used in
the mask industry?
Therefore, the main objective of this research is to address the research questions
listed above and the ultimate goal is to investigate the use and effectiveness of the
traditional quality control method in mask industry, identify a higher performing
quality control tool and apply this tool to a mask company. Specifically, this research
will investigate and apply the Six Sigma technique and identify a suitable statistical
software tool and apply it to the mask industry.
- 5 -
The outcomes of the project will pave the way for modifications of the quality
control tool used in an actual case. In this research, the United Excel Enterprise
Corporation (UEE) was selected as the real case organisation.
The case study was selected as the most appropriate technique to collect primary
data in this thesis regarding the research questions defined in the earlier..
1.3 Research method
A number of researchers have discussed empirical research methodology in
operations management. Reid and Sander(Reid & Sanders, 2005) proposed a
systematic approach to conducting empirical research. They suggested that one
method, or a combination of several data collection methods, should be used in
conjunction with the research design.
In this study, the research problem was firstly emphasised from the literature and
an in-depth case study. It has been suggested in the literature that case studies can
be applied to the area of theory development as well as problem solving (Creswell,
2008; Ponterotto, 2005). In general, case studies are often preferred when
researchers have little control over the event and when the focus is on a
contemporary phenomenon in some real life context(Cavana, Delahaye, & Sekaran,
2001; Reid & Sanders, 2005). The case study method was selected after careful
consideration of several issues.
First, one key aim of the study is to empirically identify quality related difficulties in
mask industry. Manufacturing takes place in a complex environment. Hence, it is
critical to capture the experiences of the relevant people and the context of their
actions to better understand quality practices and related difficulties. Case studies
are particularly suitable for identifying the difficulties. Second, as the research deals
with the difficulties and challenges mask manufacturers are currently facing, this
research deals with a contemporary event(Edmondson & Mcmanus, 2007;
- 6 -
Ponterotto, 2005). Third, as this study investigates in detail the quality practices in
its real life settings, no control over the behaviour of the organisation within the
plant is possible.
This research aims to identify root causes of quality problems and suggest a quality
improvement method for mask industry. Case study was conducted to identify the
root causes of quality problem, to investigate the suitability of six sigma technique
and suggest a quality control methodology for mask industry.
1.4 Outline of this thesis
This thesis comprises six chapters to develop the knowledge of improving the
quality in the Mask Manufacturing Industry by using the Six Sigma technique with
case study analysis. The chapter are summarised as follows:
Chapter 1 introduces the overall picture of this study. To begin with, the research
framework and background were introduced, and the research question and
research objective were identified after that. Chapter one also outlines the research
methodology and research classification for this study.
Chapter 2 particularises the Six Sigma technique from both theoretical and practical
perspectives. The history of the Six Sigma technique is firstly presented with
empirical literature. The principles and the methods of the Six Sigma technique then
are discussed later in this chapter.
Chapter 3 addresses the quality problems in the Mask Industry by analysing chosen
company, the United Excel Enterprise Corporation (UEE), as a case study in this
research. The research objectives and research questions are defined the following
explanation of mask industry in Taiwan.
Chapter 4 describes the research methodology in this research. In this chapter, the
- 7 -
research problems are attempted to be explained by using those Six Sigma
techniques with data analysis.
Chapter 5 summarises the findings of this research. Chapter 5 discusses the
requirements for improving quality control and also illustrates the implementation
and evaluation of the Six Sigma method.
Chapter 6 concludes those results in this study. The major implication for future
research is recommended at the end of this research.
- 8 -
Chapter 2 Literature Review
2.1 The history of the Six Sigma technique
Since the 1980s, applying statistical methods for quality control and overall business
improvement have grown rapidly not only in the United States but all over the world
(Antony & Banuelas Coronado, 2002). This was motivated, in part, by the
widespread loss of business and markets suffered by many US companies that
began during the 1970s. For example, the US automobile industry was nearly
destroyed by international competition during this period. One US automobile
company estimated its operating losses at nearly $1 million per hour in 1980
(Antony & Banuelas Coronado, 2002; Caulcutt, 2001). The adoption and use of
statistical methods with respect to quality have played a central role in the renewed
competitiveness of US industry.
The Six Sigma technique was first used in the 1980s at Motorola. In 1983, Bill Smith
who is a reliability engineer concluded that inspections and tests were not detecting
all product defects. Customers were finding defects and defects causing products to
fail (Zu, Fredendall, & Douglas, 2008). Since process failure rates were much higher
than the indication from final product tests, Smith decided that the best way to
solve the problem of defects was to improve the processes and to reduce or
eliminate the possibility of defects in the first place (Barney & McCarty, 2002). The
CEO of Motorola, Bob Galvin, was impressed by the early successes Smith achieved.
Therefore, Motorola began to apply the Six Sigma technique across the organisation
and to focus on manufacturing processes and systems (He, 2008).
Motorola established Six Sigma as both an objective for the corporation and as a
focal point for process and product quality improvement efforts. The Six Sigma
concept was tremendously successful at Motorola. It has been estimated that
Motorola reduced defects in semiconductor devices by 94% between 1987 and
- 9 -
1993 (Wienclaw, 2008a; Zhang, Hill, & Gilbreath, 2011). In recent years, Six Sigma
has spread beyond Motorola and has become a program for improving corporate
business performance by improving quality, reducing costs and expanding markets
for products and services. The Six Sigma technique has been adopted by thousands
of companies both large and small in scale.
2.1.1 The Six Sigma technique in practise
The Motorola Company first used the Six Sigma technique in 1987 and the Six Sigma
technique is now accepted and utilised in several famous companies, for example,
GE (the General Electric Company), Allied Signal, Philips Electronics, Sony and
Samsung (Montgomery & Woodall, 2008). The application of the Six Sigma
technique has helped global enterprises to save over a billion US dollars and it has
brought about remarkable improvements in enterprise management (Djurdjanovic
& Ni, 2003).
The Six Sigma technique brings the following benefits to businesses (Desai &
Shrivastava, 2008; George, 2003; Gygi, Williams, & Gustafson, 2005):
1). It can reduce the production cycle time and percentage of defective units.
2). It can increase productivity and product reliability.
3). It can enhance customer satisfaction, quality of employees and quality of
products.
4). It can also improve production capacity, outcomes and operation
processes.
- 10 -
On the other hand, using Six Sigma has two main disadvantages:
1). It will use up resources and time.
2). The company needs to invest an adequate amount of its budget for the
project at the outset.
Since data collection and analysis has become more important, there are some
famous software packages available for researchers. For instance, the Minitab,
Microsoft Excel and Sigma Work are widely implemented. These software packages
have some features including the statistical methods, statistical chart tools and
project management (Biehl, 2004; Redzic & Baik, 2006). Moreover, general users
find them easy to understand and utilise.
The Six Sigma technique has three powerful interconnected features (Connaughton,
2005a; Costello, Molloy, Lyons, & Duggan, 2005; Tayntor, 2007).
1). The executive leadership must choose a topic which is related with
company’s profit. Before beginning to use Six Sigma, the financial
department will select an area where there is potential for the greatest
amount of improvement.
2). A Black Belt (BB) employee should guide this project team so that the
company can execute and accomplish the project.
3). The project and training course should proceed simultaneously. During the
training course, the Black Belt (BB) has no other job except the project.
However, the Six Sigma technique’s shortcomings and features illustrate the
relationship between positive and negative characteristics (Azis & Osada, 2010;
Zackrisson, Franzén, Melbin, & Shahnavaz, 1995; Zhang, et al., 2011).
- 11 -
1). The key method of project improvement is to reduce waste but there are
also some positive effects from waste.
2). In the Six Sigma technique the improvement of customers’ satisfaction
levels requires weekly action.
3). Initially, the Six Sigma technique does not play a prominent role and does
not affect the foundations of the organisation. Most companies don’t
understand it and the only perceived effect is that it increases costs.
However, tactic management, which is part of the Six Sigma technique,
becomes a part of the way the company manages projects.
4). The Six Sigma technique does not have a method of unifying all the
employees in the company.
The basic components of the Six Sigma technique are not new, however, the
packaging of the method is new. The Six Sigma technique is a useful compilation of
proven techniques from many previous management methods (Redzic & Baik, 2006).
The power comes from the Six Sigma technique’s team-based approach, customer
orientation, financial motivation and assessment, tangible rewards for success,
qualitative and statistical tools and its focus on short duration and high impact
projects (He, 2008; Kim, 2008).
According to some researchers, there are some key elements which affect the
implementation of the Six Sigma technique. These factors also become problems
which need to be addressed by the company executives (Azis & Osada, 2010; Sekhar
& Mahanti, 2006; Tamura, 2006; Tayntor, 2007; Tká & Lyócsa, 2010; Tong, Tsung, &
Yen, 2004; Wienclaw, 2008d; Zou & Lee, 2010; Zu, et al., 2008). The problems are:
- 12 -
The company management levels of investment and commitment. In
successful cases, the company commits strongly to the Six Sigma
implementation.
Six Sigma involves changes to enterprise values and requires cultural
adjustment. This often involves changing the organisational structure and
the staff may resist the changes. Continuous communication, motivation
and training are the best methods to solve this problem.
The team’s project management skills. Team members should have some
basic knowledge of project management, including an awareness of its
limitations, its use in problem solving, its goals, the resources used, how
much time it will take, and how much it will cost.
The team should correctly choose the project. It must be consistent with
the enterprise's overall goal, output value and profits. The team also has
to respond to and understand what its customers want.
The company should choose suitable tools and techniques. Companies
sometimes choose inappropriate tools or methodologies and this
increases costs and wastes human resources. To understand all relevant
tools is the most important things for company leaders.
2.2 The quality management systems
2.2.1 Total Quality Management (TQM)
There are various management systems which have appeared as frameworks to
achieve quality improvement. The Total Quality Management (TQM), then, is
another familiar quality control technique to be applied in manufacturing industry.
TQM is a system for implementing and managing quality improvement activities on
an organisation-wide basis (Chau, Liu, & Ip, 2009). TQM began in the early 1980s
- 13 -
and was influenced by some eminent philosophies, for example, those of W.
Edwards Deming, Joseph Juran, and others (Wienclaw, 2008b).
It developed some concepts and ideas, which involved connections between
participating organisations, work culture, customer focus, supplier quality
improvement, and other activities. It focused on all essentials of the organisation in
achieving the goal of quality improvement. Normally, organisations establish TQM
operation quality councils or high-level teams that cope with strategic quality
initiatives; workforce-level teams that focus on routine production or business
activities; and cross-functional teams that address specific quality improvement
issues (Ali, 2005; Jones, et al., 2010; Montgomery et al., 2005).
2.2.2 The difference between the Six Sigma technique and the Total
Quality Management (TQM)
In general, the Six Sigma technique and the TQM have some similarities. For
instance, both techniques are basically the same. They are common manipulated for
the quality improvement in manufacturing industry. However, the Six Sigma
technique is not a part of TQM. Generally, the purpose of utilising TQM is to
improve the quality of manufacturing processes, the products, and even the
services. On the contrary, the Six Sigma technique is to make those improvements
more sharper and more focused (Amasaka, 2000; Ayad, 2010; Catherwood, 2002).
Compared with the Six Sigma technique, TQM has been more successfully and
extensively practised in the manufacturing industry (Zu, et al., 2008). It is inasmuch
as TQM is aimed at keeping already existing quality standards at a high while
simultaneously improving quality and the term of “quality” in TQM is defined as the
level which the product reaches the standards produced inside the company
(Barney & McCarty, 2002). It is unlike TQM, the Six Sigma technique is more
emphasised the best results when focused on customers. The Six Sigma technique is
- 14 -
a statistical process control and data driven approach and is highlighted the quality
is the fewest number of defects, which must be removed as much as possible.
Furthermore, the term of “quality” in Six Sigma is defined in large part by targeting
segmentations (Besterfield, 2008; Pan, Park, Baik, & Choi, 2007).
Generally speaking, the Six Sigma technique is more focusing on the quality
improvement in entire business and TQM is more focusing on the simplex processes
or operations within departments. Considering the objectives in organisations,
therefore, managers in manufacturing industries would normally choose TQM to
attempt improving the quality in manufacturing department instead of the Six
Sigma technique (Barney & McCarty, 2002).
However, the importance of the Six Sigma technique has been maintained recently
since the growth of technology. Appling this technique in organisations has a strong
and a positive impact on the business financial performance (Yang & Hsieh, 2009;
Zou & Lee, 2010). Quality improvement projects with Six Sigma result in real savings,
expanded sales opportunities, or documented improvements in customer
satisfaction (Bengtson, 2008; Montgomery, 2010). Being a successful enterprise,
plant managers or managers who are in a higher managing positions start to pay
more attention to the entire business performance in the organisation (Azadegan &
Pai, 2008).
Moreover, the company leaders would be more likely to be fully concentrated,
provide the resources needed to train personnel and to establish full-time
employment positions related to Six Sigma once these improvements occur,. These
positions can be used as steppingstone to positions of higher responsibility in the
organisation (Bendell, 2004). It is much more likely that the techniques will actually
be used since the training is project-oriented, notwithstanding, the Six Sigma
technical training is normally deeper and more extensive than the typical TQM
program training (Antony, Banuelas, & Knowles, 2001; Patterson, Bonissone, &
Pavese, 2005).
- 15 -
2.2.3 Basics for Six Sigma technique
Six Sigma is a statistical measurement tool. It is used to identify customer-critical
features and evaluate performances at each step in the production process. DPMO
(Defects per Million Opportunities) is one measurement of performance level and
this measurement is frequently used in Six Sigma. DPMO standardises the rejects
rate and it is based on the opportunities in terms of units (He, 2008; Wienclaw,
2008d).
The formula is:
DPMO = [Total number of defects / (Total number of units verified * Average
number of opportunities in a unit)] * 106
DPMO is the average number of defects in one million units. It is best used when the
process or characteristic is repeated many times (Evans, 2004). For instance,
company A manufactures 1,000 pieces of mask per hour every day and total 210 out
of 1,000 pieces of mask are defect goods. In the meanwhile, the manager also
discovered that there are four potential opportunities may result in those defect
goods during the manufacturing procedure. According to the formula above, it
computes that they will have 52,500 pieces of defect mask per million. The number
of DPMO, the 52,500 pieces of mask, is located in the range between 3 Sigma and 4
Sigma referring to the Sigma Scale in Table 1.
Table 1 below illustrates the DPMOs for a range of performance levels. Performance
at the Six Sigma level means that a process produces fewer than 3.4 defects or
errors per million opportunities for defects (Evans, 2004; Stevenson, 2005).
Therefore, the manager in Company A, then, can expect that there will be near 93
percentage of opportunity in producing the finished goods with reaching customer
satisfaction in normal circumstances.
- 16 -
Table 1: The sigma scale
Specific Limit Per cent inside specs Number of DPMO
1σ 30.23 697,700
2σ 69.13 608,700
3σ 93.32 66,810
4σ 99.3790 6,210
5σ 99.97670 233
6σ 99.999660 3.4
Source: (Evans, 2004; Stevenson, 2005)
Source: (Evans, 2004; Stevenson, 2005)
LCL Mean
UCL
-3σ -2σ -1σ σ +1σ +2σ +3σ
99.99%
99.37%
69.13
%
- 17 -
Figure 1: The six sigma diagram
Source: (Evans, 2004; Stevenson, 2005)
Figure 1 is derivative from the data in Table 1 and it demonstrates that the less the
process variation from suppliers, the less the number of defect opportunities and
the lower the potential risk for customers. That is the reason why customers are
paying more and more attention to the Six Sigma technique.
2.2.4 The Six Sigma technique principles
The Six Sigma technique begins with one general-purpose equation. This simple
equation is
Y = ƒ(х)+ ε (1)
Where: Y is the process outcome. It is the result which you desire or discover. ƒ is
the process by which inputs are transformed into outcomes. хis the inputs and
factors. There may be several x’s and if so, the symbol – “ε” is added after the х. This
indicates the presence of error and the uncertainty in depending upon the х’s. The
- 18 -
transformation function is used to actually create the desired outcomes (Stewart &
Spencer, 2006; Tká & Lyócsa, 2010).
2.3 The Six Sigma technique methods
The two project implementation methodologies in the Six Sigma technique
comprising DMAIC method and DMADV method will be demonstrated in this
section.
2.3.1 The DMAIC method for the Six Sigma technique
The basic method consists of the following five steps:
Define (D): the company identifies high-level project goals, the current process
and problems. The problems are serious problem for organisation.
Measure (M): the company measures and researches the production process
and identifies the key aspects of the current process and collects relevant data.
Analyse (A): the company obtains the data and verifies the cause and effect
relationships. It attempts to ensure that all factors have been considered.
Improve (I): the company optimises the process based upon data analysis and
the use of techniques such as design for Six Sigma (DFSS).
Control (C): the company ensures that any failures to achieve targets are
corrected before they result in defects. The company sets up pilot runs to
establish process capability, move on to production, set up control mechanisms
and continuously monitor the process.
Some practitioners do not include the define (D) phase because they consider that
this phase is a part of preparation. This method is used to improve the existing
- 19 -
processes (Bañuelas, Antony, & Brace, 2005; Jones, 2004; Pojasek, 2003; Redling,
2005).
Figure 2: DMAIC cycle
Source: Developed for this research from (Bañuelas, et al., 2005; Jones, 2004;
Pojasek, 2003; Redling, 2005)
2.3.2 The DMADV method for the Six Sigma technique
The another project implement methodology is DMADV method which is basically
consisted of the following five steps:
Define (D) the design goals that are consistent with customer demands and the
enterprise strategy.
Measure (M) and identify CTQs (Critical to Quality factors), product capabilities,
production process capability and risks.
Analyse (A) to develop and design alternatives, create a high-level design and
evaluate design capability to select the best design.
- 20 -
Design (D) details, optimise the design, and plan for design verification. This
phase may require simulations.
Verify (V) the design, set up pilot runs, implement the production process and
hand it over to the process owners.
This type of method is utilised in the design of Six Sigma (DFSS). To implement the
DFSS requires a solid implementation of DMAIC as a foundation, and managerial
experience. Coordinated communication is the most important factor (Bañuelas, et
al., 2005; Jones, 2004; Pojasek, 2003; Redling, 2005)
.
Figure 3: DMADV cycle
Source: Developed for this research from (Bañuelas, et al., 2005; Jones, 2004;
Pojasek, 2003; Redling, 2005)
2.3.3 The Comparison between two methods
The original Six Sigma project focused on the improvement of the production
process and utilised the PDCA (Plan-Do-Check-Action) or the DMAIC for its project
model (AI-Mishari & Suliman, 2008). There are several differences between the two
- 21 -
methods (Anand, 2006; Antony & Banuelas Coronado, 2002; Antony, et al., 2001;
Besterfield, 2008; Chakravorty, 2009; Chau, et al., 2009). Table 2 below shows these
differences.
Table 2: Comparison of DMAIC and DMADV
DMAIC DMADV
The goal is to improve the process.
This is called the IFSS (Improvement
for Six Sigma) project
Looks for improvements with
changes that are within the system.
Uses the existing processes
Aims to discriminate and quantify
the reasons for variations in quality.
The DMAIC is passive.
Also called DFSS (Design for Six
Sigma) project
Aims to break through the existing
barrier
Used for designing both process
and product
The goal is to design or redesign the
process before the operation starts
The DMADV is active.
2.4 Implementation roles for the Six Sigma technique
The quality management function of the Six Sigma technique is its most important
innovation. In earlier applications of the Six Sigma technique in quality management,
quality control personnel and statisticians were always in separate departments
(Antony, Kumar, & Madu, 2005). The Six Sigma technique uses ranking terminology
to define a hierarchy that cuts across all business functions and a promotion path
which leads straight into the executive suite.
There are several key roles involved in successfully implementing Six Sigma (Antony
& Banuelas Coronado, 2002; Antony, et al., 2001; Chakravorty, 2009; Feo & Bar-El,
2002; Franza & Chakravorty, 2007; Montgomery, et al., 2005).
Executive Leadership which includes the CEO and other top management.
Their responsibility is to set goals for Six Sigma implementation. They also
- 22 -
motivate others who perform other key roles the freedom and resources
to explore new ideas for breakthrough improvements.
Champions are responsible for implementing the Six Sigma technique
across the organisation in an integrated manner. The executive leaders
choose them from upper management. Champions also act as mentors to
Black Belts.
Master Black Belts (MBB), identified by champions, act as in-house
coaches for Six Sigma. They devote 100% of their time to Six Sigma. They
assist Champions and guide Black Belts and Green Belts. Apart from
statistical tasks, their time spent ensuring the consistent application of Six
Sigma across various functions and departments.
Black Belts (BB) operate under Master Black Belts to apply Six Sigma
methodology for specific projects. They devote 100% of their time to Six
Sigma. They focus primarily on Six Sigma project execution, whereas
Champions and MBBs focus on identifying projects or functions for Six
Sigma.
Green Belts (GB) are the employees who take up Six Sigma
implementation along with their other job responsibilities. They operate
under the guidance of Black Belts.
- 23 -
Figure 4: Levels of roles
Source: Developed for this research from (Antony & Banuelas Coronado, 2002;
Antony, et al., 2001; Chakravorty, 2009; Feo & Bar-El, 2002; Franza & Chakravorty,
2007; Montgomery, et al., 2005)
- 24 -
2.5 Useful tools and methodologies for the Six Sigma technique
It is essential for a company to use the appropriate tools and techniques in order to
successfully support, develop and progress a process of continuous improvement
(Geoff, 2002). Some of these tools are simple to use, but some of them are more
complex. Those tools and methods have different roles to play in the improvements.
If the company applies those correctly, useful and reliable results will be obtained.
2.5.1 Failure Mode and Effects Analysis (FMEA)
Failure Mode and Effect Analysis (FMEA) is a reliability technique for analysing
potential failure modes by classifying consequences within a system and its value is
as a planning tool to assist with building quality into a business’s products, services
and processes. This procedure is implemented to identify the failure modes and
determine the effect of failures upon the system (Goh, 2002; Goh & Xie, 2003).
FMEA is a fundamental tool adopted in numerous industries for asset management.
By measuring the severity of defects, this method can be applied in a variety of
phases including product design, product manufacture, equipment investment,
preventative maintenance and customer service. The objective is to eliminate or
minimise the potential risk and provide feasible remedies. Industries can utilise this
approach to ensure acceptable levels of reliability and improve product quality
(Huang, Yeh, Lin, & Lee, 2009). This method uses the table to calculate the each
potential value.
Table 3: FMEA calculation diagram
Part Function Failure mode
Failure mechanism
Effect S O D RPN
Source: Developed for this research from (Goh, 2002; Goh & Xie, 2003)
- 25 -
2.5.2 Fault Tree Analysis (FTA)
Fault Tree Analysis (FTA) is used to analyse the risk of undesirable outcomes and the
potential causes of these outcomes in the system. FTA is a top-down technique that
identifies the primary cause or causes of unexpected events such as compressor
failure (Mast, 2003). The important concept of the fault tree combines all of the
probable causes and depicts an undesired occurrence or state using a graphical
illustration. FTA illustrates the logical relationships between equipment failures,
human error and external events (Rao et al., 1996). It shows how combinations of
such factors can combine to cause specific accidents.
Basic event
Event
Condition event
Undeveloped event
AND gate
OR gate
- 26 -
Figure 5: FTA symbols
Source: Developed for this research from (Mast, 2003)
2.5.3 Flow Chart
Flow charts are also called process mapping or flow sheets. They are necessary for
obtaining an in-depth understanding of a process (Rao, et al., 1996). A flow chart is
used to provide a diagrammatic picture and it often uses a set of established
symbols to represent the processes. It is shows all the steps or stages in a process,
project or sequence of events and it is of considerable assistance in documenting
and describing a process as an aid to understand the examination and improvement.
There are two main types of flow charts (Stevenson, 2005). One is used to display
processes such as manufacturing operations or computer operations. It indicates
the various steps taken as the product moves along the production line or the
problem moves through the computer. The other type is a traditional method of
representing in schematic form the flow of data in a system (Stuart, Mullins, & Drew,
1996). This flow chart illustrates the input and output points, the logic or sequence
of the various processing steps in the system and the relationships of each element
of the system to the other parts of the system or to other information systems
(Stevenson, 2005).
- 27 -
Process
Decision
Document
Manual operation
Stored data
Data
Figure 6: Flow chart symbols
Source: Developed for this research from (Stevenson, 2005)
2.5.4 Histogram
Histograms are also called frequency diagrams. They are basic statistical tools and
also graphical diagrams. They illustrate the frequency or number of observations of
a particular value or occurrences within a specified group (Stevenson, 2005). The
histogram represents collections of large amounts of data. The reason for collecting
the information is to research the main data for each possible cause of an event and
to identify the differences between them. The abscissa axis represents measured
values of variations in some quality, characteristic or classification.
The ordinate axis represents the number of times each characteristic or variation is
observed. Histograms can be used to assess performance against a given standard,
specification or tolerance (Swarbrick, 2007). Variations which are seen with difficulty
in general digital graphs become very obvious in histograms.
- 28 -
Figure 7: Example of histogram
Source: Developed for this research from (Swarbrick, 2007)
2.5.5 Pareto Diagrams
The Pareto diagram is a tool which is used to illustrate key points in management.
The key use of the Pareto chart is to focus on root causes. Compared to the total
number of causes, the number of root causes is small, but once the root causes are
understood, the other elements can be controlled. The significance of Pareto chart
is to calculate the important factors or majority of influences in the research
outcomes. This chart is exerted by minority of input features. In this chart, the
variable factors will organize and calculate with percentage from higher proportion
to lower proportion. Those factors’ percentage will be cumulated until a hundred
percentages. The most root causes have been occupied around eighty percentages.
This is called “80-20 Principle”. According to the 80-20 principle, 80 per cent of
effects are due to 20 per cent of causes. (Stevenson, 2005; Tiwary, 2008).
Pareto Diagrams do not classify data according to projects or items. They categorise
according to size and arrange data in a chart. Pareto analysis is often used to analyse
data from check sheets or histograms. The Pareto distribution is a kind of histogram
in which the characteristics observed are arranged from the largest frequency to the
smallest frequency. In addition to that, there is often a line which depicts the
0
20
40
60
80
100
1 2 3 4
- 29 -
cumulative frequency curve. Pareto diagrams can also display the results of
improvement programs over time (Adams, Gupta, & Wilson, 2003).
Figure 8: Example for Pareto Diagram
Source: Developed for this research from (Stevenson, 2005; Tiwary, 2008)
2.5.6 Cause and Effect Diagrams
This type of diagram is also called a fishbone diagram or Ishikawa diagram. It is used
to explain the relationships between primary and the secondary factors and quality
characteristics (Besterfield, 2008). It deals with the characteristics of problems and
it shows correlations that are considered to be influential. These diagrams
reorganise information from charts into a form that can be easily understood
(Chakraborty & Tah, 2006).
There are two basic types of Fishbone diagrams. The first one involves dispersion
analysis and is usually used to find and identify the possible major causes of specific
quality problems. In addition it carries out the suitable classification of data. The
other type involves process classification. It uses information from process flow
charts. It finds out the possible major causes of problems from each step in the flow
chart (Coleman, Arunakumar, Foldvary, & Feltham, 2001). Each stage of the process
is then brainstormed and ideas developed by the team members.
0%
20%
40%
60%
80%
100%
120%
0
5000
10000
15000
20000
25000
30000
1 2 3 4
- 30 -
Figure 9: Example for Cause and Effect Diagram
Source: Developed for this research from (Chakraborty & Tah, 2006)
2.5.7 Control Chart
Quality Control is a continuity activity in the company and it needs to be measured
periodically by engineers. Control charts are used to calculate the control limitation
in statistics for fundamental elements of processes and differentiate between
unusual variations and normal variations according to the data. It presents data for
the performance of one actual product characteristic and compares current process
capability with previous capability. This data is displayed in a time sequence graph
(Chen, Hsu, & Ouyang, 2007; Chen, Chang, & Huang, 2009).
Control charts have two horizontal lines which are called control limits. They are
upper control limit (UCL) and lower control limit (LCL). Control limits are selected by
statistical calculation and specify a high probability (generally greater than 0.99) that
experimental points would fall between these limits. This condition will be met if
the process is in control (Connaughton, 2005b).
Cause Cause
Cause Cause
Problem
Sub-cause
- 31 -
Figure 10: Example of a Control Chart
Source: (K. Chen, et al., 2007; S. C. Chen, et al., 2009) (Connaughton, 2005b)
2.6 Methods for obtaining the data
There are several methods which can be used to obtain information from companies.
In this report, several methods of obtaining knowledge from an expert operator
were suitable for my topic due to the expert (the main source of knowledge) being
one of the team members. An explanation and analysis of each method is given
below.
- 32 -
1. Unstructured interviews
Interviewers ask experts questions relating to a specific topic or the expert
actively shares his/her expertise and experience with the interviewers. This
is the most common and simple method for eliciting knowledge. The
following six methods are generally utilised during interviews (Evans, 2004):
Problem discussion
Problem description
Problem analysis
Refinement
Examination
Validation
However, this method is time-consuming because interviewers might not be well
prepared for extracting knowledge and the procedures of the interview might not
be well managed. In addition, interviews are costly and they have sometimes been
considered as ineffective (Evans, 2004; Mast, 2003).
2. Brainstorming
Brainstorming is a group creativity technique designed to generate a large
number of ideas for the solution to a problem. Although brainstorming has
become a popular group technique, researchers have generally failed to find
evidence of its effectiveness for enhancing either the quantity or quality of
ideas. Because of problems such as distraction, social loafing, evaluation
apprehension, and production blocking, brainstorming groups are not much
more effective than other types of groups, and they are actually less
effective than individuals working independently (Barney & McCarty, 2002;
Mairani, 2007). For this reason, there have been numerous attempts to
improve brainstorming or replace it with more effective variations of the
basic technique.
- 33 -
Although traditional brainstorming may not increase the productivity of groups, it
has other potential benefits, such as enhancing the enjoyment of group work and
improving morality. It may also serve as a useful exercise for team building
(Murugappan & Keeni, 2000).
To prepare for brainstorming, we need to do self-study first and then create various
ideas of directions for the topic based on what we know.
3. Collecting historical data
Quality control and manufacturing departments should have monthly data
which can be used to identify problems and draw a curve line. Moreover, it
can be used compare with historical and current information.
4. Modifying and developing the tool
As we discussed before, this study is the first time where the Six Sigma
technique has been implemented in the mask industry. The Six Sigma
technique uses some statistical charts and diagrams to present the data. In
this research project, it will utilise the special statistical software package,
Minitab (Biehl, 2004; Pan, et al., 2007).
5. Apply the method back to the case
The Six Sigma technique is a new technique for the United Excel Enterprise
Corporation (UEE). This research will modify the Six Sigma technique for
application to this company. Moreover, it will adapt the Minitab program for
applying to this company. After the company implemented the Six Sigma
technique and software package, it evaluated and observed its performance
and this enhanced its quality control level.
The main source of information for this project is from expert knowledge and data
collection. Fortunately, a specialist was available who were working in a mask
company and this specialist provided information about the types and quantities of
- 34 -
materials required for production and the equipment used. Moreover, technical
sources, quality reports and manufacturing reports were also provided by the
specialist.
2.7 The Six Sigma technique in mask industry
To conclude, the Six Sigma focuses on finance and operation objectives which have
major impacts on process regulation and product improvement. Therefore, Six
Sigma is relevant to business strategies, customers, human resource and suppliers
(Montgomery & Woodall, 2008). Most of literature review about the Six Sigma
technique, reports that it is a popular and useful tool for the manufacturing and
service industries, such as the car industry and banking services. Manufacturing
masks involves many unpredictable factors which could cause defective products
(Nesladek, 2007). One issue is the quality control methodology.
A gap between the theoretical quality control technique and the real case dealt with
in this research arises because the Six Sigma technique is being introduced into the
mask industry for the first time. Moreover, there is no prior experience to indicate
what kind of the quality control tools are the best ones to apply to a mask
manufacturing company. The mask industry is still utilising traditional quality control
techniques.
The traditional inspection methods have limitations as discussed before. Mask
companies often provide large quantities of rejects which reduce production
capacity and increase costs. This can lead to decrease their investments. Therefore,
introducing the Six Sigma technique to the mask industry is an important
contribution made by this thesis.
- 35 -
2.8 Conclusion
The Six Sigma technique is a measurement tool and management philosophy. It
utilises two statistical methods which are normal distribution and the probability
model. The major aims of the Six Sigma technique are to improve quality rates,
reduce costs, increase customer satisfaction and eliminate errors in business tactics,
management methods, research and development of products, manufacturing the
products, delivery to customers and after sales service (Amasaka, 2000; Anand,
2006).
The Six Sigma approach makes full use of the standard deviation (σ). In the Six Sigma
approach a company aims to reduce the rate of defects to almost zero (Vore, 2008).
Traditional quality improvement methods cannot achieve this goal because
traditional quality control presumes that the way to improve quality is by
inspections and it focuses on the problem itself. If a company wants to accomplish
the Six Sigma standard, it needs to accept that quality also depends on design,
manufacture and planning (Dedhia, 2005; Montgomery & Woodall, 2008).
The next chapter discusses the quality problem in the mask industry with the case
study.
- 36 -
Chapter 3 Quality Problems in the Mask Industry – A Case Study
3.1 Introduction
As mentioned earlier, due to the severe acute respiratory syndrome (SARS), bird flu,
influenza, swine flu, radiation and hay fever , the mask industry has become an
important industry in the last ten years. Some experts indicate that 80% of these
diseases or viruses will occur again . Most governments around the world are
working to prevent these diseases(Centre for Disease Control, 2011; Organization,
2011; Sinica, 2011). Masks are good protective tools and these can protect the
respiratory tract and prevent inhaling viruses from the air. In fact, there are
thousands of mask products on the market. Customers buy masks for wide variety
of purposes. Masks are also used by various industries such as, medical industry,
semi-conductor industries, the food industry, traditional manufacturing and the
metal industry. The mask usage differ from industry to industry (Grenon, Hamaker,
& Buck, 1995; Reita, 2006). In this chapter, background information about the
production line is presented.
3.2 Company Background
The United Excel Enterprise Corporation (UEE) was registered in March, 1990. It is
Taiwan’s first company to specialise in designing and manufacturing masks for
customers. This company’s products aim to satisfy all customers’ requirements and
to produce high quality products. The main business of UEE is to design and
manufacture a diverse range of masks based on customers’ requirements.
The company management philosophy is dedicated to the manufacture of high
quality products and to provide perfect after-sales services. It adopts suitable
- 37 -
marketing strategies for the Taiwanese and Japanese markets and promotes its
products to semi-conductor factories in Taiwan, as well as to hospitals and
traditional industries. It has a good reputation because of its quality products. In
addition to that after-sales service helps UEE gain customer loyalty and trust.
This company has four factories which produce different products designed to meet
customers’ needs. These factories are located in three different countries:
1. Taiwan (Hsinchu). This factory is also near to the Hsinchu Science Based
Industrial Park. It provides masks for some traditional industries, newer
industries, semi-conductor factories, and hospitals.
2. Japan (Tokyo). This factory is a joint venture with K.T. International Inc.
3. China (Shanghai). This factory is also a joint venture with K.T. International
Inc.
This research was conducted in UEE’s Taiwan factory.
3.2.1 Product Background
The performance of masks is primarily depends on the materials used. Non-woven
textiles are the principal fabrics used to fabricate masks. Non-woven fabrics provide
specific functions such as absorbency, liquid repellence, filtering, bacterial barriers
and sterility.
Masks are made from a combination of different types of non-woven fabrics, for
example, polypropylene non-woven (PP non-woven), melt-blown non-woven, fibre
non-woven, and spun-bonding non-woven and active carbon fibre (Kang Na Hsiung
Enterprise Co. Ltd. - Nonwoven, 2011; MATSUKURA CO., 2011). Details of the fabrics
used in masks and their properties are provided below;
- 38 -
Polypropylene non-woven (PP non-woven): this type of non-woven fabric
is used to contact human skin and ensure the user’s comfort.
Melt-blown non-woven: this cloth’s function is to protect the user from
bacteria and pollen. In three-level masks, this fabric is placed in between
polypropylene non-woven and fibre non-woven fabrics.
Fibre non-woven: this sort of fabric is normally used as the outer level to
provide waterproofing and exclude a range of substances.
Spun-bonding non-woven: this type of cloth can strengthen the
performance of masks by providing specific traits, including air
permeability, chemical resistance, and bacteria resistance.
Active carbon fibre: good activated carbon filters are used to make carbon
fibre cloth and the cloth presents a pliable soft shape. The active carbon
fibre in the cinereous black colour has extremely well for the adsorption
effect.
Recently, some techniques of producing non-woven fabrics have been developed
and have become popular. Two of these new techniques that are widely used and
therefore described below:
Nano (Gold) non-woven: It is produced using nanotechnology; which has
three main characteristics: the reaction rate is rapid, the temperature is
low and the acceptance is high. A nanometre mask emits the anion
elements and the remote infrared ray material into the cotton material.
Active carbon anion has antibacterial effect.
- 39 -
Nano Ag (Silver) non-woven: uses new vacuum sputtering coating
technology. The SGS (Société Générale de Surveillance Group) test reports
have verified its anti-bacterial effect on many common types of bacteria.
Zero pollution which will be more and more important in future products
is enforced in manufacturing processes.
Whether or not all these properties are needed depends on what jobs the masks
are to be used for. Various materials have different characteristics and the
properties and specifications of the masks sold to customers depend on the working
environment of the customer.
3.3 Production Process in case organisation
In the case organisation, the whole production process is divided into three sections.
The first one is the procurement process (input) in which the materials and parts are
purchased from the suppliers. The second one is the manufacturing process, in
which the masks are produced. The last section is the delivery process (output) in
which the products are transported or delivered to the customers.
Figure 11 shows what happens when the sales and/or research and development
departments receive order information from customers. The purchasing department
identifies required component and orders the required raw materials. The suppliers
deliver those raw materials to the company’s storage depot. The quality control
department then inspects the materials. If the materials satisfy the required
specifications and quality requirements, the supply is accepted. However, the
company will return them to the providers if the supply fails to satisfy the required
specifications.
The UEE purchases raw materials such as non-woven mask cord and wire from
different suppliers. After IQC (Incoming Quality Control), the manager delivers the
raw materials to the production lines and workers assemble those parts. After
completing production, inspectors check the product to ensure quality and then the
- 40 -
products are delivered to customers. During these processes, every stage involves
some causes for defects to occur. In order to find out those causes of defects, a
number of factors should be taken into consideration. The investigator has looked at
factors related to workers, machines, method and materials.
Figure12 shows the procedures the UEE follows with its customers. Figure 13
illustrates the processes UEE follows to minimise the risk of delivering defective
products.
- 41 -
Figure 11: raw material Input process
Customer
Sales / Research and Develop departments
Order information
Identify and order raw materials
Purchasing department (PO number)
Material management
Suppliers
Materials delivered
Quality control department
Accept
Inventory Return to suppliers
Defective goods
- 42 -
Figure 12: The process linking the company with its customers
Customer
Release order
Sales / R&D Department
Production management Department
Identify requiring parts
Production Planning (PP)
Manufacture
Quality Department N
Rework
Y Products delivered
Customer Quality Department
Customer Inventory Return
N Y
- 43 -
Figure 13: Simplified depiction of output process
Y
N
N
Y
Decision
UEE
FQC
Delivery Rework
Customer IQC
Decision
Customer Warehouse Return
- 44 -
After UEE finishes making its products, it implements Finish Quality Control (FQC)
before delivery to customers. When customers receive a batch of products,
inspectors use the IQC procedure to check product quality. The measured product
values are then presented and compared with specification values. Normally, if an
inspected sample fails to meet the required standard, the batch of products will be
considered as failure.
Table 4: The classification of quality level for product quality.
Quality level Percentage of quality
A 100%
B 95%
C 90%
D Under 90%
Table 4 shows that the company has categorised product quality in four levels. The
quality level “A” means that the mask’s appearance is in good condition and that this
type of mask always sells at a good price. Level “B” mask are usually lower priced
than level “A”. Masks in this category have some minor defects but these defects will
not cause any price reduction. Level “C” masks have some serious quality and
appearance problems and the manufacturing department often reworks these
defective products. Level “D” masks have problems which cannot be repaired and
they are scrapped.
- 45 -
Figure 14: The process between purchase department and customers
Order
Sales / R&D Department
Production management Department Inventory
IQC
Materials from suppliers
Manufacture
PQC
FQC
Customers
- 46 -
Figure 14 shows the internal relationships between materials and quality
departments. IQC (incoming quality control) checks the incoming items to ensure
their quality with design specifications. They randomly pick the raw materials to
check from every lot. Once they find any defective items, they stop the lot entering
and warn the supplier of the bad quality materials. In situations where the
production schedule is tight, the UEE informs the customer about the bad lot of
materials and asks for permission to accept the bad lot and start manufacturing. The
permission document requested from the customer commits the customer to
accept the final products whether the quality is good or not.
PQC (processing quality control) works with production. They select every end
product of a day to inspect and record any quality related issues. PQC employees
also play the role of supervisor in production processes.
FQC (finally quality control) is the last door to make sure the UEE is delivering
quality products to its customers. They check only three products out of a batch and
record the specifications and then submit it to their customer. It is a serious problem
if FQC detects any non-conforming products from those three.
Figure 15 displays the whole process in which the company receives the order from
its customers, manufactures the products and the customers accept those products.
- 47 -
Figure 15: The whole production process for the mask company
Customers
Produce information (PO no.)
Sales / Research & Develop departments
Identify and require materials
Production management department
Production plan
Manufacture
Quality department
Reworking / sorting
N Y
Products delivered
Customer Quality Department
Customer Inventory Return
N Y
Inventory
- 48 -
Figure 16 illustrates the production of masks. Most masks have similar processes;
however, cup mask are made with a different process. To begin with, the raw
materials should be feed in the mask blank masking machine. After this, the
materials are turned into semi-finished products.
The workers inspect these products. If they are in good condition, the employees
put those semi- finished products into the mask ear-loop welding machine. If they
are defective, the workers store them in the warehouse or scrap them. After the
mask ear-loop welding machine process, a member of staff will examine the masks
and pack good quality masks into a box or bag.
For defective products, the company has two different processes. One is to rework
them. The other is to sell them to different customers at reduced prices.
- 49 -
Figure 16: Process for manufacturing masks
Materials installation
Mask Blank Making Machine Processing
Mask Ear-loop Welding Machine processing
Semi-finished products
Finished products
Quality audit and control
Packing in box or bag
Y
N
Store or scrap
Y
N
Rework or sale
- 50 -
3.4 Quality control in UEE
3.4.1 Quality control issues
The United Excel Enterprise Corporation’s products are based on customer’s
requirements. Each customer has different needs. For example, the Taiwan Semi-
conductor Manufacturing Co. Ltd. (TSMC) wants its employees to wear very
comfortable masks which do not slip off their faces because they wear the mask all
the time in the cleaning room.
Therefore, the United Excel Enterprise Corporation’s research and development
(R&D) department communicated with TSMC’s departments and employees. They
decided to use tie-back cord style masks. Another example is a medical company in
Japan which wanted to protect their employees from ninety-five percent of viruses
and so it needs high quality protection. The masks made for this company are made
of high quality active carbon materials.
Different customers have different requirements and the company has to satisfy
them. For this reason, the company experiences the following major problems
related to the product quality:
Raw materials consistency. Each customer requires different materials’
weight and stretch and so on. The company needs to have a good
communication with suppliers and request them to provide materials with
consistent quality.
Machine fluency. Since different materials have different features and
steps, mask manufacturing machines sometimes cannot adapt to those
materials. Machines need to operate for a short time and the worker
needs to adjust raw materials which could make them suitable for
machine operation.
- 51 -
Operator training. In the factories, there are different production lines to
produce masks. For diversities of work, each worker needs to learn
different types of inspection and packaging. They need to have few weeks
of training.
Defective goods. There are several timings that can sieve out the
production of damaged goods during the manufacturing procedure. Firstly,
operators may manufacture defective masks in the production of semi-
finished goods. Secondly the quality controller may find damaged goods in
the finished masks. They also can discover the imperfect goods in other
steps.
Productivity achievement. Due to inappropriate operation of machines, or
employees’ inefficiency the production of finished goods may not reach
the required standard.
At present, the company has a quality control department which is very large and
important. Quality control is also the one of the important requirements for
satisfying customer. However, current quality control systems cannot always ensure
goods which meet the customers’ requirements. Because the United Excel
Enterprise Corporation produce masks based on customers’ requirements, the
products must pass specific examinations. Normally, quality control has four
inspections: IQC (In-coming quality control), PQC (Process quality control), FQC
(Final quality control) and OQC (Out-coming quality control). Total inspection (100%
inspection) and random inspection are also used in this company.
The research and development department of United Excel Enterprise Corporation
designs and develops suitable masks for its customers. There is much variability
which the UEE and its customers consider like working area, ventilation and
absorbency. Before the factory manufactures masks, it determines the raw materials
weight. Each raw material has a different density, and different filtration and
- 52 -
sterilisation properties. These variations influence the mask manufacturing process.
The company spends about three to four months in researching and designing new
masks before production begins.
For this reason, the raw materials are the key factor in the quality control of the
mask industry. The manufacturing department of United Excel Enterprise
Corporation asks the suppliers to provide raw materials with the same weight as the
materials used in developing the product. However, sometimes the contractors
supply the wrong materials. This mistake will result in defective goods.
The quality control department of UEE usually ask each supplier to provide test
reports of its raw materials quality. These test reports need to be from a certified
authority. Suppliers must provide these test reports each year. Moreover, the quality
control department also requests providers to supply high quality products and they
must sign a work contract and basic ordering agreement. The contract specifies that
the suppliers use total inspection for their whole manufacturing process and that
they should provide zero defective raw materials to the United Excel Enterprise
Corporation.
When the raw materials are delivered to the United Excel Enterprise Corporation’s
warehouse, the quality control department conducts an IQC (In-coming quality
control) inspection. In this stage, the company uses random inspections, machine
validation and sight checks. The raw material validity check is to inspect weight,
tensile strength, weld and calliper. Random examinations have potential risks;
however, the probability of not detecting defects is very small. After this inspection,
the company accepts those raw materials which pass examination.
Sometimes the manufacturers would not sift the masks from defective raw
materials during the process because the inspection is random. However, the quality
control department in the United Excel Enterprise Corporation would reject those
raw materials and return the whole batch to the suppliers once the manager
- 53 -
discovers defective raw materials. It is inasmuch as If the company did not reject
those defective materials, the production line would process some defective semi-
finished goods.
The manufacturing department of United Excel Enterprise Corporation requisitions
materials before manufacturing masks. During the process, each working station’s
operator has to check the quality of masks. These are PQC (Process quality control)
and FQC (Final quality control). However, sometimes the worker fails to check the
quality. As a result, the customers may request the company to indemnify or reduce
the price.
- 54 -
Chapter 4 Root causes of quality problems in case organisation
4.1 Introduction
This research uses a modified version of the DMAIC method of the Six Sigma
technique. The first step of the Six Sigma technique is to define and discover the
critical problems. When the company determines problems or root causes of the
problems, the project team should analyse the data and consider the possible
solutions (Brady & Allen, 2006; Liu, 2006).
In this chapter, root causes of the quality problems in the case organisation are
analysed, The production data will be presented by utilising a particular software
package (Minitab, 2011).Figure 17 shows the methods followed to identify root
causes of quality problems. This also represents the relationship between the
research question and the theory presented.
- 55 -
Figure 17: Theoretical Model for this thesis
- 56 -
4.2 Survey of UEE management and employees
To identify the root causes of the quality problems at UEE, management and
employees of the company were interviewed using a semi structured questionnaire
presented in appendix C. Archive data was also analysed to determine the causes of
the quality problems. Results of the interviews are summarised below:
There are some possible causes which could lead to the company manufacture the
defective products. Moreover, there are some probable solutions to solve those
problems.
The suppliers may provide defective raw materials. Masks are made at
different levels of non-woven fabrics, which vary from polypropylene non-
woven (PP non-woven), melt-blown non-woven, fibber non-woven, and
spun-bonding non-woven and active carbon fibber. Those raw materials have
different basic weight, stretch, softness, strength, washability, density and
sterility and so on. Those possible elements would cause the suppliers
provides the defective raw materials for the company. In the IQC (incoming
quality control) procedure, those factors are not simple to discover by
random inspection. The better solution of this situation is to request the
suppliers provide the quality report of raw materials for each batch.
Moreover, the quality control of the UEE Company should have a good
communication and supervision to the manufacturing department of
supplier’s company.
The manufacturing department adjusted the machines. Each product has
their setting and design requirements. The employees who are working in
the maintenance department may set the incorrect product’s information for
the machines. Moreover, the workers would need to adjust the product’s
information for many times. This situation might produce defective masks
and those masks cannot be repaired.
- 57 -
Wrong product size: Sometimes the workers, who are working in the
production of semi-finished goods station, are providing the oversize or
small sized semi-finished masks to the next work station or storage. The
masks have their specification, for example, the adult’s size is approximately
90cm*25cm and the children’s size is around 45cmX25cm. If the mask is
oversize or small size, the machine could not properly manufacture the
perfect masks for the customers. This is one of the major reasons for
producing the defective masks.
The workers who are working in the manufacturing department may not
concentrate on the works for long hours. Sometimes the employees might
have worked for over 8 hours per day and they did not take a good relax in
their holidays. Due to this situation, the workers might feel tired and they
could not focus on the manufacturing the masks.
Another situation is that sometimes the employees will chat with other
workers when they are manufacturing the products. Because of this situation,
the workers might accumulate many masks in the working station and they
do not have adequate time for inspection the semi-finished and finished
goods. When the workers have those masks on the table, they do not check
clearly for each mask and they will pass those masks to package into the box.
The employees are not trained adequately: In the factories, there are
different production lines to produce customers’ masks. Each worker has to
learn different type of inspection and package. They need have a few weeks
to train and teach. The company usually train the new employees for about
one month before they start working in the working environment. In this
training, the company just tell the new workers the rules and inspection
methodology. After that, the company will arrange the proper job for those
workers and request them to do which they had learnt before. The company
do not have training after this initial training.
- 58 -
The inspection method also has problems. When the raw materials are
delivered to the United Excel Enterprise Corporation’s (UEE) warehouse, the
quality control department adopts the IQC (In-coming quality control)
inspection. In this stage, the company will use random inspection, machine
validation and sight check. The raw material of validity checking is to inspect
the weight, tensile strength, weld and calliper etc. For the incoming products,
the quality control department is utilizing the random inspection. They will
select randomly for each batch of raw materials. For the PQC (Process quality
control), the workers are implementing the total examination. In this process,
they just use simple visual check that checks the colour, size and appearance
and so on.
The machine problems. Sometimes the workers will increase the speed for
increasing the production rate. Each machine has their setting for production
rate. If the machine undergo over speeding, the machine might create some
problems. For instant, the speed of packaging machine is around 80 pieces
per minute. If the machine is over speeding, the machine will produce
defective goods. The company recommend the workers should not over
speed the machine and maintain the same speed for producing the products.
4.3 Use of six sigma tools to identify causes of quality problems
The UEE Company purchases raw items such as types of non-woven materials, wire
and mask cords from suppliers. After the IQC, manager distributes those raw
materials to the production line and workers collect them. After production is
completed, inspector’s measure values to ensure quality and then employees
deliver the product to customers.
When the employees collect the data from the quality control department, a team
member analyses those information. In the collection of the data, there are some
possible problems. This is shown in Figure 18.
- 59 -
4.3.1 Cause and effect diagram
During these processes, every stage involves some potential for non-conforming
results. In order to find out the root causes of problems, a number of factors should
be taken into consideration. The analysis comprises four sections: worker, machine,
method and material.
1) Manpower
Assembly procedure variation: Workers assemble parts in the masks
machine. Although every worker follows the same assembly procedure,
variations generally occur. Work experience, work training, and even a
worker’s mood affect the quality of the final product. For example,
operators may put the wrong input value into the machine program to
calibrate the rotation of screws or screw harder than other workers.
Delivery: Raw materials or finished products are delivered to storage by
staff. In the process materials or products might be damaged because of
carelessness.
2) Machine
Lifespan: every machine has its own lifespan. Older machines are more
likely produce defective products. Therefore, the implementation of
repair, regular maintenance and inspection is an important issue.
- 60 -
Figure 18: Fishbone diagram for identifying defective products.
Machine Manpower
Defective
goods
Worker Experience
Measuring Method
Supplier delivery
Supplier produce
Worker Training
Daily work hours
Material
Specification
Repair
Maintenance method/assembly
Material Inventory
Method Material
Usage
Measuring Device
- 61 -
3) Method
Measuring methods: Measuring methods might be different between
UEE Corporation and it’s suppliers and customers. For instance, the IQC
(Incoming Quality Control) of UEE measures raw material quality and
UEE requests quality reports from the suppliers. The FQC (Finally
Quality Control) of UEE measures overall appearance of products before
delivery. Although employees confirm that the products meet the
specifications, the customer’s IQC might report non-conforming results.
The situation can be attributed to different measuring methods.
Moreover, the PQC (Processing Quality Control) in the company is still
implementing the same inspection method which is total examination.
4) Material
Material storage: UEE receives raw materials from suppliers and stores
them in its warehouse. During the storage period, raw materials may
deteriorate.
Raw material specification: The IQC of UEE inspects raw materials to
check if they meet the company’s standards. Even if the raw materials
from the supplier meet the specifications, the values may be close to
the borderline of upper control limit (UCL) and lower control limit (LCL).
In such cases, after production, final products may be measured as
defect.
4.3.2 Pareto chart
The Pareto principle states that the eighty per cent (80%) of effects comes from the
twenty per cent (20%) of the causes. A Pareto chart could help the company
understand the issues which contribute most to the problematic products or
processes (Stuart, et al., 1996). A team member can create a Pareto chart based on
a group brainstorming and an analysis of the collected data. Using this chart, the
team members can try to resolve the potential issues.
- 62 -
In Figure 19, the Pareto chart illustrates that problems regarding raw materials
(23%), semifinished goods (20%), machine problems (13%), speed problems (10%),
chatting (10%) and inspection method (10%) are the main causes of rejection of the
defective masks and the company should focus on these categories for problem
solving.
These findings are based on collected data and opinion of thirty employees’ in the
company. As can be seen, there are eight categories of defect quality causes, namely,
raw material problems, semi-finished products problems, machine problems, speed
problem, operators chat with each other, inspection method, employees less
training and adjustment machine.
Figure 19: A Pareto chart of the main causes of defects
defect of
raw
material
s
defect of
semifini
shed
goods
machine
problem
s
speed
problem
not
concentr
ate
inspectio
n
method
less
training
adjustme
nt
machine
s
23.33 20.00 13.33 10.00 10.00 10.00 6.67 6.67
% 23.33 43.33 56.67 66.67 76.67 86.67 93.33 100.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
- 63 -
4.4 Production data analysis
In July of 2009, the UEE worked 12 hours per day on weekdays and eight hours per
day on weekends. The UEE Company executed total inspection during this time. The
manufacturing department manufactured two types of products – semi-finished
goods and finished goods. That is, an operator will first put the raw material into the
Mask Blank Machine, and the machine then will combine and roll with the whole
materials after the first procedure. In the meanwhile, the machine will produce
masks into the designated flat pattern which are the manufacturers’ requirements.
The products manufactured in this procedure is so-called the semi-finished goods.
However, these semi-finished goods are incomplete masks during the entire mask
manufacturing procedure, and they need further work to be done on them at a later
stage. As a result of incomplete masks, the second procedure of manufactured
goods is designed by using other machine. To manufacture the complete and
standard goods based on specimen of the masks, the workers need to put those
semi-finished goods into the Mask Ear-Loop Welding Machine with necessary raw
materials in processing the final manufactured product. Ultimately, those complete
goods which are manufactured in the second procedure are termed as “finished
goods” in manufacturing industry.
Tables 6 and 7 show the number of finished and semifinished goods produced in
July 2009. Numbers of good and defective products are also presented in the tables.
In this research, the working days in this month were equivalent into six weeks
considering five-day working per week (including weekends). The weekly
productivity was calculated from data provided by the quality department as shown
in Tables 5 and 6.
In this month, the company manufactured around 500,100 finished masks.
Approximately 495,900 masks were accepted and 5,000 were defective.
- 64 -
Table 5: Weekly data for finished goods in July 2009
Week / July Finished goods Accepts Defects
1 77,265 76,359 906
2 88,241 87,093 1,148
3 76,640 75,758 882
4 76,674 76,125 549
5 81,409 80,464 945
6 100,699 100,048 651
Total 500,928 495,847 5,081
In summary, during this month, the number of finished masks in week 4 was only 26
more than in week 3. However, the number of defects in week 4 was significantly
lower compared with week 3. This difference could be related to problem caused by
machine and speed problems. These risks were mentioned before (Table 5).
Similarly, for the semifinished goods, there were 2,421,648 goods manufactured. Of
these, 2,367,565 were accepted and 54,083 were defects.
Table 6: Weekly data for semifinished goods in July 2009
Week / July Semifinished goods Accepts Defects
1 368,438 359,110 9,328
2 426,826 414,699 12,127
3 371,360 362,955 8,405
4 367,788 362,072 5,716
5 400,936 391,000 9,936
6 486,300 477,729 8,571
Total 2,421,648 2,367,565 54,083
- 65 -
With the availability of above data, this research recommends that UEE can use p
control chart, a c control chart or an np control chart to analyse its data. The p chart
provides information about the proportion of defective goods. In this type of chart,
the subgroups do not need to be of equal size. The np control chart is used to plot
the number of non-conforming units. The c control is used to determine the number
of defects. However, the subgroups have to be of equal size in the np and c charts.
To begin with, the value of p needs to be calculated.
The formula of calculating p is
(2)
In equation (2), d is the number of defective good for each sample and is the
number of manufactured products in that sample. Table 7 and Table 8 present p
values for the data presented in tables 5 and 6.
Table 7: The proportion of finished goods in July 2009
Week / July Finished goods Accepts Defects P value
1 77,265 76,359 906 0.0117
2 88,241 87,093 1,148 0.0130
3 76,640 75,758 882 0.0115
4 76,674 76,125 549 0.0072
5 81,409 80,464 945 0.0116
6 100,699 100,048 651 0.0065
Total 500,928 495,847 5,081
- 66 -
Table 8: The proportion of semifinished goods in July 2009
Week / July Semifinished goods Accepts Defects P value
1 368,438 359,110 9,328 0.0253
2 426,826 414,699 12,127 0.0284
3 371,360 362,955 8,405 0.0226
4 367,788 362,072 5,716 0.0155
5 400,936 391,000 9,936 0.0248
6 486,300 477,729 8,571 0.0176
Total 2,421,648 2,367,565 54,083
Table 7 presents the proportion of finished goods that were defective in July 2009.
The company produced the largest number of finished goods in week 6 and about
0.65 per cent was defective goods. The highest defective proportion of 1.3 precent
occurred in week 2 as seen in Table 8.
Similarly, Table 8 shows that more than one-third of the total semifinished goods in
July were manufactured in the last two weeks and the peak productivity was in
week 6. The average proportion of defective semifinished goods was higher than the
proportion of defective finished goods. For instance, the defective proportion for
semifinished goods (2.84 per cent) in week 2 was 1.55 per cent higher than that of
finished goods in the same week.
Following the calculation of p value, the CL (centre limit), UCL (upper centre limit)
and LCL (lower centre limit) had to be calculated for each day in July. The equations
for CL, UCL and LCL are:
(3)
(4)
- 67 -
(5)
The values of CL, UCL and LCL for finished goods and semifinished goods are shown
in Table 9 and Table 10. The quality control department then can minimise the
amount of defective goods by monitoring the weekly proportion of imperfection in
mask products via calculating the weekly statistics. As can be seen in Table 10 and
Table 11, those two tables demonstrate the weekly productivity of finished goods
(Table 9) and semi-finished goods (Table 10) and the scopes of acceptable quality
restriction by calculating the CL, UCL, and LCL in UEE in July 2009.
Table 9: The CL, UCL and LCL for finished goods in July 2009.
Week / July Finished goods CL UCL LCL
1 77,265 0.0101 0.0112 0.0091
2 88,241 0.0101 0.0112 0.0091
3 76,640 0.0101 0.0112 0.0091
4 76,674 0.0101 0.0112 0.0091
5 81,409 0.0101 0.0112 0.0091
6 100,699 0.0101 0.0111 0.0101
Total 500,928
- 68 -
Table 10: The CL, UCL and LCL for semifinished goods in July 2009.
Week / July Semifinished goods CL UCL LCL
1 368,428 0.0223 0.0231 0.0216
2 426,826 0.0223 0.0230 0.0217
3 371,360 0.0223 0.0231 0.0216
4 367,788 0.0223 0.0231 0.0216
5 400,936 0.0223 0.0230 0.0216
6 486,300 0.0223 0.0230 0.0217
Total 2,421,648
Figure 20: The p chart for finished goods in July 2009
Source: Analysis of research data in the case study
- 69 -
Generally Figure 20 and Figure 21 were generated from statistics found in Table 9
and Table 10. Figure 20 shows that all of the p results are beyond the control limits.
In weeks 1, 3 and 5, p values are above the upper control limit (p values are 0.0117
in week 1 and 0.0115 in week 3 and 0.0116 in week 5). Conversely, in weeks 4 and 6
p values are below the lower control limit. In short, the numbers of finished goods
seems to be unstable in the period.
Figure 21: The p chart for semifinished goods in July 2009
In Figure 21, the semi-finished goods chart shows that most of the p values are
outside the control limits. In week 2, 4 and 6 are beyond the control limit which the
p values are 0.0284 in week 2 and 0.0155 in week 4 and 0.0176 in week 6. The week
- 70 -
1 and 5 are above the upper control limit. Only week 3 is within the control limit
which the p value is 0.0226.
A summary of the data from July can be seen in Table 11 which shows the numbers
of finished and semifinished products in each week. In July, the manufacturing
department produced around 2,922,600 masks. Of those products, 2,863,412 were
acceptable and 59,164 were defective.
As can be seen in Table 11, in two weeks in July 2009 Company manufactured over
500,000 pieces of mask. During the experimental period, the week 6 was
highlighted as the manufacturing department produced the largest amount of
products (586,999 pieces of mask with the highest acceptable goods of 577,777
pieces of mask). On the contrary, week 2 had high performance of producing
515,067 pieces of mask. However, it also produced highest of defective products in
that week (13,275 pieces of mask).
- 71 -
Table 11: Summary of July production in 2009
Week Goods Accept Defect P value CL UCL LCL
1 445,703 435,469 10,234 0.0230 0.0202 0.0209 0.0196
2 515,067 501,792 13,275 0.0258 0.0202 0.0208 0.0197
3 448,000 438,713 9,287 0.0207 0.0202 0.0209 0.0196
4 444,462 438,197 6,265 0.0141 0.0202 0.0209 0.0196
5 482,345 471,464 10,881 0.0226 0.0202 0.0209 0.0196
6 586,999 577,777 9,222 0.0157 0.0202 0.0208 0.0197
Total 2,922,576 2,863,412 59,164
- 72 -
Figure 22: The P chart of total production in July 2009.
Figure 22, shows that in week 3 (p=0.0207) p was within the control limits. The
defective proportion in week 4 was below the lower control limit. Referring to Table
11, it can be seen that in week 4 the company produced around 6,265 defective
masks which is the lowest number in this month.
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4.5 Conclusion
This chapter has discussed the systematic methodology followed to identify the root
causes of quality problems in a mask industry. The face to face interview of
management and shop floor employees was designed as a data collection method.
Production and archive data was also used for this purpose.
From the face to face interview and analysis of production data, possible root causes
of quality problem were identified. This research also utilized some techniques for
analysing the information.
There are various causes which could explain why this situation occurred. First of all,
the technique of the manufacturing department might be the reason. The goods
may be defective due to the inability to follow the predetermined method for
production. Another reason might be the manufacturing department. The
employees might use an incorrect inspection method or apply the wrong product
information for the machines.
The raw material quality might also be a cause. The quality control department may
accept defective raw materials from external suppliers. Finally, the purchase
department might purchase the wrong machine components. All these situations
can result in defective products.
United Excel Enterprise (UEE) Corporation, on average, manufactured approximately
487,000 pieces of mask weekly in the experimental period according to the tables
and figures shown within this chapter. However, the p value (p=0.0207) was within
the control limits only in week 3. That is to say, the quality control department in
UEE now is facing the issue of unstable quantity of output in products owing to
beyond the scope of defective goods.
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Chapter 5 Improving quality using the Six Sigma technique
5.1 Empirical Findings
The DMAIC approach to the Six Sigma technique has five steps for process
improvement. In this chapter, the empirical findings will be discussed and analysed
using this theoretical framework and the analysis will be connected to the approach.
Empirical findings for this chapter follow the conceptual model shown in Figure 23.
This figure shows the clear connection between the data, the research question and
the theoretical model. This figure also shows that the analytical model is connected
to the empirical model.
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Figure 23: Empirical Findings and Analysis
- 76 -
5.2 Step of implementation the Six Sigma technique
In this research, the Six Sigma technique has been first applied into a Mask Company.
There are five stages of implementation initiative for the Six Sigma technique which
are initialization, deployment, implementation, expandability and sustainability.
Figure 24: The lifecycle for implementing the Six Sigma technique
Source: Six Sigma Software Development
Figure 24 illustrates the lifecycle of the Six Sigma technique. First, an organisation
needs to initialize program for the Six Sigma technique by establishing objectives
and creating necessary facilities. Next, team members need to be assigned the jobs,
provided training and necessary resources
After that, the organisation should implement the selected tasks and improve
Initialize
Deploy
Implement Expand
Sustain
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quality performance. Following the successful implemented, the organisation needs
to expand the scope of initiative goals into new functional areas and others
additional organisation departments.
Figure 25: The Six Sigma deployment model
Source: Six Sigma Software Development
Opportunity & initial implementation
Management Champion & Excutive Leader
Strategic Plan
Project Selection
Master Black Belt, Black Belt selection
Green Belt selection
End of project & Continuous improvement
Awareness &
Support
Coaching &
Training
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Figure 25 illustrates that the company obtains opportunity and selects the projects
for reducing the defective product rate, improving the product quality and
increasing the product performance. The executive leader and general manager
choose the Master Black Belt (MBB), Black Belt (BB) and Green Belt (GB).
5.3 The Six Sigma team in the United Excel Enterprise (UEE)
Corporation
As this research discussed previously, the Six Sigma methodology is about tools,
techniques and statistics. However, the results of the Six Sigma approach depend on
the people applying the technique (Coleman, 2008; Shanmugam, 2007).
In this study, the Six Sigma technique was first introduced in the mask industry. This
technique is the first time utilised in the UEE Corporation. In order to implement the
Six Sigma technique successfully, the UEE Corporation firstly selected the team
members and they were five key players for the Six Sigma initiative. This team
included the following positions: Executive Leader, Champion, Master Black Belt
(MBB), Black Belt (BB) and Green Belt (GB) (Cheng, 2008; Hahn, Doganaksoy, &
Hoerl, 2007; Hilton, Balla, & Sohal, 2008).
In the first place, the key role of the “Executive Leader” was chosen by the CEO of
the UEE Corporation to decide on applying which types of Six Sigma technique and
promoting it throughout the UEE Corporation (Antony, et al., 2001). A
“Companion” , the senior level of general manager, was chosen to promote the
technique throughout the company and especially in functional groups. The
“Companion” in the UEE Corporation was required not only to understand the
discipline, strategies and tools of the Six Sigma technique but also to be able to
educate other employees about the tool and its implementation (Antony &
Banuelas Coronado, 2002; Barney & McCarty, 2002; Evans, 2004). The general
manager in the UEE Corporation was also required to ensure that the project was
selected aligns with the executive strategy and would be supported by the team
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members. Furthermore, the general manager selected Black Belt (BB) employees to
identify the project area, and to establish clear goals for the UEE Corporation
(Barney & McCarty, 2002; Jones, et al., 2010; Moosa & Sajid, 2010).
The position of Master Black Belt (MBB) in the UEE Corporation recruited the person
who was an expert in the Six Sigma technique with the highest level of proficiency.
MBB was also required of cooperating with both the frontline working colleagues
and the outside experts engaging in introducing, training and supporting the
initiative Six Sigma technique in the UEE Corporation during the investigation. This
position in the organisation was taken by a department manager to serve as a
trainer, mentor and guide (Desai & Shrivastava, 2008; Franza & Chakravorty, 2007).
Furthermore, the Black Belt (BB) was chosen to conduct a team on selecting
projects either on a full time basis or part time based on the occasion. They worked
on defining, measuring, analysing, improving and controlling processes to reach the
targeting outcomes in the UEE Corporation. Black Belt in the organisation was
selected to solve problems within the Six Sigma framework and the person was
trained to be technical leaders in using tools and methods to improve quality
(Barney & McCarty, 2002; Tayntor, 2007).
Finally, Green Belts (GB) were chosen to assist the Black Belt (BB) in their functional
area in the UEE Corporation. They worked part time in this role and they usually
work in a limited and specific area during the researching period. They used Six
Sigma tools to examine and solve continuing problems within their regular jobs
(Costello, et al., 2005). Green Belts (GB) are full-time employees in the UEE
Corporation. They also helped the manager in the organisation collect information,
analyse data and do other important tasks for this team. They were the team
members with enough understanding of the Six Sigma technique so that they can
share the Sigma tools, their working experience, and basic knowledge for other
employees during the training (Jalali, Shafieezadeh, & Naiini, 2008; Xu, Sikdar, &
Gardner, 2006).
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The leader in the UEE Corporation organised these five key players into the team of
Six Sigma and guided the team members to communicate with each other in the
cause of making the best decisions for the project of the company. All members
came from various functions in the UEE Corporation and they also worked part time
on the project. They are very familiar with the processes and they have attended
any training courses which related to the quality control area during the research.
The improvement phase was initiated into company by selecting the performance
characteristics from products or processes. These characteristics were improved to
achieve the goal. They started to select the objective of research project and
identify the critical few factors that caused the defects when the team members
went through the first four phases of the DMAIC process. Moreover, the purpose of
the control phase in the Six Sigma technique is to maintain the changes that the
team members make to some critical factors in order to continue the improvement
(Mach & Guaqueta, 2001). The team members are now ready to develop tests and
implement solutions and use a software package to improve the processes by
reducing the variations in the critical output variables after the investigation (Zhang
Wu & Shamsuzzaman, 2005; Zhang. Wu, Shamsuzzaman, & Wang, 2007; Xiao,
Huang, Qian, & Lou, 2007).
5.4 Results of case improvement
Six sigma was applied exactly after one year of initial investigation at UEE. The root
causes identified in Chapter 4 was addressed by the six sigma team. In July of 2010,
the United Excel Enterprise (UEE) Corporation worked for 12 hours per day in the
weekdays and 8 hours per day on the weekend. After the company implemented
the quality control tool, the manufacturing department made some changes to the
production process.
From Table 12 and Table 13, it can be seen that in July 2010, the company
manufactured around 501,000 finished masks. There were approximately 496,100
acceptable masks and 4,766 were defective masks. For the production of
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semifinished goods, there were 2,420,538 goods manufactured with 2,369,465
acceptable and 51,073 defects.
Table 12: The finished goods after improvement in July 2010
Week / July Finished goods Accepts Defects
1 78,569 77,559 1,010
2 87,707 86,893 814
3 75,958 75,468 490
4 76,586 76,125 461
5 81,409 80,464 945
6 100,594 99,548 1,046
Total 500,823 496,057 4,766
Table 13: The semi finished goods after improvement in 2010.
Week / July Semifinished goods Accepts Defects
1 367,938, 359,010 8,928
2 426,826 416,699 10,127
3 371,360 362,955 8,405
4 367,788 362,072 5,716
5 400,826 391,000 9,826
6 485,800 477,729 8,071
Total 2,420,538 2,369,465 51,073
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Table 12 represents the total amount of finished goods after the development of
quality control in July 2010. The production in week 6 had the highest performance
during the experimental period which produced over 100,000 pieces of finished
goods and the productivity was also higher than the average in July in 2010 referring
to the analysis in Table 12. In addition, within the experimental implementation
period, the week 6 was highlighted that the manufacturing department also
produced the largest amount of products for 99,548 pieces of acceptable mask but
with the maximum of defective goods of 1,046 pieces of mask. On the other hand,
the week 3 was manufacturing the lowest productivity in this month. The
production and acceptable mask in week 3 were approximate 24,600 pieces in
production and 24,000 pieces in acceptable masks less than the amount in week 6
respectively.
Table 13 is similar to Table 12. Table 13 represents the total amount of semi-finished
goods after the development of quality control in July 2010. The manufacturer was
producing the products with maximum efficiency for 485,800 pieces of semi-
finished goods in week 6. In the same week, it also produced 477,729 pieces of
acceptable mask. On the contrary, notwithstanding the production in week 4 had
lowest performance of producing 367,788 pieces of semi-finished mask in July 2010,
it minimised the defective products dropping in 5,716 pieces of mask which was
obviously lower than the average of defective goods in July 2010.
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Figure 26: The p values for finished goods after improvement.
Figure 26 demonstrates the curve of the finished goods data after the quality
control development in July 2010 based on the statistics in Table 13. As shown in
Figure 26, p values in weeks 2 and 6 were within the control limit (0.093 in week 2
and 0.0104 in week 6). Table 13, shows that week 6 produced the most defective
goods which was 1,046 masks.
Similarly, Figure 27 illustrates the curve of semi-finished products data after the
quality control development in July 2010 based on the statistics in Table 14. In week
4, for semi-finished goods less defective goods were produced than in other weeks.
In week 4, only 5,716 masks were manufactured. However, from Figure 27 it can be
seen that this week was far below the lower control limit.
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Figure 27: The semi finished goods data after improvement.
To sum up the data, the summary of total production data after the Six Sigma
technique implemented in July 2010 is represented in Table 15. The manufacturing
department produced around 3 million pieces of mask for the company. Among
those masks, there were approximately 2.8 million satisfactory masks and 55,000
pieces of defective mask. The manufacturer was producing the maximum of
products for total 586,394 pieces of goods with the largest amount of acceptable
goods in approximate 577,300 pieces of mask. In comparison, the quality control
department also examined that the highest defective products was in week 2 in
10,941 pieces of mask.
Figure 28 illustrates the calculation of defective proportion for total production after
implementing the Six Sigma technique in July 2010. The result shows that the
defective proportion in week 1 was the highest (p=0.0223) during the experimental
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month. On the contrary, the p value in week 4 (p=0.0139) was the lowest point and
producing the lowest total production (444,374 pieces of mask) within the smallest
defect products (6,177 pieces of mask) in this month referring to the analysis in
Table 14. Moreover, Figure 28 also points out that the week 4 and 6 were far below
the lower control limit.
Figure 28: The total goods after improvement
The result shows that in week 1 production capacity was high. After this week, the
operators were trying to reduce the capacity and adjust the machines. In the
following weeks, the capacities of the production lines decreased. However, the
defect rate also fell.
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Table 14: Summary of production after improvement in July of 2010
Week Goods Accept Defect p CL UCL LCL
1 446,507 436,569 9,938 0.0223 0.0191 0.0197 0.0185
2 514,533 503,592 10,941 0.0213 0.0191 0.0197 0.0185
3 447,318 438,423 8,895 0.0199 0.0191 0.0197 0.0185
4 444,374 438,197 6,177 0.0139 0.0191 0.0197 0.0185
5 482,235 471,464 10,771 0.0223 0.0191 0.0197 0.0185
6 586,394 577,277 9,117 0.0155 0.0191 0.0197 0.0186
Total 2,921,361 2,865,522 55,839
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Table 15: Comparison of total goods data
Week (July)
Pre-test (2009)
Post-test (2010)
Difference
1 445,703 446,507 -804
2 515,067 514,533 534
3 448,000 447,318 682
4 444,462 444,374 88
5 482.345 482,235 110
6 586,999 586,394 605
Total 2,922,576 2,921,361 1,215
Table 15 is the comparison of the total amount of goods between pre-test and post-
test experimental group design. The outcome in this table presents the difference in
capacity between pre-test and post-test by manipulating the Six Sigma technique in
mask manufacturing industry. These results seem to suggest that there was a
negative effort due to using the Six Sigma technique. Productivity was 1,215 pieces
lower after the improvement in technique.
In particular, the lowest production during the research period was in week 3, in
which total production fell by 682, as can be seen in Table 16. In addition, only the
first week in the post-test experimental period manufactured 804 pieces more than
same period in pre-test experimental period (2009). The smallest difference in
capacity between the period of time was only 88 pieces in week 4.
The finding was surprisingly different with respect to overall production (Table 16).
Table 16 however expatiated on the comparison of the experimental results in the
case study with detailed statistics consisting of accepted goods and defected goods.
This table compares total goods production for the same period but in different year.
The number of defective semi-finished and finished goods decreased by 3,325
pieces of mask (from 59,164 down to 55,839) after the use of the Six Sigma
technique. In comparison with the significant reduction in defective goods, the
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company manufactured only 2,080 pieces more acceptable goods than before which
was from 2,863,412 up to 2,865,522 pieces of mask.
Table 16: The comparison for the case study.
Goods
Pre-test (July 2009) Post-test (July 2010)
Accept Defect Total Accept Defect Total
Semi-finished goods
2,367,565 54,083 2,421,648 2,369,465 51,073 2,420,538
Finished goods
495,847 5,081 500,928 496,057 4,766 500,823
Total of produce
2,863,412 59,164 2,922,576 2,865,522 55,839 2,921,361
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5.5 Summary
This chapter has discussed the data analysis for this research. To investigate the
influence of the Six Sigma technique, this research used a longitudinal study to
collect the data before and after the change in approach at more than one point in
time. The data were collected in the same month in two successive years (July in
2009 and 2010).
The result of implemented the Six Sigma technique illustrated that the defective
goods rate and total mask production capacity had slightly reduced. and the total
acceptable production rate had increased.
To sum up, the total production via the Six Sigma technique in July 2010 was 1,215
pieces less than same period in 2009 with an increase in the number of acceptable
goods and a decrease in the number of defective goods.
The Six Sigma technique is a continuous improvement (CI) strategy for controlling
the quality system. The limitation in this research is that the time frame in
implemented the Six Sigma technique was only one month.
As a consequence, the results show that using the Six Sigma technique had a
positive impact on the total goods production, and therefore, company needs to
spend more time on conducting and adjusting the Six Sigma technique.
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Chapter 6 Conclusion
The Six Sigma technique was practised with the case study and the result was
analysed in the previous chapter. This chapter, which is also the final chapter, will
summarise and discuss the conclusions and the limitations for the future research.
Section 6.1 will summarise the overall performance by utilising the Six Sigma
technique in the United Excel Enterprise Corporation (UEE) referring to the main
research problem and research questions. Section 6.2 and 6.3 will describe the
conclusions about the research questions and main problem; moreover, evaluate
the performance in the mask industry in Section 6.4. Eventually, limitations in the
research will be expounded in Section 6.5 and Section 6.6 will consider the further
research area for future research.
6.1 Summary of the research
In this research, the process of quality control improvement was explored in mask
manufacturing industry by using the Six Sigma technique involving the group
activities, data analysis, and specialist knowledge and training courses. The five
steps DMAIC approach was selected as the Six Sigma technique in this thesis which
consists of define (D), measure (M), analyse (A), improve (I), and control (C).
In the DMAIC approach of the Six Sigma technique, there are some techniques and
tools which are excellent in identifying and classifying the quality problems within
the group activities. For instance, the 5W2H (why, what, where, who, how and how
many), Pareto chart, cause and effect diagram, and control charts were conducted
with the case in this research.
The Six Sigma technique was firstly introduced in 1980’s and it has been a
remarkable technique to improve the quality in manufacturing industries. Chau, Liu
and Ip’s (2009) defined that the Total Quality Management (TQM) is a system for
implementing and managing quality improvement activities on an organisation-wide
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basis and the concept of TQM was established in manufacturing industries since
early 1980s. However, recent literature shows that mask industry do not really
utilise the Six Sigma technique for improving the quality of products. Hence, the Six
Sigma technique was first time introduced and applied into the mask industry in this
research.
The United Excel Enterprise Corporation (UEE) was chosen as a case to study for this
research. This mask company uses the traditional quality control techniques of total
inspection and random inspection for IQC (Incoming Quality Control), PQC
(Processing Quality Control) and FQC (Finally Quality Control) to determine the
problem and process characteristics in the quality control department of the
organisation.
This research mapped the current supplier processes, manufacturing processes and
delivery processes from the UEE Corporation. To understand and present those
three processes in mask industry, the flowchart was utilised as a mapping tool to
provide an idea about the current processes in this research.
Analysis of the data revealed that there are some possible root causes which could
lead to defective goods. Raw material quality and inspection methods were found to
be the main root causes for providing defective products. The quality control
department has difficulty for measuring and examining the whole materials.
The production data was collected through the company’s manufacturing and
quality control departments. The employees and managers from this company were
also interviewed.
In the recent time, there is about 95% of the masks produced by the UEE Company
are rated as being of an acceptable standard. This means that the quality control
level is around the five sigma level.
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Table 17: Summary of results in the case
Pre-test Post-test Result
Semi-finished goods in July 2009 and 2010
Accept 2,367,565 2,369,465 Increase
(1,900 pieces)
Defect 54,083 51,073 Decrease
(3,010 pieces)
Finished goods in July 2009 and 2010
Accept 495,847 496,057 Increase
(210 pieces)
Defect 5,081 4,766 Decrease
(315 pieces)
Table 17 summarises the difference between acceptable goods and defective goods
in semi-finished and finished products in July 2009 and 2010. As can be seen from
above table, the capacity of acceptable products in semi-finished goods and finished
goods were increased after implementing the Six Sigma technique. In addition to
that, the total amounts of defective goods were decreased during the post-test
experimental periods. The results of the research appear to illustrate that the Six
Sigma technique has the positive effect on improving quality control in mask
manufacturing industry.
In particular, the number of semi-finished acceptable goods was increased by 1,900
pieces where the finished acceptable goods was increased by only 210 pieces.
This technique is not currently the chosen method of quality control in the mask
industry. After implementation the Six Sigma technique into mask industry, the team
showed some improvements regarding total production, defective rate and
acceptable rate. In this research the time frame was only one month. More time is
needed to assess the results of implementing the Six Sigma technique in this
industry. Eventually, company needs to concentrate on employee training period
and training budget. The Six Sigma technique is a continuous improvement tool that
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will help industries to provide high quality products and increase process reliability.
6.2 Conclusions about research questions
In order to have a better understanding about the research process and problem,
the four major research questions were generalised in this research in Section 1.2.
This section will summarise the findings according to the investigation this time for
each research questions with the case study in this research.
Research question 1: What is the quality control (QC) process in a mask company?
Generally speaking, the term “quality control process” in mask industry is defined as
a procedure to ensure that the entire quality of manufactured masks are all reach
the requirement of the customers (Schilling & Neubauer, 2009; Webber & Wallace,
2007). To accomplish their requirement successfully, the quality control process in
the United Excel Enterprise (UEE) Corporation is divided into three procedures
consisting of the Incoming Quality Control (IQC), Processing Quality Control (PQC),
and Finally Quality Control (FQC) (Gustavsson & Wanstrom, 2009; Ramlan, Ahmad,
& Kellyn, 2009).
First of all, the Incoming Quality Control (IQC) inspects the whole incoming raw
materials to ensure the quality being consistent with design specifications from
suppliers, such as from Kang Na Hsiung Enterprise Corporation (Kang Na Hsiung
Enterprise Co. Ltd. - Nonwoven, 2011) before the assembly process starts. The
Process Quality Control (PQC), then, is conducted to detect any potential problems
which may arise the quality issues during the assembly process. PQC works in whole
production and records the number of defective products (Schilling & Neubauer,
2009). The Finally Quality Control (FQC) is the final procedure before the masks ship
to the customers and it is applied to ensure the final shipment is defect-free after
the manufacturing process (Gustavsson & Wanstrom, 2009; Nicolay et al., 2011).
The products in the United Excel Enterprise (UEE) Corporation are designed by
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customers’ requirements. To satisfy every single customer and its requirement, the
company, therefore, has abundant experience in resolving the issues regarding to
the quality control. For instance, the issues about raw materials consistency, the
machine fluency, operator training, and so on. The detailed explanation was early
discussed in Section 3.3 in this thesis.
Research question 2: What are the possible root causes of defective products?
According to the findings in this thesis, the eight issues were concluded as the
possible root causes resulting in defective products in mask industry as follows and
was elaborated in Section 4.2:
The suppliers may provide defective raw materials to company.
The manufacturing department adjusted the machines.
Wrong product size.
The workers who are working in the manufacturing department may not
concentrate on the works for long hours.
Another situation is that sometimes the employees will chat with other
workers when they are manufacturing the products.
The employees are not trained adequately.
The inspection method also has problems.
The machine problems.
Research question 3: How could these root causes be addressed?
According to Gustavsson and Wanstrom (2009) point of view, those root causes in
the mask industry could be noticed from the four perspectives involving employees,
machine, method, and material. Those four perspectives were expounded with the
case study in Section 4.3.
These findings are based on collected data and opinion of thirty employees’ in the
company. As can be seen, there are eight categories of defect quality causes, namely,
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raw material problems, semi-finished products problems, machine problems, speed
problem, operators chat with each other, inspection method, employees less
training and adjustment machine.
Research question 4: What quality control tools and software packages are used in
the mask industry?
The five tools were mainly utilised to assist in monitoring the products during the
procedure for controlling the quality in the mask industry which are Flow Chart,
Histogram, Pareto Diagrams, Cause and Effect Diagrams, and Control Chart.
In the first place, the Flow Chart is necessary for obtaining an in-depth
understanding of a process (Rao, et al., 1996). It is shows all the steps or stages in a
process, project or sequence of events and it is of considerable assistance in
documenting and describing a process as an aid to understand the examination and
improvement (Stevenson, 2005).
Secondly, the Histogram is known as frequency diagrams. The reason for collecting
the information is to research the main data for each possible cause of an event and
to identify the differences between them (Stevenson, 2005).
The Pareto Diagrams, then, is used focusing on root causes in mask industry. The
significance of Pareto chart is to calculate the important factors or majority of
influences in the research outcomes. The most root causes have been occupied
around eighty percentages. This is called “80-20 Principle”. According to the 80-20
principle, 80 per cent of effects are due to 20 per cent of causes. (Stevenson, 2005;
Tiwary, 2008).
Cause and Effect Diagrams is to explain the relationships between primary and the
secondary factors and quality characteristics (Besterfield, 2008). The final tools is
Control Chart. It presents data for the performance of one actual product
characteristic and compares current process capability with previous capability (K.
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Chen, et al., 2007; Chen, et al., 2009). The detailed explanation was early discussed
in Section 2.5 in this thesis.
6.3 Conclusions regarding the research problem
The purpose of this section is to summarise the results relating to the initial
research problem in Chapter 1 which was:
Is the Six Sigma technique an appropriate quality control methodology to improve
the entire performance in the mask industry?
To recapitulate, the findings in the case study could conclude that the Six Sigma
technique is an appropriate and ideal quality strategy in managing the overall
organisational performance for mask industry as the technique is a statistical
process control and data driven approach and is highlighted the quality is the fewest
number of defects, which must be removed as much as possible.
The Six Sigma technique was first introduced into the UEE Corporation in 2010.
Firstly, the UEE Corporation organised one Six Sigma team with the five major
positions during the preparatory work in 2009. This team included the following
positions: Executive Leader, Champion, Master Black Belt (MBB), Black Belt (BB) and
Green Belt (GB) (Cheng, 2008; Hahn, et al., 2007; Hilton, et al., 2008).
In short, the key role of the “Executive Leader” was chosen by the CEO of the UEE
Corporation to decide on applying which types of Six Sigma technique and
promoting it throughout the UEE Corporation (Antony, et al., 2001). The
“Companion” in the UEE Corporation was required not only to understand the
discipline, strategies and tools of the Six Sigma technique but also to be able to
educate other employees about the tool and its implementation (Antony &
Banuelas Coronado, 2002; Barney & McCarty, 2002; Evans, 2004).
The position of Master Black Belt (MBB) in the UEE Corporation recruited the person
who was an expert in the Six Sigma technique with the highest level of proficiency.
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MBB in the organisation was taken by a department manager to serve as a trainer,
mentor and guide (Desai & Shrivastava, 2008; Franza & Chakravorty, 2007).
Furthermore, the Black Belt (BB) was chosen to conduct a team on selecting
projects either on a full time basis or part time based on the occasion. Black Belt in
the organisation was selected to solve problems within the Six Sigma framework
and the person was trained to be technical leaders in using tools and methods to
improve quality (Barney & McCarty, 2002; Tayntor, 2007).
Finally, Green Belts (GB) were chosen to assist the Black Belt (BB) in their functional
area in the UEE Corporation. They not only used Six Sigma tools to examine and
solve continuing problems within their regular jobs (Costello, et al., 2005) but also
helped the manager in the organisation collect information, analyse data and do
other important tasks for this team in the organisation.
Those five members all came from various functions in the UEE Corporation and
they also worked part time on the project. They are very familiar with the processes
and they have attended any training courses which related to the quality control
area during the research.
The improvement phase was initiated into company by selecting the performance
characteristics from products or processes since the use of Six Sigma technique.
These characteristics were improved to achieve the goal. Employees started to
select the objective of research project and identify the critical few factors that
caused the defects when the team members went through the first four phases of
the DMAIC process. The team members are now ready to develop tests and
implement solutions and use a software package to improve the processes by
reducing the variations in the critical output variables after the investigation (Zhang
Wu & Shamsuzzaman, 2005; Zhang. Wu, et al., 2007; Xiao, et al., 2007). The results
also revealed that this technique had a positive impact on the overall production
with reducing the rate of defective goods and increasing the productivity of
acceptable masks after utilising the Six Sigma technique.
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6.4 Research evaluation for the mask industry
In this research, the Six Sigma technique has been investigated, modified and
applied to the mask industry. The Six Sigma technique is a continuous improvement
tool that can help the mask industry to control the activities of production and
improve quality (Cantrell, 1992; Tayntor, 2007).
Before implementation of the Six Sigma technique, aspects of the technique were
explained. This included a description of the roles of Green Belts (GB), Black Belts
(BB), Master Black Belts (MBB) and Champions. A manufacturing company should
select at least five employees as the members of a Six Sigma team. Those members
should understand their working areas, share their working experiences and have
backgrounds in quality control (Adams, et al., 2003; Chakravorty, 2009).
After the technique was applied in the company, the defective rate decreased. To
control the activities involved in the production process, including the acquisition of
raw materials from suppliers, the mask industry should focus on communication
between suppliers and purchase department employees.
Collaboration between departments is essential for achieving the overall goal of the
organisation. If a mask industry is well managed, the implementation the Six Sigma
technique result in the following benefits (Chau, et al., 2009; Coleman, et al., 2001;
Dale, 2002):
It can reduce the number of defects and returned goods.
It can increase the company profits and reputation.
It can decrease the variations in materials or manpower.
It can improve the customer satisfaction.
The company could enhance its production capacity and the quality if its
products.
It could also increase its products’ reliability.
- 99 -
6.5 Research limitations
There are a number of limitations that need to be identified and addressed within
this research. Firstly, this research comprised of both theoretical and practical
perspectives with the case study analysis. The longitudinal study(Cavana, et al.,
2001) was conducted to explore the phenomena at more than one point in time in
order to answer the research questions in this thesis. However, the short research
period was a prime issue as it is difficult to explore and resolve the defined
problems in this research within the short period of time.
There are many companies have achieved remarkable success in manufacturing
adopting Six Sigma technique in the business world. It is because the Six Sigma
technique would take around five years to examine and evaluate any significant
improvement of implementing the technique since an initial assurance has been
made under the normal circumstances, Therefore, it was not enough as the Six
Sigma technique has been utilised in the company for approximately one to two
months.
Secondly, difficulty in gathering the complete data from the United Excel Enterprise
Corporation was another limitation in this research. The company is located in four
locations and therefore, it increased the difficulty to obtain the prompt information
and data from the site of that company.
Finally, the research budget was a limitation for this research. To investigate and
implement the Six Sigma technique and statistical software involves large budget for
any industry. The initial institutionalisation of Six Sigma technique into the
corporation would be a significant investment This cost might discourage many
enterprises to introduce, develop and implement this technique.
Moreover, the Six Sigma technique consists of numerous preparations for
developing the quality control in organisations. The preparations are namely the
training courses, the counselling an advisory counsellor company, and so forth. The
- 100 -
complexity of preparation for Six Sigma technique therefore leads to a costly
expenditure for manufacturers. Considering the expenditure for applying the Six
Sigma technique in the organisation, it is unaffordable for small or medium
businesses to sponsor their employees wholeheartedly participating in the training
course of the Six Sigma technique without taking their job responsibility.
6.6 Recommendation and future research
The DMAIC approach of the Six Sigma technique is a technique of continuous
improvement in quality improvement. The present research investigated the use of
the Six Sigma technique in the mask industry. Based on this thesis, future research
for this industry could be done in a few areas such as budget plan, quality control
tool and manufacturing managements.
The company needs to allocate budget for training courses for its employees. In
early days the Six Sigma program, the key players will need some training about the
technique and specialist roles.
The techniques such as brainstorming sessions and nominal group techniques must
not be overlooked. Particularly, the transfer of expert knowledge from individuals to
teams through socialization practices in the Six Sigma team increases their
performance levels significantly. The managers should not only be trained in
complicated analytical techniques but should also increase their expertise in
practices for generating ideas and encouraging team members to share their
experiences.
The major purpose of the Six Sigma technique is to reduce waste and costs. It can
also improve product quality and improve the company’s reputation. In recent
decades, lean manufacturing has become popular in many industries. Practitioners
have developed “Lean Six Sigma” technique, based on the Six Sigma technique
(Breyfogle, 2010). The goals of lean manufacturing and the Six Sigma technique are
to reduce waste, increase capacity and improve the company’s reputation (Gubata,
- 101 -
2008; Pojasek, 2003; Roth & Franchetti, 2010; Sharma, 2003).
Moreover, the Six Sigma technique and lean manufacturing are related and share
the same general foundations in terms of their aim of achieving customer
satisfaction (Breyfogle, 2010). Their integration is both possible and beneficial. It
would be good opportunity to implement Lean six sigma in mask industry.
There is more scope for improvement in moving towards cost reduction, increased
product reliability, a minimisation of risks, and transparency of supplier costs and
quality and enhanced efficiency of sourcing processes.
- 102 -
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Appendix A - The Symbol of Mask Production
Process symbol Name of process Control and check items
1
Inspection of raw material
Name, specifications and quantity
2
Storage Raw material storage management
3
Material requisition Process work sheet
4
Put materials on the machine
Confirmation before operation
5
Operation Appearance, size and thickness
6
inspection Tensile strength and weld
7
Semi-finish goods in storage or bank
Quantity and coordination
8
Process the semi-finish goods on the machines
Size, tensile strength and mask direction
9
Examination Weld and size
10
Aseptic package Quantity and packaging
11
Bank or warehouse Stamp in the box
12
Warehouse Storage management
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13
Delivery and shipping Delivery note
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Appendix B - Sampling Control Method
Item Check Check item Method
1
The raw materials
inspection before
the storage
The colour’s
appearance
The material number,
category and weight
The tensile strength,
tear strength and
elongation porosity
Confirmation the
test report
Microscope
The tensile
strength machine
The callipers
machine
2 Process inspection
Size and length
Appearance and
direction
Tensile strength and
weld
Sight check
Length inspection
Tensile test
Destructive test
3 Packaging
examination
Appearance and weld
Quantity and
classification
Sight check
The counter test
4 Delivery
inspection
Specification and stamp
Description and the
number of case
Sight check
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Appendix C - Sample of Interviews
There are four questions and the interview time will be almost 10 minimums.
Question 1:
How long have you been working in this area? And what is your
position in here?
Answer 1:
I am production manager in the manufacturing department and I
have been working in the manufacturing department for almost ten
years.
Question 2:
Do you understand or clear about the quality control tools for whole
manufacturing process? And how many quality control tools or
software does this company use?
Answer 2:
I have been an operator for almost 8 years and I only can state that I
understand and clear about the manufacturing processes for around
ninety percentages. There are only two methods in this company
which are total inspection and random inspection. This company
does not utilize any particularly statistic software at this moment and
it just uses Microsoft Excel.
Question 3:
Does the department calculate the production capacity for monthly
or weekly? Why do the manufacturing and quality control
departments produce amount of defective goods each week?
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Answer 3:
In this company, the department calculates the data for monthly. And
I will also look the calculation data and observe the employees
working situations.
There are some possible reasons for providing the defective products.
For example, sometimes the workers will increase the speed for
producing the high capacity and sometimes the employees will chat
with other workers when they are manufacturing the products.
Moreover, I also have considered that the inspection method might
have inaccurate problems and the supplier might provide defective
raw materials for our company.
Question 4:
Have you want to resolve those problems by using different skill or
technique?
Answer 4:
I want to resolve those problems and increase the acceptable
product rate. Because when the manufacturing department produces
the amount of defective products, those products are costly expenses
for the business profit. If there has any better resolution or technique,
I think I will consider it.
