Achieving quality improvement in the mask manufacturing industry by using six sigma technique

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

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

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 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

Pao-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

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 D P Do Check

A PDCA C 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

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

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

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

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"

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 -

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 -

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 -

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 -

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)

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

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

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)

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

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

As discussed before, the purpose of quality control tools is to support the manufacturing process, improve product quality and reduce the numbers of product

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 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) O DMAIC efine

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

techniques Therefore, selecting a appropriate quality control technique is the prime

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

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;

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

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

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

Chapter 2 Literature Review

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

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

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

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 cu

levels requires weekly action

3) Initially, the Six Sigma technique does not play a prominent role and does

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 tech -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:

 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

 T 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

2.2.1 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

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

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

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

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)

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)] * 10 6

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

Table 1: The sigma scale Specific Limit Per cent inside specs Number of DPMO

Source: (Evans, 2004; Stevenson, 2005)

Source: (Evans, 2004; Stevenson, 2005)

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 + (1)

Where: Y is the process outcome It is the result which you desire or discover is

the process by which inputs are transformed into outcomes

factors There may be s is added after the T

transformation function is used to actually create the desired outcomes (Stewart & Spencer, 2006; Tká & Lyócsa, 2010)

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

processes (Bañuelas, Antony, & Brace, 2005; Jones, 2004; Pojasek, 2003; Redling, 2005)

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

 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)

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

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

 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

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

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

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)

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

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

Part Function Failure

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

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)

Source: Developed for this research from (Stevenson, 2005)

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

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

percentages The most root causes have been occupied around eighty percentages

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

cumulative frequency curve Pareto diagrams can also display the results of improvement programs over time (Adams, Gupta, & Wilson, 2003)

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

Figure 9: Example for Cause and Effect Diagram

Source: Developed for this research from (Chakraborty & Tah, 2006)

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)

Problem Sub-cause