Embed Size (px)
Citation preview
PERSONAL DETAIL
Name : Suresh s/o Vasu
IC No. : 830811-05-5287
Date of Birth : 11th
August 1983
Age : 24
Sex : Male
Nationality : Malaysian
Contact Address : 290 Taman Temiang Jaya, Jalan Sikamat,
70400 Seremban, Negeri Sembilan D.K.
Tel. No.(H/P) : 017-3118109
Tel. No. (Home) : 06-7634612
E-mail : [email protected]
QUALITY IMPROVEMENT USING SIX SIGMA
CONCEPTS IN INJECTION MOULDING
MANUFACTURING
SURESH A/L VASU
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
UNIVERSITI TEKNIKAL MALAYSIA, MELAKA
QUALITY IMPROVEMENT USING SIX SIGMA
CONCEPTS IN INJECTION MOULDING
MANUFACTURING
Thesis submitted in accordance of with the requirement of the Universiti Teknikal
Malaysia Melaka for the Degree of Bachelor of Engineering (Honours)
Manufacturing (Manufacturing Process)
By
SURESH S/O VASU
B050510019
Faculty of Manufacturing Engineering
April 2008
ABSTRACT
This thesis has focused on the quality improvement of major defect injection
moulding assembly line in the XXX Sdn.Bhd. The objectives of this thesis were to
identify current quality problem and to improve major quality problem in the 30 tone
injection moulding operation department using Six-Sigma DMAIC methodology. In
order to analyze the data some of Statistical Quality Control (SQC) tools were used
such as pareto chart, histogram, cause and effect diagram and control chart. The main
defects in the assembly line determined and proper tool is used to analyze the quality
problem. Major defects were highlighted and analyzed. Root causes for the problems
were determined and suggestions for improvement were suggested. After the
improvement stage, suggestions for control the quality also were suggested.
ABSTRAK
Tesis ini bertujuan untuk memperbaiki kualiti pada produk yang di hasilkan melaui
proses “injection moulding” di XXX Sdn.Bhd. Objektif tesis ini adalah, untuk
mengenalpasti masalah kualiti yang dihadapi pada masa kini di kawasan kajian dan
seterusnyamemperbaiki masalah tersebut dengan menggunakan metodologi enam-
sigma(DMAIC). Untuk menganalisa data yang diperbaiki, beberapa komponen-
komponen kawalan kualiti secara statistik (SQC) seperti rajah pareto, histogram,
rajah sebeb dan akibat dan rajah kawalan. Masalah-masalah kualiti yang wujud pada
produk yang dihasilkan dikesan. Daripada masalah-masalah ini masalah utama akan
dikenalpasti dan dianalisis. Sebab-sebab utama masalah tersebut berlaku dan
cadangan untuk memperbaiki masalah tersebut akan dicadangkan. Selepas itu,
cadangan untuk mengawal kualiti pada produk siap juga akan dicadangkan.
DEDICATION
This thesis is dedicated to my parents, aunty, brother, sisters and other family
members who provide a loving, caring, encouraging and supportive atmosphere.
These are characteristics that contribute to the environment that is always needed to
achieve the goals ahead.
ACKNOWLEDGEMENTS
Many people have contributed to my learning experience at the Universiti Teknikal
Malaysia, Melaka. I would like to thank for my thesis advisor MR. HAERY
SIHOMBING and co- advisor PN.ROHANA, for his and her insight, thought
provoking questions and guidance for my thesis.
I also would like to thank MR.SIVA who is a senior engineer in XXX Malaysia for
his contribution for this thesis. He has spent his valuable time to guide me to gather
some information for this thesis. I also would like to thank other XXX staffs who
contribute directly or indirectly in this thesis.
Finally, I would like to thank my family members and friends who provide a loving,
caring, encouraging and supportive atmosphere.
TABLE OF CONTENTS
Declaration ………………………………………………………………..…….…..i
Approval…………………………………………………………………………...…ii
Abstract…………………………………………………………………….….….iii
Abstrak……………………………………………………..……………….…..…...iv
Dedication ………………………………………………….……………….….…..v
Acknowledgement …………………………………………….……….….…....….vi
Table of Contents ……………………………………………….……….………...vii
List of Figures …………………………………………………….………....…... xi
List of Tables ………………………………………………………………..….xiii
List of List Of Abbreviations, Symbols, Specialized Nomenclature ……………..xiv
1. INTRODUCTION………………………………………………………......…….1
1.1 Background ……………………………………………………..……....……1
1.2 Problem Statements……………………………………………...…...….……4
1.3 Objectives of the Research……………………………………...…...….…….4
1.4 Scope Of Project………………………………………………...…...….….…4
1.5 Project Overview……………………………………………...……………....5
2. LITERATURES REVIEW……………………………………....…....…….....6
2.1 Introductions …………………………………………………..…...…..……..6
2.2 Definitions of quality…………………………………………….…….6
2.3 Quality Management Philosophies ……………………………………...…….7
2.3.1 The Deming Philosophy …………………………………………….…8
2.3.1.1 Deming's 14 Points for Management …………………………….......9
2.3.2 Juran’s Quality Trilogy……………………………………………...…10
2.3.3 The Crosby philosophy ……………………………………………......11
2.4 Introduction and Implementation of Total Quality Management (TQM)….....12
2.4.1 TQM Defined…………………………………………………….........12
2.4.2 Implementation Principles and Processes of TQM……………………..13
2.4.3 Summary for TQM…………………………………………………….14
2.5Six-Sigma Quality………………………………………………………...…...15
2.5.1 Six-Sigma methodology…………………………………………………16
2.5.1.1 Define (D)……………………………………………………..……..17
2.5.1.2 Measure (M)………………………………….………………...…......18
2.5.1.3 Analyze (A) …………………………………………..………..…..…19
2.5.1.4 Improve (I) …………………………………………………..…..……20
2.5.1.5 Control (C) ……………………………………………………..……21
2.6Analytical tools for Six-Sigma and continuous improvement……………….23
2.7Six-Sigma versus Total Quality Management (TQM) ………………………...29
2.8Six-Sigma versus Other Quality system or tools……………………..……......31
2.8.1 ISO 9001 objectives…………..……………………………………...….31
2.8.1.1 Comparison of ISO 9001 with Six Sigma……………………..…....31
2.8.1.2 Combining Six Sigma with ISO………………………..…….….........31
2.8.2 Lean manufacturing objectives……………….………………….……..…32
2.8.2.1 Comparison with Six Sigma………….………….…………………....33
2.8.2.2 Combining Lean Manufacturing with Six Sigma…………….……...34
2.8.3 Comparison between six sigma DMAIC and PDCA…………………..35
2.9Case study: An example of DMAIC at American Express……………………35
2.9.1 The general situation…………………….………………………...…….36
2.9.2 Define and Measure…………….…………………………………....….36
2.9.3 Analyze……………….…………………………………………......…..36
2.9.4 Improve…………………………………….……………………...…....37
2.9.5 Control………………………….………………………………..……...37
2.10 Summary …………………………………………….………………...….....37
3.METHODOLOGY………………….……………………….………….……....39
3.1Introduction………………………………………………….………...….....39
3.2Company selection……………………………………….…………...……..39
3.3 Methodology………………………………………...………………...…….39
3.3.1 Define stage. ………………………………...………………..……….41
3.3.2 Measure stage………………………………..………………….……….41
3.3.3 Analyze stage………………………………...…………...…………….42
3.3.4 Improve stage…………………………………..……………….…...….42
3.3.5 Control stage…………………………………………..…………..…….42
3.4Techniques used in identifying the general and major problem…….……....…43
3.4.1 Interview…………………………………………….…..………...…...43
3.4.2 Observation……………………………………………..……….…….43
3.4.3 Data Collection……………………………………….………...…..….43
3.5Gantt chart ………………………………………………….………..…..…….44
3.6 Summary ………………………………………………...………....……….45
4. COMPANY BACKGROUND………………………………………………...46
4.1 Introduction………………………………………………….……..…...…….46
4.2 Companies Profile…………………………………………………...…...….46
4.3 Companies Location………………………………………………...……….48
4.4Company’s Product…………………………………………………….….….49
4.5 Introduction To Injection Moulding ………………………….……….……50
4.5.1 Injection Molding Cycle & Process…………………………..…….…52
4.5.2 Moulding Defects………………………………………………….…… 54
5. RESULT AND DISCUSSION…………………………………..….……..56
5.1 Introductions ………………………………………………………………...56
5.2 DMAIC – Define stage………………………………………………………56
5.2.1 Define the process……………………………………………………….56
5.2.2 Molding Process Flow Chart………………………………………….58
5.2.3 Identify the current reject problem……………………………………59
5.3 DMAIC- Measure stage……………………………………………………..60
5.4 DMAIC- Analyze stage………………………………………………………63
5.4.1 Potential causes for high defects occurred in part BMQ case A………65
5.4.2 Root causes analysis…………………………………………………....66
5.4.2.1 Root causes analysis for Black dot defect……………………………66
5.4.3 Summary on the analysis………………………………………………68
5.5 DMAIC- Improve stage………………………………………………………69
5.5.1 Introduction……………………………………………………………69
5.5.2 Screw and barrel cleaning……………………………………………69
5.5.2.1 Screw cleaning……………………………………………………….69
5.5.2.2 Barrel cleaning……………………………………………………….70
5.5.3 PP and special material for cleaning screw and barrel by purging……72
5.5.3.1 Characteristic of cleaning agents……………………………………72
5.5.3.2 Comparison between the cleaning agents……………………………73
5.5.3.3 Result after implementation of both cleaning agents…………………76
5.5.4 Summary on improve stage……………………………………………81
5.6 DMAIC- Control stage………………………………………………………81
5.6.1 Control chart…………………………………………………………….81
5.6.1.1 Suggested steps in constructing a c-chart……………………………81
5.7Summary……………………………………………………………………… 83
6. CONCLUSION…………………………………………………………………84
6.1 Conclusion…………………………………………………………………..84
6.2 Suggestion for further study…………………………………………………84
REFERENCES………………………………...…………………………………86
APPENDICES
A List of rejection or part from month JAN to MAC 2007
B List of rejection type for month May to Oct 2007
C Overall calculation for sigma level
LIST OF FIGURES
2.1 SIPOC analysis diagram for Define stage 23
2.2 Measurement stage 25
2.3 Cause and Effect diagram for analyze stage 26
2.4 Opportunity flow diagram for Improve stage 27
2.5 Control chart for Control stage 28
3.1 Methodology flow chart 40
4.1 The graphic above shows the Nilai Plant layout 47
4.2 Plant location 48
4.3 Remote controllers 49
4.4 Sumitomo injection moulding machine 51
5.1 The Cover molding process 58
5.2 In- line rejection 60
5.3 In-line reject from month May to October 2007 62
5.4 Sigma level from month May to October 2007 63
5.5 Reject data based on the defect type for month May 2007 64
5.6 Potential causes for high defects 66
5.7 Root causes analysis for Black dot Injection screw before cleaning 68
5.8 Injection screw before cleaning 69
5.9 Injection screw after cleaning 70
5.10 Barrel before cleaning 70
5.11 Barrel after cleaning 71
5.12 Machine covered with plastic 71
5.13 Black Dot trend before and after screw cleaning for machine E03 72
5.14 Cleaning Agent 1 (PP) 73
5.15 Cleaning Agent 2 (Special material) 74
5.16 Purging process of Agent 1 (PP) 74
5.17 Purging process for Agent 2 (special material) 75
5.18 Black dot trend before and after special material cleaning 76
LIST OF TABLES
2.1 TQM VS Six-Sigma 30
2.2 Comparison between DMAIC and PDCA (Donna C.S summers, 2003) 35
3.1 Gantt chart 44
4.1 Injection moulding cycle 53
4.2 Common moulding defects 54
5.1 In- line rejection based on part produced 59
5.2 Total output and Sigma level 61
5.3 Reject data based on the defect type for month May 2007 64
5.4 Comparison with Special Cleaning material and Current use Material (PP) 75
5.5 Cost calculation for the material price and amount 77
5.6 Cost calculation for down time and labor cost 78
5.7 Cost calculation for Scrap data 79
5.8 Cost calculation for Scrap data 80
LIST OF ABBREVIATIONS, SYMBOLS, SPECIALIZED
NOMENCLATURE
ANOVA - Analysis of Variance
CTQ - Critical to Quality
DMAIC - Define, Measure, Analyze, Improve, Control
DOE - Design of experiment
DPMO - Defect per Million Opportunities
FMEA - Failure Mode and Effect Analysis
KPOV - Key Process Output Variables
KPIV - Key Process input Variables
PDCA - Plan, Do, Check, Act
SIPOC - Supplier, Inputs, Process, Outputs, Customers
SPC - Statistical Process Control
TQM - Total Quality Management
SQC - Statistical Quality Control
CHAPTER 1
INTRODUCTION
1.1 Project Background
Quality has become one of the most important competitive strategic tools which
many organizations have realized it as a key to develop products and services in
supporting continuing success. Quality system is designed to set a clear view for
organization to follow enabling understanding and involvement of employees
proceeding towards common goal.
The aim of business is long term profitability. Over a considerable length of time,
earning is achieved by pleasing customers with good products or services while
keeping production cost at a minimum. The use of quality tools and technique
provides long term dividends through lower costs and productivity improvements.
As competition increases and changes occur in the business world, one should need
to have a better understanding of quality. Quality concerns affect the entire
organization in every competitive environment. Consumer demands high quality
level of product or services at reasonable prices to achieve value and customers
satisfaction.
There is an increasing focus on quality throughout the world. With increased
competition, companies have recognized the importance of quality system
implementation in maintaining effectiveness in a volatile business environment.
Specifically meeting the needs and desired of the customers is critical and must be
done much better and efficiently than it has done in the past.
Total Quality Management (TQM) is one of the most common quality management
practices in today’s industrial environment. TQM refer to the broad set of
management and control processes designed to focus an entire organization and all of
its employees on providing products or services that do the best possible job of
satisfying the customer. According to Sashkin and Kiser (1993), TQM means that the
organization’s culture is defined by, supports, the constant attainment of customer
satisfaction through an integrated system of tools, techniques, and training. This
involves the continuous improvement of organizational process, resulting in high
quality products and services.
Thus, the TQM philosophy of management is customer-focused. TQM incorporates
the concepts of product quality, process control, quality assurance, and quality
improvement. Some advise that customer satisfaction is the driving force behind
quality improvement; other suggest quality management is achieved by internal
productivity or cost improvement programs; and still others consider TQM as mean
to introduce participatory management. In general, the Japanese concentrate on
customer satisfaction with a particular focus on understanding customer needs and
expectations.
Besides TQM there are other quality system used to improve quality such as Lean
and Six Sigma. These two are related, but distinct. Among the several quality
management concepts that have been developed, the lean concept, as in lean
manufacturing, lean production, etc. is one of the more wide-spread and successful
attempts. Briefly, lean is about controlling the resources in accordance with the
customers’ needs and to reduce unnecessary waste (including the waste of time). The
concept was introduced at a larger scale by Toyota in the 1950s, but not labeled lean
manufacturing until the now famous book about the automobile appeared in 1990
(Womack et al., 1990). While there are many formal definitions of the lean concept,
it is generally understood to represent a systematic approach to identifying and
eliminating elements not adding value to the process. Consequences of this are
striving for perfection and a customer-driven pull of the process.
Meanwhile, most recent quality philosophy to be adopted by businesses around the
world is known as “Six Sigma.” The founder of the “Six Sigma” philosophy is Mikel
Harry (Harry and Schroeder, 2000). Mikel Harry developed and implemented his
“Six Sigma” philosophy with the Motorola Corporation and the philosophy has had
great success at the GE Corporation (Harry and Schroeder, 2000). Six sigma focuses
on the reduction and removal of variation by the application of an extensive set of
statistical tools and supporting software. This powerful business management
strategy has been exploited by many world class organizations such as General
Electric (GE), Motorola, Honeywell, Bombardier, ABB, Sony, to name a few from
the long list. Six sigma applications in the service sector are still limited although it
has been embraced by many big service oriented companies such as J P Morgan,
American Express, Lloyds TSB, Egg, City Bank, Zurich Financial Services, BT, etc.
Six sigma today has evolved from merely a measurement of quality to an overall
business improvement strategy for a large number of companies around the world.
The concept of six sigma was introduced by Bill Smith in 1986, a senior engineer
and scientist within Motorola’s communication Division, in response to problems
associated with high warranty claims. The success of the efforts at Motorola was not
just achieving six sigma quality level rather the focus was on reducing defect rate in
processes through the effective utilization of powerful and practical statistical tools
and techniques. This would lead to improved productivity, improved customer
satisfaction, enhanced quality of service, reduced cost of operations or costs of poor
quality, and so on.
This thesis mainly focused on six sigma quality philosophy and other related
philosophy that would be implemented in these studies in order to identify the
current problem or rejection criteria facing by the company. The “Six Sigma”
philosophy used because, it provides a step-by-step quality improvement
methodology that uses statistical methods to quantify variation. An extensive on
related literature reviews was carried in order to enhance more knowledge on the
related study field.
1.2 Problem Statement
Currently in XXX Electric Parts Production Department which produce Plastic
product for Electronic Component especially Remote Control as main business
facing many rejection problem. The main defect cause this rejection is “Black dot”
on the appearance of the product. There are also some other causes that lead to
rejection such as part breakage, scratches, oily surface, white mark silver mark,
parting burr and etc. In order to study the problem a research has carried out with
help of an engineer by study the literature review on TQM, Six Sigma and PDCA
philosophies and other reference for this analysis and research method.
1.3 Objectives
The objectives of this thesis are:
To utilize six sigma methodology in performing the study.
To study the “black dot” rejects utilizing QC tools at the identified assembly
lines.
To identify the root causes of the “black dot” rejects
To recommend actions to improve the black dot rejects and sigma level.
1.4 Scope Of Project
The scope of the study is limited to part production 30 tone assembly lines only and
the analysis is focused on major defect only. Six Sigma DMAIC methodologies will
be used where DMA is applied and IC will be suggested to the company. The data
will be collected for the assembly line for six month period from May 07 to October
07.
1.5 Report outline
Chapter 1 gives an introduction to the projects which are including objectives, scope,
and background. In this chapter, it describes the background of quality problem as
the case study of Company.
Chapter 2 presents the literature review on concepts of TQM, Six-Sigma Quality, and
what the correlation about DMAIC with other Quality Improvement approaches
(PDCA). It also presents some quality tools that incorporate with the study.
Chapter 3 describes the company background and the description of the methodology
used in this project.
Chapter 4 presents the data analysis using Six-Sigma methodology. In this chapter
the collected data from the case Study Company was analyzed stage by stage. First,
the analysis starts with Define stage, which is continued with Measurement stage and
then followed by Analyze stage. After analyze the problem based on the data
collected, and then we go to the Improve stage and culminate with Control stage.
Chapter 5 presents the conclusions of the whole project and suggestions for future
work.
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
Literature review includes study and research of published materials like journals,
thesis, case studies, technical documents and online library. Generally, the purpose of
a review is to analyze critically a segment of a published body of knowledge through
summary, classification and comparison of prior research studies, reviews of
literature, and theoretical articles. This chapter will describe topics that related to
quality such as Total Quality Management, Quality Management Philosophies, Six-
Sigma methodology, ISO 9000, Lean manufacturing, concept of quality, quality tools
and other relevant quality topics. Emphasizes is more on six sigma methodology
since the study conducted in a Six Sigma manner.
Besides that, this chapter also includes review on injection moulding process which
currently applied by the studied company and the types of defects that frequently
occurs in the production line.
2.2 Definitions of quality
In the Webster’s New World Dictionary, quality is defined as physical or
nonphysical characteristic that constitutes the basic nature of a thing or is one of its
distinguishing features.
Shewhart (1980), mention that there are two common aspects of quality; one of these
has to do with the consideration of the quality of a thing as an objective reality
independent of the existing of man. The other has to do with what we think, feel or
sense as a result of the objective reality. This subjective side of quality is closely
linked to value. It is convenient to think of all matters related to quality of
manufactured product in terms of these three functions of specification, production
and inspection. (Grant and Leavenworth, 1988).
Quality is fitness for use, (Juran, 1989). Quality is conformance to requirements
(Crosby, 1986) and quality should be armed at the needs of the customer present and
future (Deming, 1986).
Feigenbaum (1983) said that quality is the total composite product and service
characteristics of marketing, engineering, manufacture and maintenance through
which he product and service in use will meet the expectations of the customer.
Mizuno (1988) mention that product quality encompasses those characteristics which
the product most posses if it is to be used in the intended manner. Actually, quality
can take many forms. All the definitions mentioned above can be classified into three
types. They are quality of design, quality of conformance and quality of
performance. Quality of design means that the product has been designed to
successfully fill a consumer need, real or perceived. Quality of conformance refers to
the manufacture of the product or the provision of the service that meets the specific
requirements that set by customer. Finally, quality of performance brings out the
definitions that the product or service performance its intended function as identified
by the customer.
2.3 Quality Management Philosophies
More managers than ever before are focusing on quality as a way of increasing
productivity, reducing costs, and meeting customer needs. These managers are
beginning to understand the importance of continuously improving the quality of
their services and products as a means of achieving these goals. Those who begin to
learn about quality quickly become familiar with the names of Philip B. Crosby,
W.Edwards Deming, and Joseph M. Juran--renowned quality experts--who have
been carrying forth the message of quality for more than 30 years. At an age when
most people have retired, Philip B. Crosby and Joseph M. Juran continue an untiring
pace of work conducting seminars, consulting with clients, and writing new texts.
They have devoted their lives to helping organizations improve the quality of their
products and services. Their influence is now worldwide and their accomplishments
are legendary in the discipline.
2.3.1 The Deming Philosophy
W. Edwards Deming was originally trained as a statistician, and much of his
philosophy can be traced to these roots. He worked for Western Electric during its
pioneering era of statistical quality control development in the 1920s and 1930s.
During World War II, he taught quality control courses as part of the national
defense effort. Deming began teaching statistical quality control in Japan shortly
after Word War II a is credited with having been an important contributor to the
Japanese quality improvement programs. In fact, the highest award for quality
improvement in Japan is called the Deming Prize. While Japan embraced his
methods for 30 years, he was virtually unknown in the United States until 1980.
Deming focuses on the improvement of product and service conformance to
specifications by reducing uncertainty and variability in the design and
manufacturing process. In Deming's view, variation is the chief culprit of poor
quality. In mechanical assemblies, for example, variations from specifications for
part dimensions lead to inconsistent performance and premature wear and failure.
Likewise, inconsistencies in service frustrate customers and hurt the reputation of the
company. To achieve reduction of variation refines a never-ending cycle of product
design, manufacture, test, and sales, followed by market surveys, then redesign, and
so forth. Deming claims that higher quality leads to higher productivity, which in
turn leads to long-term competitive advantage. The Deming "chain reaction" theory
summarizes this view; the theory states that process improvements lead to lower
costs due to less rework, fewer mistakes, delays and snags, and more efficient use of
materials. Lower costs, in turn, lead to productivity improvements. With better
quality and lower prices, the firm can achieve a greater or larger market share and
remain competitive and provide more meaningful and rewarding jobs. Upper
management needs to recognize the benefits of quality as a strategic factor and strive
to create a culture that supports empowerment, continuous improvement and
customer satisfactions. Deming stresses that top management has the overriding
responsibility for quality improvement (Deming, 1986)
2.3.1.1 Deming's 14 Points for Management
1. Create and publish to all employees a statement of the aims and
purposes of the company or other organization. Management must
demonstrate constantly their commitment to this statement
2. Learn the new philosophy throughout all areas everybody.
3. Understand the purpose of inspection. It should evaluate process
improvements and cost reductions.
4. End the practice of awarding business on the basis of price alone
5. Improve constantly and forever the system of production and service
6. Institute training
7. Teach and institute leadership
8. Drive out fear. Create trust. Create a climate for innovation
9. Optimize all efforts toward the aims and purposes of the company.
10. Eliminate exhortations for the work force
11. (a) Eliminate numerical quotas for production Instead learn and institute
methods for improvement
(b) Eliminate management by objectives (MBO). Instead, learn the
capabilities of processes, and how to improve them.
12. Remove barriers that rob people of pride of workmanship.
13. Encourage education and self-improvement for everyone.
14. Take action to accomplish the transformation
2.3.2 Juran’s Quality Trilogy
Dr. J. M. Juran, whose impact on the quality movement in Japan, was second only to
Deming’s, developed a useful framework to what referred to as "a universal thought
process-a universal way of thinking about quality, which fits all functions all levels,
all product lines.” He called it the "quality trilogy”: The underlying concept of the
quality trilogy is that managing for quality consists of three basic quality oriented
processes:
• Quality planning
• Quality control
• Quality improvement
The starting point is quality planning which involves creating a process that will be
able to established goals. Once the process is turned over to the operating forces,
their responsibility is to run the process at optimal effectiveness and take corrective
action when the process or product does not conform to established specifications.
Finally, quality improvement is "the process for breaking through to unprecedented
levels of performance. “But quality improvement does not happen of its own accord.
It results from purposeful action taken by upper management to introduce a new
managerial approach throughout the organization of quality improvement process.
This quality improvement process is super-imposed on the quality control process. It
is implemented in addition to quality control, not instead of it. Juran's approach is
essentially the same as Deming’s. Quality is a management responsibility that needs
to be performed systematically to achieve continuous improvement over time.
This is the same basic idea behind the so-called PDCA cycle, known in Japan as the
Deming wheel, which is considered to be the essence of the Japanese approach to
total quality control:
Plan: The basic planning process described by Juran.
Do: The implementation of the plan.
Check: Evaluation of performance according to critical measures appropriate
methods
Act: Quality improvement efforts based on the lessons learned from
experiences. These experiences feed into the new plan, since PDCA is a
cyclical process (Costin, 1994)
2.3.3 The Crosby philosophy
Philip B. Crosby was corporate vice president for quality at International Telephone
and Telegraph (ITT) for 14 years after working his way up from line inspector. After
that he e established Philip Crosby Associates in 1979 to develop and offer training
programs related to quality. He is also the author of several popular books. His first
book, Quality is Free published in 1979, sold about one million copies.
The essence of Crosby's quality philosophy is embodied in what he calls the
"Absolutes of' Quality Management and the Basic Elements of Improvement."
Crosby's Absolutes of Quality Management areas follow:
Quality means conformance to requirement, not elegance
There is no such thing as quality problem only opportunities to improve.
There is no such thing as the economics of quality; it always cheaper to
do the job right the first time.
The only performance measurement is the cost of quality approach.
The only performance standard is “Zero Defect”
Crosby's “Basic Elements of Improvement” include determination, education, and
implementation. By determination, Crosby means that top management must be
serious about quality improvement. The “Absolutes” should be understood by
everyone; this can be accomplished only through education. Finally, every member
of the management team must understand the implementation process. (Evans &
Lindsay,1993).
2.4 Introduction and Implementation of Total Quality Management
(TQM)
Total Quality Management is a management approach that originated in the 1950's
and has steadily become more popular since the early 1980's. Total Quality is a
description of the culture, attitude and organization of a company that strives to
provide customers with products and services that satisfy their needs. The culture
requires quality in all aspects of the company's operations, with processes being done
right the first time and defects and waste eradicated from operations.
Total Quality Management, TQM, is a method by which management and employees
can become involved in the continuous improvement of the production of goods and
services. It is a combination of quality and management tools aimed at increasing
business and reducing losses due to wasteful practices. Some of the companies who
have implemented TQM include Ford Motor Company, Phillips Semiconductor,
SGL Carbon, Motorola and Toyota Motor Company (Gilbert, 1992).
2.4.1 TQM Definination
TQM is a management philosophy that seeks to integrate all organizational functions
such as marketing, finance, design, engineering, and production, customer service,
etc. to focus on meeting customer needs and organizational objectives.
TQM views an organization as a collection of processes. It maintains that
organizations must strive to continuously improve these processes by incorporating
the knowledge and experiences of workers. The simple objective of TQM is "Do the
right things, right the first time, every time". TQM is infinitely variable and
adaptable.
Although originally applied to manufacturing operations, and for a number of years
only used in that area, TQM is now becoming recognized as a generic management
tool, just as applicable in service and public sector organizations. There are a number
of evolutionary strands, with different sectors creating their own versions from the
common ancestor.
TQM is the foundation for activities, which include commitment by senior
management and all employees, meeting customer requirements, reducing
development cycle times, Just In Time/ Demand flow manufacturing and
improvement teams. This shows that all personnel, in Manufacturing, Marketing,
Engineering, R&D, Sales, Purchasing, HR, etc must practice TQM in all activities.
(Hyde,1992).
2.4.2 Implementation Principles and Processes of TQM
A preliminary step in TQM implementation is to assess the organization's current
conditions. Relevant preconditions have to do with the organization's history, its
current needs, precipitating events leading to TQM, and the existing employee
quality of working life. If the current reality does not include important
preconditions, TQM implementation should be delayed until the organization is in a
state in which TQM is likely to succeed.
If an organization has a track record of effective responsiveness to the environment,
and if it has been able to successfully change the way it operates when needed, TQM
will be easier to implement. If an organization has been historically reactive and has
little skill at improving its operating systems, there will be both employee skepticism
and a lack of skilled change agents. If this condition prevails, a comprehensive
program of management and leadership development may be instituted. A
management audit is a good assessment tool to identify current levels of
organizational functioning and areas in need of change. An organization should be
basically healthy before beginning TQM. If it has significant problems such as a very
unstable funding base, weak administrative systems, lack of managerial skill, or poor
employee morale, TQM would not be appropriate (Tichey, 1993).
However, a certain level of stress is probably desirable to initiate TQM. People need
to feel a need for a change. Kanter (1983) addresses this phenomenon as building
blocks, which are present in effective organizational change. These forces include
departures from tradition, a crisis or galvanizing event, strategic decisions, individual
"prime movers," and action vehicles. Departures from tradition are activities, usually
at lower levels of the organization, which occur when entrepreneurs move outside the
normal ways of operating to solve a problem. A crisis, if it is not too disabling, can
also help create a sense of urgency, which can mobilize people to act. In the case of
TQM, this may be a funding cut or threat, or demands from consumers or other
stakeholders for improved quality of service. After a crisis, a leader may intervene
strategically by articulating a new vision of the future to help the organization deal
with it. A plan to implement TQM may be such a strategic decision. Such a leader
may then become a prime mover, who takes charge in championing the new idea and
showing others how it will help them get where they want to go. Finally, action
vehicles are needed and mechanisms or structures to enable the change to occur and
become institutionalized (Smith, 1999).
2.4.3 Summary for TQM
TQM encourages participation amongst shop floor workers and managers. There is
no single theoretical formalization of total quality, but Deming, Juran and Ishikawa
provide the core assumptions, as a:
"...discipline and philosophy of management which institutionalizes planned and
continuous improvement ... and assumes that quality is the outcome of all activities
that take place within an organization; that all functions and all employees have to
participate in the improvement process; that organizations need both quality systems
and a quality culture."
2.5 Six-Sigma Quality
Six-Sigma refers to the philosophy and methods companies such as General Electric
and Motorola use to eliminate defects in their products and processes. A defect is
simply any component that does not fall within the customer’s specification limits.
Each step or activity in a company represents an opportunity for defects to occur and
Six-Sigma programs seek to reduce the variation in the processes that lead to these
defects. Indeed, Six-Sigma advocates see variation as the enemy of quality and much
of the theory underlying Six-Sigma is devoted to dealing with this problem. A
process that is in Six-Sigma control will produce no more than 3.4 defects out of
every million units.
One of the benefits of Six-Sigma thinking is that it allows managers to readily
describe the performance of a process in terms of its variability and to compare
different processes using a common metric. This metric is defects per million
opportunities (DPMO). (Raisinghani,M.S 2005).This calculation requires three
pieces of data:
Unit: The item produced or being serviced.
Defect: Any item or event that does not meet the customer’s
requirements.
Opportunity: A chance for a defect to occur.
A straightforward calculation is made using the following formula:
2.5.1 Six-Sigma methodology
Six Sigma’s methods include many of the statistical tools that were employed in
other quality movements, Six-Sigma is employed in a systematic project-oriented
fashion through define, measure, analyze, improve, and control (DMAIC) cycle. The
DMAIC cycle is a more detailed version of the Deming PDCA cycles, which
consists of four steps Plan, Do, Check, and Act within continuous improvement.
Continuous improvement, also called Kaizen, seeks continual improvement of
machinery, materials, labor utilization, and production methods through application
of suggestions and ideas of company teams. Like Six Sigma, it also emphasizes the
scientific method, particularly hypothesis testing about the relationship between
process inputs (X’s) and outputs (Y’s) using design of experiments (DOE) methods.
The availability of modern statistical software has reduced the drudgery of analyzing
and displaying data and is now part of the Six-Sigma tool kit. The overarching focus
of the methodology, however is, understanding and achieving what the customer
wants, since that is seen as the key to profitability of a production process. In fact, to
get across this point, some use the DMAIC as an acronym for “Dumb Managers
Always Ignore Customers.”
The standard approach to Six-Sigma projects is the DMAIC methodology developed
by General Electric (G.E). The DMAIC methodology is central to Six Sigma process
improvement projects. The following phases provide a problem-solving process in
which specific tools are employed to turn a practical problem into a statistical
problem, generate a statistical solution and then convert that back into a practical
solution (Henderson, Evans and et al, 2000).
DPMO = Number of defects
Number of opportunities for error per unit x Number of unit
X 1,000,000
2.5.1.1 Define (D)
The purpose of the Define phase is to clearly identify the problem, the requirements
of the project and the objectives of the project. The objectives of the project should
focus on critical issues, which are aligned with the company’s business strategy and
the customer’s requirements. The Define phase includes:
Define customer requirements as they relate to this project. Explicit customer
requirements are called Critical-to-Quality (CTQ) characteristics;
Develop defect definitions as precisely as possible;
Perform a baseline study (a general measure of the level of performance
before the improvement project commences);
Create a team charter and Champion;
Estimate the financial impact of the problem; and
Obtain senior management approval of the project
Some of the key questions addressed in this stage are:
What matters to the customers?
What Defect are we trying to reduce?
By how much and by when?
What is the current Cost of defects?
Who will be in the project team?
Who will support us to implement this project?
The most applicable tools in this phase are the following:
Project Charter - this document is intended to clearly describe the
problem, defects definitions, team information and deliverables for a
proposed project and to obtain agreement from key stakeholders.
Trend Chart - to see (visually) the trend of defect occurrence over a
period of time.
Pareto Chart - to see (visually) how critical each input is in contributing
negatively or positively to total output or defects.
Process Flow Chart - to understand how the current processes functions
and the flow of steps in current process
2.5.1.2 Measure (M)
The purpose of the Measure phase is to fully understand the current performance by
identifying how to best measure current performance and to start measuring it. The
measurements used should be useful and relevant to identifying and measuring the
source of variation. This phase includes:
Identifying the specific performance requirements of relevant Critical-to-
Quality
(CTQ) characteristics;
Map relevant processes with identified Inputs and Outputs so that at each
process step, the relevant Outputs and all the potential Inputs (X) that might
impact each Output are connected to each other;
Generate list of potential measurements
Analyze measurement system capability and establish process capability
baseline;
Identify where errors in measurements can occur;
Start measuring the inputs, processes and outputs and collecting the data;
Validate that the problem exists based on the measurements;
Refine the problem or objective (from the Analysis phase)
Some of the key questions addressed in this stage are:
What is the Process? How does it function?
Which Outputs affect CTQ’s most?
Which Inputs affect Outputs (CTQ’s) most?
Is our ability to measure/detect sufficient?
How is our current process performing?
What is the best that the process was designed to do?
The most applicable tools at this phase include the following:
Fishbone Diagram – to demonstrate the relationships between inputs and
outputs
Process Mapping - to understand the current processes and enables the team
to define the hidden causes of waste.
Preliminary Failure Mode & Effect Analysis (FMEA) - using this in the
Measure phase helps to identify and implement obvious fixes in order to
reduce defects and save costs as soon as possible.
Gauge Repeatability & Reproducibility (GR&R) - used to analyze the
variation of components of measurement systems so minimize any
unreliability in the measurement systems.
2.5.1.3 Analyze (A)
In the Analyze phase, the measurements collected in the Measure phase are analyzed
so that hypotheses about the root causes of variations in the measurements can be
generated and the hypothesis subsequently validated. It is at this stage that practical
business problems are turned into statistical problems and analyzed as statistical
problems. This includes:
generate hypotheses about possible root causes of variation and potential
critical
Inputs (X’s);
identify the vital few root causes and critical inputs that have the most
significant impact; and
Validate these hypotheses by performing Multivariate analysis.
Some of the key questions addressed in this stage are:
Which Inputs actually affect our Critical to quality’s most (based on actual
data)?
By how much?
Do combinations of variables affect outputs?
If input is changed, does the output really change in the desired way?
How many observations are required to draw conclusions?
What is the level of confidence?
The Analyze phase offers specific statistical methods and tools to isolate the key
factors that are critical for a comprehensive understanding of the causes of
defects:
Tests for normality (Descriptive Statistics, Histograms) – this is used to
determine if the collected data is normal or abnormal so as to be properly
analyzed by other tools.
Correlation/Regression Analysis - to identify the relationship between
process inputs and outputs or the correlation between two different sets of
variables.
Analysis of Variances (ANOVA) - this is an inferential statistical
technique designed to test for significance of the differences among two
or more sample means.
FMEA (Failure Mode and Effect Analysis) - applying this tool on current
processes enables identification of sufficient improvement actions to
prevent defects from occurring.
Hypothesis testing methods - these are series of tests in order to identify
sources of variability using historical or current data and to provide
objective solutions to questions, which are traditionally answered
subjectively.
Cause & Effect Matrix - to quantify how significant each input is for
causing variation of outputs.
2.5.1.4 Improve (I)
The Improve phase focuses on developing ideas to remove root causes of variation,
testing and standardizing those solutions. This involves:
identify ways to remove causes of variation;
verify critical Inputs;
discover relationships between variables;
establish operating tolerances which are the upper and lower specification
limits (the engineering or customer requirement) of a process for judging
acceptability of a particular characteristic, and if strictly followed will
result in defect-free products or services;
Optimize critical Inputs or reconfigure the relevant process.
Some of the key questions addressed in this stage are:
Once we know for sure which inputs most affect our outputs, how do we
control them?
How many trials do we need to run to find and confirm the optimal
setting/procedure of these key inputs?
Who should the old process be improved and what is the new process?
How much have Defects per Millions Opportunities (DPMO) decreased?
The most applicable tools at this phase are:
Process Mapping - this tool helps to represent the new process subsequent
to the improvements.
Process Capability Analysis (Cpk) - in order to test the capability of
process after improvement actions have been implemented to ensure we
have obtained a real improvement in preventing defects.
DOE (Design of Experiment) - This is a planned set of tests to define the
optimum settings to obtain the desired output and validate improvements.
2.5.1.5 Control (C)
The Control phase aims to establish standard measures to maintain performance and
to correct problems as needed, including problems with the measurement system.
This includes:
validate measurement systems;
verify process long-term capability;
Implement process control with control plan to ensure that the same
problems don’t reoccur by continually monitoring the processes that
create the products or services.
Some of the key questions addressed in this stage are:
Once defects have been reduced, how do we ensure that the improvement
is sustained?
What systems need to be in place to check that the improved procedures
stay implemented?
What do we set up to keep it going even when things change?
How can improvements be shared with other relevant people in the
company?
Most applicable tools at the Control phase include:
Control Plans this is a single document or set of documents that outlines
the actions, including schedules and responsibilities, which are needed to
control the key process inputs variables at the optimal settings.
Operating Flow Chart(s) with Control Points - this is a single chart or
series of charts that visually display the new operating processes.
Statistical Process Control (SPC) charts - these are charts that help to
track processes by plotting data over time between lower and upper
specification limits with a center line.
Check Sheets - this tool enables systematic recording and compilation of
data from historical sources, or observations as they happen, so that
patterns and trends can be clearly detected and shown (Hagemeyer and
Gershenson, 2005).
2.6 Analytical tools for Six-Sigma and continuous improvement
The analytical tools of Six-Sigma have been used for many years in traditional
quality improvement programs. What makes their application to Six-Sigma unique is
the integration of these tools in a corporate wide management system. The tools
common to all quality efforts, including Six-Sigma, are flowcharts, run charts, Pareto
charts, histograms, check sheets, cause-and-effect diagrams, and control charts.
Examples of these, along with an opportunity flow diagram, are shown in Figure 2.1
to Figure 2.5 arranged according to DMAIC categories where they commonly
appear.
1. Flow charts. There are many types of flow charts. The one shown in
figure 2.1 depicts the process steps as part of a SIPOC (supplier, input,
process, output and customer) analysis. SIPOC in essence is a formalized
input-output model, used in the define stage of a project.
Figure 2.1: An example of SIPOC analysis diagram for Define stage
(Hagemeyer,C. and Gershenson, J.K. (2005))
2. Run charts shown in the Figure 2.2A. They depict trends in data over
time, and thereby help to understand the magnitude of a problem at the
define stage. Typically, they plot the median of a process.
3. Pareto charts shown in the Figure 2.2C. These charts help to break down a
problem into the relative contributions of its components. They are based
on the common empirical finding that a large percentage of problems are
due to a small percentage of causes. In the example, 80 percent of
customer complaints are due to late deliveries, which are 20 percent of the
causes listed.
4. Check sheets shown in Figure 2.2B. These are basic forms that help
standardize data collection. They are used to create histograms such as
shown on the Pareto chart.
5. Cause-and-effect diagrams. Also called fishbone diagrams, they show
hypothesized relationships between potential causes and the problem
under study. Once the C&E diagram is constructed, the analysis would
proceed to find out which of the potential causes were in fact contributing
to the problem. Example of cause and effect diagram shown in the Figure
2.3.
6. Opportunity Flow Diagram. This is used to separate value-added from
non-value added steps in a process. Example of opportunity flow diagram
shown in Figure 2.4.
7. Control charts shown in Figure 2.5. These are time-sequenced charts
showing plotted values of a statistic including a centerline average and
one or more control limits. It is used here to assure that changes
introduced are in statistical control.
Figure 2.2: Example of charts for Measurement stage (Hagemeyer,C. and
Gershenson, J.K. (2005))
Figure 2.3: An example of Cause and Effect diagram for analyze stage
(Hagemeyer,C. and Gershenson, J.K. (2005))
Figure 2.4: An example of Opportunity flow diagram for Improve stage
(Hagemeyer,C. and Gershenson, J.K. (2005))
Figure 2.5: An example of Control chart for Control stage (Hagemeyer,C. and
Gershenson, J.K. (2005))
2.7 Six-Sigma versus Total Quality Management (TQM)
In some aspects of quality improvement, TQM and Six-Sigma share the same
philosophy of how to assist organizations to accomplish Total Quality. They both
emphasize the importance of top-management support and leadership. Both
approaches make it clear that continuous quality improvement is critical to long-term
business success. However, why has the popularity of TQM waned while Six
Sigma's popularity continues to grow in the past decade?
Pyzdek (2001) stated that the primary difference is management. Unlike TQM, Six-
Sigma was not developed by technicians who only dabbled in management and
therefore produced only broad guidelines for management to follow. The Six-Sigma
way of implementation was created by some of America's most gifted CEOs people
like Motorola's Bob Galvin, Allied Signal's Larry Bossidy, and GE's Jack Welch.
These people had a single goal in mind: making their businesses as successful as
possible. Once they were convinced that tools and techniques of Six-Sigma could
help them do this; they developed a framework to make it happen. The differences
between TQM and Six-Sigma are summarized in Table 2.1
2.8 Six-Sigma versus Other Quality system or tools
2.8.1 ISO 9001 objectives
ISO 9001 is a Quality Management System, which includes specialized quality
management standards. A Quality Management System is a system of clearly defined
organizational structures, processes, responsibilities and resources used to assure
minimum standards of quality and can be used to evaluate an organizations overall
quality management efforts. An ISO 9001 certification assures a company’s
customers that minimum acceptable system and procedures are in place in the
company to guarantee that minimum quality standards can be met.
2.8.1.1 Comparison of ISO 9001 with Six Sigma
ISO 9001 and Six-Sigma serve two different purposes. ISO 9001 is a quality
management system while Six-Sigma is a strategy and methodology for business
performance improvement. ISO 9001, with guidelines for problem solving and
decision making, requires a continuous improvement process in place but does not
indicate what the process should look like while Six Sigma can provide the needed
improvement process. Meanwhile, Six-Sigma does not provide a template for
evaluating an organization’s overall quality management efforts whereas ISO 9001
does.
2.8.1.2 Combining Six Sigma with ISO
Six-Sigma provides a methodology for delivering certain objectives set by ISO such
as:
prevention of defects at all stages from design through servicing
statistical techniques required for establishing, controlling and verifying
process capability and product characterization
investigation of the cause of defects relating to product, process and quality
system
Continuous improvement of the quality of products and services.
Six-Sigma supports ISO and helps an organization satisfying the ISO requirements.
Further, ISO is an excellent vehicle for documenting and maintaining the process
management system involving Six Sigma. Besides, extensive training is required by
both systems for successful deployment.
2.8.2 Lean Manufacturing Objectives
Lean manufacturing, also called Lean Production, is a set of tools and methodologies
that aims for the continuous elimination of all waste in the production process. The
main benefits of this are lower production costs; increased output and shorter
production lead times. More specifically, some of the goals include:
1. Defects and wastage - Reduce defects and unnecessary physical wastage,
including excess use of raw material inputs, preventable defects, costs
associated with reprocessing defective items and unnecessary product
characteristics, which are not required by customers.
2. Cycle Times - Reduce manufacturing lead times and production cycle times
by reducing waiting times between processing stages, as well as process
preparation times and product/model conversion times.
3. Inventory levels - Minimize inventory levels at all stages of production,
particularly works-in-progress between production stages. Lower inventories
also mean lower working capital requirements.
4. Labor productivity - Improve labor productivity, both by reducing the idle
time of workers and ensuring that when workers are working, they are using
their effort as productively as possible (including not doing unnecessary tasks
or unnecessary motions).
5. Utilization of equipment and space - Use equipment and manufacturing space
more efficiently by eliminating bottlenecks and maximizing the rate of
production though existing equipment, while minimizing machine downtime.
6. Flexibility - Have the ability to produce a more flexible range of products
with minimum changeover costs and changeover time.
7. Output – Insofar as reduced cycle times, increased labor productivity and
elimination of bottlenecks and machine downtime can be achieved,
companies can generally significantly increased output from their existing
facilities. Most of these benefits lead to lower unit production costs – for
example, more effective use of equipment and space leads to lower
depreciation costs per unit produced, more effective use of labor results in
lower labor costs per unit produced and lower defects lead to lower cost of
goods sold.
2.8.2.1 Comparison with Six Sigma
Both Six Sigma and Lean Manufacturing have unique strengths and they integrate
well. Lean is broader in nature since it sets the broad objective of eliminating all
waste, and recommends certain processes for achieving that. When the objective is
process design, factory layout, waste reduction and the way to accomplish the
objectives is known, Lean tools and approaches are recommended. Six-Sigma is
more focused in nature since it a set of tools for achieving clearly defined
improvements, which are likely to help make the company leaner. Six-Sigma
provides a richer infrastructure and tool set for problem solving especially with
unknown causes and solutions.
2.8.2.2 Combining Lean Manufacturing with Six Sigma
It is quite common for companies to combine Lean Manufacturing with Six Sigma in
what is sometimes called Lean Six Sigma. The two are quite complementary since
Six Sigma is a useful tool for helping to make the company more lean. Likewise,
some of the processes often used in lean manufacturing may be the solutions to
problems addressed in a Six-Sigma project (Bendell, 2006)
Table 2.1: TQM VS Six-Sigma
2.8.4 Comparison between six sigma DMAIC and PDCA
Table 2.2:- Comparison between DMAIC and PDCA (Summers, D.C.S. (2003))
DMAIC PDCA
Steps SIX SIGMA:-
1. Select appropriate metrics:- Key Process Output
Variables (KPOVs)
2. Determine how these metrics will track over time
3. Determine current baseline performance of
project/process
4. determine the Key Process Input Variables
(KPIVs) that drives the Key Process Output
Variables (KPOVs)
5. Determine what changes used to be made to the
key process input variables in order to positively
affects the key process output variables
6. Makes changes
7. Determine if the changes have positively
affected the KVOPs
8. If the changes made result in performance
improvements establish control of the KVIPs and
the need new levels. If the changes have not
resulted in performance improvement return to
step 5 and make the appropriate changes.
Plan:-
1. Recognize a problem
exist
2. Foam quality
improvement team
3. Clearly define the
problems
4. Develop performance
measures
5. Analyze
problem/process
6. Determine possible
causes
Do:-
1. Select and implement
solution
Study:-
1. Evaluate solution
Act:-
1. Ensure permanence
2. Continuous
improvement
2.9 Case study: An example of DMAIC at American Express
In this case the customer is using Six-Sigma to reduce defects in a service.
2.9.1 The general situation
A number of merchants that accept American Express cards fail to place point of
purchase materials (e.g., decals) that notify customers that they can use these cards at
these establishments while displaying competing (e.g., Visa, MasterCard, etc.) point
of purchase materials. American Express defines these merchants as “passive
suppressors.”
In an effort to increase visibility, an external vendor that placed point-of-purchase
material in the marketplace identified passive suppressors, and measured placement
and passive suppression rates was hired by American Express. However, the vendor
had a significant rate of failure to contact or meet with the merchants. The leading
reason for not meeting with the merchant was that the store was closed when the
vendor stopped by.
2.9.3 Define and Measure
The objective was to reduce closed store uncallables (failures to contact), which
represented 27.4 percent of total uncallables and 8.0 percent of the annualized
attempted visits. The process represented a 2.9-sigma level and 80,000 defects per
million opportunities.
2.9.3 Analyze
A Pareto chart pointed to the “closed store” category as the number one reason for
uncallables. By shadowing the vendor on merchant visits, American Express learned
that the visits took place between 8:00 A.M. and 6:00 P.M. Of the closed stores, 45
percent were retail establishments and 16 percent were restaurants. Typically, these
two types of establishments are not open before 10:00 A.M. therefore; American
Express hypothesized that the hours the vendor was calling on the merchant
contributed to the high uncallable rate. It also was determined that if an
establishment was closed, the inspection process was terminated with the merchant
being reported as uncallable without first checking to see if any point-of-purchase
materials were visible from the outside. This resulted in merchants who displayed
point-of-purchase material being visited multiple times leading to rework.
2.9.4 Improve
American Express then tested and validated their hypotheses. The call hours for all
visits were changed to begin after 10:00 A.M. The vendor was required to continue
the inspection process with respect to external placement of point-of-purchase
material. The first change, revised calls hours, resulted in a decrease to 4.5 percent
from 8.0 percent in the defect rate. The second change, continued inspection,
indicated that 35.4 percent of the remaining 4.5 percent closed stores actually had
external point-of-purchase material displayed. Combined, these two changes had the
following effects: the defect rate decreased to 2.8 percent, the number of defects per
million decreased to 28,000, and the sigma level increased to 3.2.
2.9.6 Control
In order to achieve control, American Express uses a p control chart to track the
proportion of closed stores over time and the vendor call report was revised to reflect
the uncallable rate by reason (Sai Kim, 2000).
2.10 Summary
As a conclusion for this chapter, it gives a brief explanation regarding the quality
approaches. In order to achieve excellent quality, the challenge is to make certain
that a quality program really does have a customer focus and is sufficiently agile to
be able to make improvements quickly without losing sight of the real time needs of
the business. The quality system must be analyzed for its own quality. There is also a
need for sustaining a quality culture over the long haul.
CHAPTER 3
RESEARCH METHODOLOGY
3.1 Introduction
This chapter will discuss briefly about the selected company and the area focused for
this thesis. Besides that, this chapter also discussed the research methodology which
were used in this research and also the Gantt chart which attached to indicate the
planning process for this research.
3.2 Company selection
This thesis, which is actually a case study, were conducted in XXX a Japanese
company. This case study was focused on 18 and 30 tone injection molding assembly
line of part production department of the company which is located at the Nilai,
Negeri Sembilan, Malaysia plant. The department’s main production is remote
control and small plastic components which produced for internal and external
customers. Further information of the company will discuss in chapter 4, company
profile.
3.3 Methodology
The methodology adapted for this case study is by applying the six sigma project
methodology which are Define, Measure, Analyze, Improve and Control, (DMAIC)
methodology. This methodology is done in two semesters, first semester starts with
selecting a company to do the case study until first stage of DMAIC methodology
(PSM 1) and second semester starts with the measure stage and culminates with final
stage of DMAIC methodology (PSM 2). The methodology of this research is shown
in Figure 3.1.
3.3.1 Define stage
The purpose of the Define phase is to clearly identify the problem, the requirements
of the project and the objectives of the project. In this stage rejection data will be
collected and tabulated to foam a pareto diagram to identify the main rejection
problem. A team will be formed to brainstorm and produce a fish bone diagram to
identify the major contribution for the main rejection problem.
3.3.2 Measure stage
Depending on whether it is a new or old process investigated during the project, a
method and what kind of response (Y) to measure the performance of the process has
to be developed (new). If it is an existing process, evaluate the accuracy and
variation of the measurement system and also determine the current process
performance. Most importantly identify the input variables that cause variation in the
process performance. Benham (2003), Montgomery (2001b) and GE-DMAIC all
claims that to ensure that the measurement system is adequate to measure the Y, a
gauge R&R study must be done. The project team should then gain consensus on any
actions needed regarding the measurement system. It is important to build up an
understanding of the measurement system, which includes operators, gauges and
environment.
Fishbone Diagram – to demonstrate the relationships between inputs and
outputs
Process Mapping - to understand the current processes and enables the
team to define the hidden causes of waste.
3.3.3 Analyze stage
In all quality work it is not enough to define the problem collect and display the
suitable data. The data also needs to be analyzed so the sources of variation are
identified. Mostly there is more than one cause of variation, but there is usually a
root cause more important than the other causes, this root cause has to be identified
and eliminated later on.
Cause & Effect Matrix - to quantify how significant each input is for
causing variation of outputs.
Why and why analysis to identify the problems
3.3.4 Improve stage
The root cause that was found during the measure phase must now be eliminated; the
process is also to be optimized. A number of solutions to solve the problem will be
suggested with the root cause are generated and the one that best addressees the root
cause is chosen. To optimize the process a designed experiment is usually conducted,
the input variables are set to achieve the optimum output.
3.3.5 Control stage
The final phase of the DMAIC roadmap is control. In this stage the control method
will be suggested but in actual after implementing the improvements, evidence is
needed to prove that the process is in control and is more capable than before the
improvements. If the process is more capable than before it is important to keep and
maintain this new higher quality level. Statistical process control and especially
control charts mainly do this.
3.4 Techniques used in identifying the general and major problem
Apart from six sigma DMAIC methodology, other techniques needed during the
study are:-
3.4.1 Interview
In identifying the problems interviews are conducted with the Quality Assurance
engineer in the assembly operation department. The questions were focused mainly
in the category of man, machine, material, method and also company policy.
3.4.3 Observation
Through observation, the specific problem of the department can be understood.
Normally the observation period is within one to two hours per day. The process
flow in the assembly line was also observed.
3.4.3 Data Collection
Data was collected from the assembly line to find out where the problem lies. Data
such as the actual time of each activity was carried out, number of rejected items,
targeted value of each day and actual quantity production was taken. Thus the
problem based on the data can be identified and then analyzed to determine where
the actual problem lies. Statistical Process Control (SPC) tools were used to analyze
the quality problem.
3.5 Gantt chart Table 3.1: Gantt chart
2007 2008 No Activity
Jul Aug Sept Oct Nov Dec Jan Feb Mac Apr
1
Selecting company and Problem
statement identification
2 Research Scope and Objective
3 Findings Literature Review
4 Research methodology
5 Start doing the report writing
6
Presentation Preparation for PSM
1 (Proposal)
7 Report Review
8 PSM 1 report Submission
9 Presentation for PSM 1
10 Data collection Measure stage
11 Data Collection and analysis stage
12
Prepare suggestion for
improvement and control
13 Result analysis and discussion
14
Report writing for PSM 2 (Final
report)
15 PSM 2 Presentation
16 Hard Cover Submission
Actual Planning
42
44
3.6 Summary
The methodology of the thesis is identified. This methodology will be used as
guidance throughout this project. This is so that a more systematic methodology will
be done.
48
4.4 Company’s Product
Figure4.3: Remote controllers
AAuuddiioo VViiddeeoo ttyyppee ((TTVV,, DDVVDD ,,VVCCRR))
AAiirr CCoonnddiittiioonneerr ttyyppee
CCaammeerraa ttyyppee
TTooiilleettrryy ttyyppee
AAuuttoommoottiivvee ttyyppee
49
4.5 Introduction to Injection Moulding
Injection moulding is a manufacturing technique for making parts from thermoplastic
material in production. Molten plastic is injected at high pressure into a mould,
which is the inverse of the product's shape. After a product is designed by an
Industrial Designer or an Engineer, moulds are made by a mould maker (or
toolmaker) from metal, usually either steel or aluminum, and precision-machined to
form the features of the desired part. Injection moulding is widely used for
manufacturing a variety of parts, from the smallest component to entire body panels
of cars. Injection moulding is the most common method of production.
Injection moulding machines, also known as presses, hold the moulds in which the
components are shaped. Presses are rated by tonnage, which expresses the amount of
clamping force that the machine can generate. This pressure keeps the mould closed
during the injection process. Tonnage can vary from less than 5 tons to 6000 tons,
with the higher figures used in comparatively few manufacturing operations.
Injection moulding machines can fasten the moulds in either a horizontal or vertical
position. The majority is horizontally oriented but vertical machines are used in some
niche applications such as insert moulding, allowing the machine to take advantage
of gravity. There are many ways to fasten the tools to the platens, the most common
being manual clamps (both halves are bolted to the platens); however hydraulic
clamps (chocks are used to hold the hold the tool in place) and magnetic clamps are
also used. The magnetic and hydraulic clamps are used where fast tool changes are
required.
Machines are classified primarily by the type of driving systems they use: hydraulic,
electric, or hybrid. Hydraulic presses have historically been the only option available
to moulders until Nissei introduced the first all electric machine in 1983. The electric
press, also known as Electric Machine Technology (EMT), reduces operation costs
by cutting energy consumption and also addresses some of the environmental
concerns surrounding the hydraulic press. Electric presses have been shown to be
quieter, faster, and have a higher accuracy; however the machines are more
50
expensive. Hybrid injection moulding machines take advantage of the best features
of both hydraulic and electric systems. Hydraulic machines are the predominant type
in most of the world, with the exception of Japan.
Robotic arms are often used to remove the moulded components; either by side or
top entry, but it is more common for parts to drop out of the mould, through a chute
and into a container.
Figure 4.4: Sumitomo Injection Moulding Machine
4.5.1 Injection Molding Cycle & Process
The basic injection cycle is as follows: Mould closes - injection carriage forward -
inject plastic - metering - carriage retract - mould open - eject part(s). The moulds are
closed shut by hydraulics or electric, and the heated plastic is forced by the pressure
of the injection screw to take the shape of the mould. Some machines are run by
electric motors instead of hydraulics or a combination of both. The water-cooling
51
channels then assist in cooling the mould and the heated plastic solidifies into the
part. Improper cooling can result in distorted moulding or one that is burnt. The cycle
is completed when the mould opens and the part is ejected with the assistance of
ejector pins within the mould.
The resin, or raw material for injection moulding, is usually in pellet or granule form,
and is melted by heat and shearing forces shortly before being injected into the
mould. Resin pellets are poured into the feed hopper, a large open bottomed
container, which feeds the granules down to the screw. The screw is rotated by a
motor, feeding pellets up the screw's grooves. The depth of the screw flights
decreases towards the end of the screw nearest the mould, compressing the heated
plastic. As the screw rotates, the pellets are moved forward in the screw and they
undergo extreme pressure and friction which generates most of the heat needed to
melt the pellets. Heaters on either side of the screw assist in the heating and
temperature control during the melting process.
The channels through which the plastic flows toward the chamber will also solidify,
forming an attached frame. This frame is composed of the sprue, which is the main
channel from the reservoir of molten resin, parallel with the direction of draw, and
runners, which are perpendicular to the direction of draw, and are used to convey
molten resin to the gate(s), or point(s) of injection. The sprue and runner system can
be cut or twisted off and recycled, sometimes being granulated next to the mould
machine. Some moulds are designed so that the part is automatically stripped through
action of the mould.
52
Table 4.1: Injection moulding cycle
Step #1 - The uncured rubber is fed into the
machine in the form of a continuous strip.
Step #2 - The uncured rubber is worked and warmed
by an auger screw in a temperature controlled barrel.
Step #3 - As the rubber stock accumulates in the
front of the screw, the screw is forced backwards.
When the screw has moved back a specified
amount, the machine is ready to make a shot.
Step #5 - While the rubber cures in the heated mold,
the screw turns again to refill.
Step #4 - With the mold held closed under hydraulic
pressure, the screw is pushed forward. This forces the
rubber into the mold, similar to the action of a
hypodermic syringe.
Step #6 - The mold opens and the part can be
removed. The machine is ready to make the next shot,
as soon as the mold closes.
53
4.5.2 Moulding Defects
Table 4.2: Common moulding defects
Moulding Defects Alternative name Descriptions Causes
Blister Blistering /
Peeling
Raised or layered zone
on surface of the part
Tool or material is too hot, often caused by a lack of
cooling around the tool or a faulty heater
Burn Marks Air Burn Localised burnt zone
(often in the
yellow/brown tones)
Tool lacks venting, injection speed is too high
Black dot Dirt like spots on
material
Material over heat, dust, mix up of material and etc
Crack Part broken External forces and etc
Color Streaks Localised change of
color
Masterbatch isn't mixing properly, or the material has
run out and it's starting to come through as natural only
Delamination Thin mica like layers
formed in part wall
Contamination of the material e.g. PP mixed with ABS,
very dangerous if the part is being used for a safety
critical application as the material has very little
strength when delaminated as the materials cannot bond
Flash Excess material in thin
layer exceeding normal
part geometry
Tool damage, too much injection speed/material
injected
Embedded
contaminates
Embedded
Particulates
Foreign particle (burnt
material or other)
embedded in the part
Particles on the tool surface, contaminated material or
foreign debris in the barrel, or too much shear heat
burning the material prior to injection
Flow marks Directionally "off tone" Injection speeds too slow (the plastic has cooled down
54
wavy lines or patterns too much during injection, injection speeds must be set
as fast as you can get away with at all times)
Jetting Deformed part by
turbulent flow of
material
Poor tool design
Silver streaks Circular pattern around
gate caused by hot gas
Sink Marks Localised depression (In
thicker zones)
Holding time/pressure too low, cooling time too low,
with sprueless hot runners this can also be caused by
the gate temperature being set too high
(Source: XXX Part Production, Injection Moulding Manual)
Splay Marks Splash mark /
Silver Streaks
Circular pattern around
gate caused by hot gas
Splay Marks
Short shot Fill / Short
mould
Partial part Lack of material, injection speeds too slow
Stringiness String like remain from
previous shot transfer in
new shot
Gate hasn't frozen off
Weld line Knit Line Discolored line where
two flow fronts meet
Mould/material temperatures set too low (the material
is cold when they meet, so they don't bond)
Voids Empty space within part
(Air pocket)
Lack of holding pressure (holding pressure is used to
pack out the part during the holding time)
Warping Twisting Distorted part Cooling is too short, material is too hot, lack of cooling
around the tool, incorrect water temperatures (the parts
bow inwards towards the cool side of the tool)
CHAPTER 5
RESULT AND DISCUSSION
5.1 Introductions
The methodology adapted for this study is by applying the General Electric (GE)
approach. They are the Define, Measure, Analyze, Improve and Control (DMAIC)
methodology.
The application of Six-Sigma methodology is a statistical analysis approach to
quality management. In this chapter the rejection ratio of 30 tone injection moulding
process department was analyzed statistically using DMAIC methodology and
suggestions for quality improvement will be made to the department.
5.2 DMAIC – Define stage
5.2.1 Define the process
Before the process can be investigated, all circumstances have to be defined. Such
circumstances are often described as SIPOC (Suppliers, Inputs, Process, Outputs and
Customers). The circumstances around the moulding of cover are listed in
chronological order below.
Suppliers -Material supplier, DuPont
Inputs -Material, ABS
Process -Receive ABS and load into hopper
-Dry ABS
-Feed ABS into moulding machine
-Mould cover
-Deliver cover to assembly stations
Outputs -Cover
Customers -assembly stations / external customers
The particular moulding processes of the cover are described in figure 5.1. It
shows the process flow to produce a cover.
5.2.2 Molding Process Flow Chart
Figure 5.1: The Cover molding process
5.2.3 Identify the current reject problem
Table 5.1: In- line rejection based on part produced
Model In-line
reject unit
In-line
(k unit) Percentage Acc.
BMQ-case A 4567 4.57 30.61 30.61
BMQ-Case B 2067 2.07 13.86 44.47
BNX-Spacer 1789 1.79 11.99 56.46
BPJ-Case B 741 0.74 4.97 61.43
BPJ-Case C 675 0.68 4.53 65.96
BPJ-Case A 477 0.48 3.20 69.16
BPY-Spacer 461 0.46 3.09 72.25
BNM-Case B 403 0.40 2.70 74.95
BNT-Case B 372 0.37 2.49 77.44
Bezel 353 0.35 2.37 79.81
BLX-Case C 353 0.35 2.37 82.18
BPZ-Case A 350 0.35 2.35 84.53
BBM-Case B 349 0.35 2.34 86.86
BNM-Case C 184 0.18 1.23 88.10
BLP-Case C 167 0.17 1.12 89.22
BNM-Case A 144 0.14 0.97 90.19
BBM-Case A 102 0.10 0.68 90.87
BNC-Case B 100 0.10 0.67 91.54
BPZ-Case D 77 0.08 0.52 92.05
STEM 51 0.05 0.34 92.40
BPZ-Case C 34 0.03 0.22 92.62
BPZ-case B 16 0.02 0.11 92.73
others 1085 1.08 7.27 100.00
TOTAL 14.92
Table 5.1 shows the rejection data for 30 tone injection moulding assembly line for
the month of April 2007. This data shows the highest rejection ratio compared to the
pervious months rejection data (pervious rejection data shown in Appendix A).
Figure 5.2 shows the Pareto diagram for the particular part rejects based on the code
name. The result shows that, part named BMQ case A have the highest rejection rate
for the month which is 4567 units and contributes 30.61 % of the total rejection rate.
Since the part has the highest rejection rate it has been taken as the studying element
for the research.
In-line Rejection
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
(kunit)
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
(%)
In-line (kunit) Acc.
Figure 5.2: In- line rejection
5.3 DMAIC- Measure stage
Data was collected for 6 months continuously from May to October 2007 (Appendix
B) for output line reject that occurred in the 30 tone injection moulding assembly line
that focused on the production of part named BMQ case A to track down the problem
encountered by this particular part. Since there are four machines producing the same
part, the reject data were collected for each machine.
These data were used to calculate defect per million opportunities (DPMO) for each
month. Table 5.2 shows the total output, reject quantity, DPMO and sigma level for
each month from May to October 2007.
Table 5.2: Total output and Sigma level
Machine (reject quantity)
Month Output E01 E03 E07 E10 Total Reject
per/mthDMPO SIGMA
May 299520 120 1870 1819 810 4619 3084.3 4.2420
June 299520 130 1828 1789 783 4530 3024.8 4.2485
July 299520 117 1893 1756 796 4562 3046.2 4.2461
Aug 299520 105 1875 1815 815 4610 3078.3 4.2426
Sept 299520 120 1890 1821 765 4596 3068.9 4.2437
Oct 299520 132 1810 1797 789 4528 3023.5 4.2486
TOTAL 1797120 724 11166 10797 4758 27445
A bar graph was constructed as in Figure 5.3, for each month based on reject
quantity. Figure 5.3 shows that the highest rejection rate was identified in the month
May 2007 meanwhile for other moths the data collected shows small variations.
In-line Reject from May to October 2007
4480
4500
4520
4540
4560
4580
4600
4620
4640
month
0 .0 0
10 .0 0
2 0 .0 0
3 0 .0 0
4 0 .0 0
50 .0 0
6 0 .0 0
70 .0 0
8 0 .0 0
9 0 .0 0
10 0 .0 0
Figure 5.3: In-line reject from month May to October 2007
Based on the data in table 5.2, the sigma level for the process were calculated and
illustrated as in figure 5.4. Calculation for the sigma level attached in Appendix C.
The figure 5.4 explains that the sigma level from the month May to October ranging
from 4.2420 to 4.2686. This shows the average sigma level for the whole process is
4.2452. The lowest sigma level was recorded for the month May and the highest
sigma level was recorded on the month October. Since the sigma level for month
May has the lowest sigma level, the studies or research will be focused on the month
May. This data will used to track down the problem that contributes to highest reject
on the part.
SIGMA LEVEL
4.2420
4.2485
4.2461
4.24264.2437
4.2486
4.2380
4.2400
4.2420
4.2440
4.2460
4.2480
4.2500
may june july aug sept oct
MONTH
SIG
MA
LE
VE
L
Figure 5.4: Sigma level from month May to October 2007
5.4 DMAIC- Analyze stage
Table 5.3 shows the defect type’s data for the month May 2007 and Figure 5.5
illustrate the Pareto diagram for this particular data. As mentioned before, there are
four machines which produce the same part which known as BMQ case A and the
data for defects were collected based on machines. This is to identify the machine
which contributes to the highest rejection rate. The defects which are recorded in
Table 5.3, are the comment types of defects which normally occurs on plastic parts
which produced by using injection moulding process. Figure 5.5 explains that black
dot defects are the major contributor for the rejection rate for the month May which
contributes almost 41% of the total rejects compared to other defects. If defect data
compared by machine, still black dot contributes the highest defects compared to
others and for the machines, machine E03 contributes to highest black dot defect
compared to other machines. As a measure to track down the problem machine E03
will be used to analyze the root cause for the black dot defects since it shows the
highest rejection rate and the analyze data will be used as references for other
machines.
Table 5.3:- Reject data based on the defect type for month May 2007
BMQ-CASE A
Machine No
Defect E01 E03 E07 E10 Sub Total Percentage Acc.
Black dot 77 694 545 536 1852 40.10 40.10
Scratches 4 608 490 188 1290 27.93 68.02
Oily/Dirty 0 320 330 43 693 15.00 83.03
Short Mold 0 0 235 0 235 5.09 88.11
Part drop 28 86 100 17 231 5.00 93.12
White Mark 4 128 20 5 157 3.40 96.51
Dented 4 17 84 15 120 2.60 99.11
Silver mark 2 17 10 0 29 0.63 99.74
Burr 0 0 0 6 6 0.13 99.87
Sink Mark 0 0 5 0 5 0.11 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
total 4619
In-line Reject category for BMQ-Case A
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 .0 0
10 .0 0
2 0 .0 0
3 0 .0 0
4 0 .0 0
50 .0 0
6 0 .0 0
70 .0 0
8 0 .0 0
9 0 .0 0
10 0 .0 0
Acc sub total
Figure 5.5: Reject data based on the defect type for month May 2007
5.4.1 Potential causes for high defects occurred in part BMQ case A
Analyzing the rejects based on models indicates that the highest percentage of
defects occurred in model BMQ case A. Figure 5.6 shows the potential causes for
high defects.
The number of defects is high when there are new models being introduced. It may
be due to the operators not given enough training or no special training for the
operator to understand the correct method to produce the part.
Besides that, the high defects might contributed by the machines. The machines
might operate by new technicians that lack of training or experience. This will lead to
misjudging in solving the problem during the machining process.
Stressful environment also can lead to high rejects. It’s a human nature, where when
workers find that the working environment stressful, this will lead to dissatisfaction
in working condition and at the same time it also leads to high defects.
Besides that the method or standard operation principles also can lead to high
defects. Methods or SOP for the particular process might be varying from the actual
SOP for the process and this will contributes to wrong machine setting or operation
parameters.
Figure 5.6: Potential causes for high defects
5.4.2 Root causes analysis
In order to determine the exact and most likely causes of major defects, a
brainstorming section was carried out with the Quality Assurance Engineer in
assembly operation. Through the brainstorming section, all possible causes including
major and minor causes were listed in the cause and effect diagram. The following
section will discuss on the root causes for black dot defects.
5.4.2.1 Root causes analysis for Black dot defect
The wrong part defect is caused by five major factors, which are machine,
environment, man (operator), method and the material. Figure 5.7 shows the cause
and effect diagram for the black dot defect.
HIGH DEFECTS
Lack of training
No special training
Stressful work environment
Poor job satisfaction
Do not understand the procedure
New model
Machine operation and condition
New work
Machines are one of the factors that must be given consideration. The machine
contribute allot of possibilities to black dot rejection defect. Examples, without
proper parameter setting it will result to a carbonized screw. Aging machines also
can lead to defects. Maintenance also plays and important part because, without
maintenance the performance of machine will be affected and the desired output
could not been gained.
When an operator does not have enough experience and practice, it is quite obvious
that the operator produces more defects than the others. Defects might occur when
jobs carried out without guidance of leader or without any instruction. Besides that,
number of defect will increase when untrained operator or new operators are
assigned to do the job.
The work method is another major cause of the problem. It was found that the
operator did not know the correct method set the machine and the parameters but
only followed the instructions without knowing the correct method. As a result the
operator can lead to black dot defect or other rejection.
Working environment was another cause for the defect. It is based on company
policy where, there are two shift with 12-hours working period each in the assembly
department. This can cause the operator to loose concentration, become tired and
bored doing the job. As a result the organizations will hire new operator who do not
have any knowledge or experience in the assembly line.
Besides that, a material as an important medium in injection molding process also
contributes to some major defects. Examples, when material are contaminated with
other foreign particles it will effects the properties of the part and at the same time it
lead to major defects.
Figure 5.7: Root causes analysis for Black dot
5.4.3 Summary on the analysis
As the conclusion for the analysis stage, the major defect found were black dot and
several problems were identified as the main problems causes high defects in 30 tone
injection moulding line. The main problem identified from the analyze section is the
machine this due to the data which colleted indicates that the major problem for each
machine is the black dot. This shows that the major defect might cause by the
machine. Although there are other factors affecting the reject problems, the main
consideration has given to the machine factor. The next section will discuss about
suggestion for improvement.
5.5 DMAIC- Improve stage
5.5.1 Introduction
After collecting and analyze the data, the identified defect was the black dot defect
which caused major quality problem in the 30 tone injection moulding line. Cause
and effect diagram was also drawn to identify the causes of major defects. From here
three suggestions was recommended to reduce the defects. The suggestions were:
1. Screw and barrel cleaning
2. PP and special material for cleaning screw and barrel by purging
5.5.3 Screw and barrel cleaning
5.5.2.1 Screw cleaning
Figure 5.8: Injection screw before cleaning
Figure 5.8 shows the injection screw before cleaning which was used to mold the
BMQ Case A. The injecting screw in the figure indicates that it has carbonized. After
a request as a suggestion to the engineering group to clean the screw the results
gained as shown in Figure 5.9. Sand paper and some chemical solvents were used to
clean the screw. Most of the dirt was identified from the material which was
carbonized because of over heated in the barrel. The over heated material will stick
on the screw and will released slowly each time injection and caused for the black
dot on the surface of the BMQ Case A.
Figure 5.9: Injection screw after cleaning
5.5.2.2 Barrel cleaning
Figure 5.10 shows the condition of machine which was not cleaned properly where a
lot of scrap material surround the tie bar and hydraulic unit area. This condition will
lead to a situation where the foreign materials or scrap material will mixed original
material and at the same time leads to black dot and other defects.
Figure 5.10: Barrel before cleaning
Figure 5.11 shows the figure of machine barrel which has cleaned and maintained by
the operator.
Figure 5.11: Barrel after cleaning
After carry out the cleaning activity on the machine, the machine was covered with a
plastic to make sure no dirt or dust affects the machine condition. Figure 5.12 shows
the machine which has covered with plastic.
Figure 5.12: Machine covered with plastic
Figure 5.13 shows a run chart that represent the Black Dot trend before and after
screw cleaning process for machine E03. Based on the figure 5.13, the trend before
cleaning shows that the defects per day from 7th
October to 23rd
October is higher
than the trend after cleaning where the cleaning process perform on 24th
of October.
This clearly shows that the machine factor plays an important role and it needs to
maintain for time of period in order to eliminate or reduce the black dot problem.
The chart itself concludes that one of the main causes for the black dot is the
machine condition. This results will be used by the production in charge member to
perform continues action and at the same time improve the sigma level for the
process.
Figure 5.13: Black Dot trend before and after screw cleaning for machine E03
From the analysis done for this project, a conclusion can be made that machine
condition is the major contributor for the black dot problem. Since the engineering
group member can not clean the injection screw or the barrel every day, a new
cleaning material agent was proposed or suggested to solve this problem. Some
testing was done to show that this cleaning agent is better than the previous. Cost
calculation also shown in next sub topic.
5.5.4 PP and special material for cleaning screw and barrel by purging
5.4.4.1Characteristic of cleaning agents
MINIMIZE DOWNTIME & REDUCE SCRAP
- Special Material is clean on the first pass, minimizing machine
downtime to maximize the productivity. This also reduces scrap so do
not waste resin.
EASY TO USE
- Special material ready to use and no soaking, mixing or waiting
necessary, so there is no hidden cost.
ECONOMICAL
Black Dot Trend before & after Screw cleaning process for M/C E03
0
50
100
150
200
250
300
350
400
7-Oct 8-Oct9-Oct10-Oct11-Oct12-Oct13-Oct14-Oct15-Oct16-Oct17-Oct18-Oct19-Oct20-Oct21-Oct22-Oct23-Oct24-Oct25-Oct26-Oct27-Oct28-Oct29-Oct30-Oct31-Oct1-Nov2-Nov3-Nov4-Nov5-Nov6-Nov7-Nov8-Nov9-Nov
Screw barrel cleaning on 24 OCT’07
- Only a small amount of material is needed to purge quickly and
effectively. It has unlimited shelf life.
SAFE TO USE
- Special material is non-chemical / no-hazardous and no-abrasive. It
does not cause wear on machines. It is safe for machines and
operators and safe for disposal.
5.5.3.2 Comparison between the cleaning agents
Figure 5.14 shows cleaning agent material 1 and Figure 5.15 show cleaning material
agent 2 in granular or resin foams. This two material will use continues to clean the
dirt in the barrel and screw. The method is, first nozzle temperature will set in high
and use agent 1 to purge and followed by agent 2. Agent 1 will clean the screw and
agent 2 will purge out the entire balance material of agent 1.
Figure 5.14: Cleaning Agent 1 (PP)
Figure 5.15: Cleaning Agent 2 (Special material)
Figure 5.16 shows the material for agent 1 which was purged out from the nozzle.
The characteristic of the material for agent 1 after melt, it gives a rubber type output.
Mean while in Figure 5.17 it shows the purging operation or process of agent 2. For
agent 2, the material was softer and can be used to clean the agent1 completely from
the nozzle and screw. This gives the screw a better cleaning compared to the
pervious cleaning process which uses only one type of cleaning agent. Table 5.4
shows the comparison of the Special agent material and PP material.
Figure 5.16: Purging process of Agent 1 (PP)
Figure 5.17: purging process for Agent 2 (special material)
Table 5.4:-Comparison with Special Cleaning material and Current use
Material (PP)
PP Material Special Material
1. Soft type, unable to purge out
all the dirt/ stain in barrel and
screw
2. When changing model
especially for parts using
material ABS and PS material
not effective. Reason is because
PP softer than PS and ABS
material.
3. Usage volume for this material
is very high.
4. Setting temperature for this
material is more than 270
Celsius so needs more waiting
time.
1. hard type and easily remove
dirt/stain in the barrel or screw
2. suitable to use during change
model either from ABS to PS or
other material
3. Usage volume for this material
is much more lower
4. Setting temperature for this
material almost same as the ABS
and PS material around 230
Celsius.
5.5.3.3 Result after implementation of both cleaning agents
Figure 5.18 illustrate the result for the reject output for BMQ case A before and after
using the special material as cleaning agent. Based on the run chart in Figure 5.18,
the rejects for the part was higher before the cleaning process was implemented
compared to result gained after implemented where the red line in the figure
represent the trend before cleaning and the blue dotted line represent the trend after
cleaning. The reject rate decreases after the implementation of the mentioned
cleaning process and at the same time it proves that the dirty elements or inclusions
in the barrel and screw were the main factor of the black dot.
Figure 5.18: Black dot trend before and after special material cleaning
Currently Part Production department production lines using both the agents as
cleaning material. Both cleaning agents are much more expensive compared to the
previous cleaning agent but after calculate and compared with the rejection cost it
still giving profit to the department. Cost calculation for the special cleaning material
shown in Table 5.5 to table 5.8.
Black Dot Trend After & Before Special Material cleaning-M/C E06
0
50
100
150
200
250
300
350
400
(pcs)
Special Material was
used
Table 5.5: Cost calculation for the material price and amount
ITEM PP MATERIAL SPECIAL MATERIAL COST
1.0 )
Material
Price &
Amount
(i) Price/Kg =RM 5.10
(i) Material that used to
cleaning screw &
Barrel.
(1.5Kg ~ 2.0Kg)
So that, 1.75 Kg X RM 5.10/Kg
=RM 8.925
(i) Agent 1: Price/Kg
=RM 29.00
Agent 2: Price/Kg
=RM8.50
(ii) a) If, 30 ton:-
Agent 1
0.2856 Kg X RM 29.00/Kg
= RM 8.28
Agent 2
0.1342Kg X RM 8.50/Kg
= RM 1.14
b) If, Above 30 ton :-
Agent 1
0.4760 Kg X RM 29.00/Kg
= RM 13.804
Agent 2
0.2684 Kg X RM 8.50/Kg
= RM 2.28
For 30 ton
Lost :
RM 0.495
/machine
For Above
30 ton
Lost :
RM 7.159
/machine
Table 5.6: Cost calculation for down time and labor cost
ITEM PP MATERIAL SPECIAL MATERIAL COST
2.0)
Down
Time
Time to be taken to manual
screw & barrel cleaning:– 10
hours
a) If, 30 ton:-
Machine rate =RM0.2024/min
(10 X 60)min X RM 0.2024/min
= RM 121.44
b) If, Above 30 ton:-
Machine rate = RM 0.2920/min
(10 X 60)min X RM 0.2920/min
= RM 175.20
Time to be taken to
screw & barrel
cleaning (Purging) :-
30 minutes
a) If, 30 ton:-
Machine rate = RM
0.2024/min
(30)min X RM 0.2024/min
= RM 6.072
b) If, Above 30 ton:-
Machine rate = RM
0.2920/min
(30)min X RM 0.2920/min
= RM 8.78
For 30 ton
Save :
RM115.368/machine
For Above 30 ton
Save :
RM 166.42 /machine
3.0)
Labor
cost
Manual screw & barrel
cleaning by Engineer.
Executive cost =RM2600/month
(RM2600)/(26days X 8hours) =
RM12.50/hour
For 10 hours; 10 hour X RM
12.50/hour
= RM 125.00
Purging with special
material by Technician.
Non-Exe cost = RM
1300/month
(RM1300)/(26days X 8 hours)
= RM 6.25/hour
For 30 min; 0.5 hour X RM
6.25/hour
= RM 3.125
Save :
RM121.875/machine
Table 5.7: Cost calculation for Scrap data
ITEM PP MATERIAL SPECIAL MATERIAL COST
4.0 )
Scrap
data
a) If, 30ton :-
Machine No.: E03
Model : BMQ (Case A)
Cost/ Unit : RM0.1019/pcs
Total scrap (pcs) = 1353
pcs/month
Ave scrap (pcs) = 50 pcs/day
Total scrap (RM) =1353
pcs/month X
RM0.1019/pcs
= RM 137.87/month
a) If, 30ton :-
Machine No.: E03
Model : BMQ (Case A)
Cost/ Unit : RM0.1019/pcs
Total scrap (pcs) = 392
pcs/month
Ave scrap (pcs) = 15 pcs/day
Total scrap (RM) =392
pcs/month X
RM0.1019/pcs
= RM 39.94/month
For 30 ton
Save :
RM 97.93
/machine
Table 5.8: Cost calculation for Scrap data
As a conclusion:-
Total cost calculation = (Material price & Amount) + (Downtime) + (Labor cost) +
(Scrap amount)
If 30 ton:-
Total save/machine = -RM0.495 + RM115.368 + RM121.875 + RM97.93
= RM334.678 /machine
If 30 ton and above:-
Total save /machine = -RM7.159 + RM166.42 + RM 121.875 + RM645.06
= RM926.20 /machine
ITEM PP MATERIAL SPECIAL MATERIAL COST
4.0 )
Scrap
data
b) If, a30ton :-
Machine No.: E03
Model : BMQ (Case A)
Cost/ Unit : RM0.3556/pcs
Total scrap (pcs) = 2595
pcs/month
Ave scrap (pcs) = 96 pcs/day
Total scrap (RM) =2595
pcs/month X
RM0.3356/pcs
= RM 922.78/month
b) If, Above 30ton :-
Machine No.: E03
Model : BMQ (Case A)
Cost/ Unit : RM0.3556/pcs
Total scrap (pcs) = 781
pcs/month
Ave scrap (pcs) = 29 pcs/day
Total scrap (RM) =781
pcs/month X
RM0.3356/pcs
= RM 277.72/month
For Above
30 ton
Save :
RM 645.06
/machine
5.5.5 Summary on improve stage
Based on the suggestion given, the rejection rate can be reduce and at the same time
the sigma level can be improve since the defect per unit will be much more smaller
than the pervious situation.
5.6 DMAIC- Control stage
Control stage is another important stage before completing DMAIC methodologies.
This chapter will describe the step taken to control. One of the comment types of
quality tool used is the control chart.
5.6.1 Control chart
Control charts is another popular statistical process control tools which will be used
in this stage because control chart can detect abnormal variation in the process.
Control chart will be used in this assembly operation is c-chart because c-chart can
monitors the number of defects per inspection unit and is based on the Poisson
model. Besides that c-chart also will monitor multiple types of quality in a product.
5.6.1.1 Suggested steps in constructing a c-chart
C-chart note occurrences of every defect found in the product and chart the number
of defects per product unit or sample. In general, the opportunities for
nonconformities or defect may be numerous, even though the chances of
nonconformity occurring in any location on a product are relatively small. The
Poisson model is appropriate in this case and serves as a basic for the c-chart. It is
important to note that the area of opportunity for defects to occur must be constant
from sample to sample when applying the c-chart.
1. Determine cbar.
cbar = 1/k c(i)
There are k inspection units and c(i) is the number of nonconformities in the ith
sample.
2. Since the mean and variance of the underlying Poisson distribution are equal,
2= cbar
Thus,
2= cbar
and the UCL and LCL are:
UCL = cbar + 3. cbar
LCL = cbar - 3. cbar
3. Plot the centerline cbar, the LCL and UCL, and the process measurements c(i).
4. Interpret the control chart.
Cbar : the populations mean number of defects per inspection unit
c(i): the number of defects observed in sample i
LCL, UCL: the lower, upper control limits for the c-chart
i: the samples index
Once the c-chart is set up using the computed control limits and centerline, plot the
c(i) values. Next, connect the points with a solid line and use the chat to monitor the
process. Here, c(i)is the observed number of defects on the (1) inspection unit.
5.7 Summary
As a conclusion for this chapter, the major defect in the 30 tone injection moulding
department were identified using the statistical process control tools and the cause
and effect diagram were used to identified the root cause for major defect. Based on
the analysis suggestion for improvement was proposed in the improvement stage.
Besides that, suggestion to control the quality level in the assembly department also
was proposed in the control stage.
CHAPTER 6
CONCLUSION AND SUGGESTION
6.1 Conclusion
The objectives of this study, to apply the Six-Sigma methodology in a manufacturing
company, were met. The suggestion for improvement was done on 30 tone injection
moulding production department. The quality problem in this department was
analyzed using Six-Sigma methodology.
The root cause for the black dot defect had been successfully determined. Corrective
action to overcome this quality problem has been suggested. The implementation of
the proposed corrective action needs commitment from the management of the
company. The findings from this project can be used as a guide to improve other
quality problems. It is hoped that the company can take up the suggestion given in
this study to be implemented.
6.3 Suggestion for further study
The quality improvement of the black dot defect for the 30 tone injection moulding
production line that had been initiated in this project should be continued to further
enhance its quality. The suggestions for further studies include improving on other
factors that contributes to defects such as the method, environmental, materials and
operator/ human.
Besides that with the suggestion for control using the control chart as discuss in
previous chapter, the company can implement the suggestion for monitors the
number of defects per inspection unit based on the Poisson model and monitor
multiple types of quality in a product.
REFERENCES
Bendell,T (2006). “A review and comparison of six sigma and the lean
organizations.” Emerald Group Publishing Limited. Vol. 18 No. 3. pp. 255-262.
Charles P.Q. (1998). “SPC Methods for Quality Improvement.” John Wiley & Sons,
Inc.
Costin, H. (1994). Readings in total quality management. pp. 11-12, 152-153, 321-
329.
Crosby, P. (1986). “Quality Improvement through Defect Prevention.” Philip Crosby
Associates, Inc., Winter Park, FL.
Deming, W.E. (1986). “Out of the Crisis”. MIT Center for Advanced Engineering
Studies.
Evans, J.R. and Lindsay, W.M. (2002).”The Management and Control of Quality.”
5th edition, Thomson Learning, Stamford, CT.
Evans, J.R. and Lindsay, W.M. (2005). “An Introduction to Six Sigma & Process
Improvement.” Thomson South-western Publishing Company, Cincinnati, OH.
Feigenbaum, A.V. (1983). “Total Quality Control.” 3rd
ed. McGraw-Hill. New York.
Hagemeyer, C. and Gershenson, J.K. “Classification and application of problem
solving quality tools.” The TQM Magazine. Vol. 18, No. 5,pp. 455-483.
Grant, E.L. and Leavenworth, R.S. (1988). “Statistical Quality Control.” 6th
edition.
McGraw-Hill.
Gilbert, J.D. (1992), “TQM flops – a chance to learn from the mistakes of others.”,
National Productivity Review, Autumn, pp. 491-9.
General Electric (n.d.), “What is six sigma? The roadmap to customer impact.”,
available at: [www.ge.com/sixsigma/keyelements.html]
Harry, M.J. and Schroeder, R. (2000). “Six Sigma: The BreakthroughManagement
Strategy Revolutionizing the World’s Top Corporations.” Doubleday, New York.
Henderson, Kim M., Evans and James, R. (2000). “Successful Implementation of
Six-Sigma: Benchmarking General Electric Company.” Benchmarking: An
International Journal; Volume No. 7.
Juran, J. (1989). “Juran on Leadership for Quality: An Executive Handbook.” Free
Press, New York, NY.
Mizuno, S. (Ed.), 1988. “Management for Quality Improvement: The7 New QC
Tools.” Productivity Press, Cambridge, MA (originally published in Japanese,
1979).
Peter S.P., Robert P.N., and Roland R.C. (2002). “The Six Sigma Way: An
Implementation Guide for Process Improvement Teams.” McGraw-Hill.
Pyzdek, T. (2001). “The Six Sigma Handbook – A Complete Guidefor Greenbelts,
Blackbelts and Managers at All Levels.” MGraw-Hill, New York, NY.
Raisinghani, M.S. (2005), “Six sigma: concepts, tools and applications”, Industrial
Management & Data Systems, Vol. 105 No. 4, pp. 491-505.
Sai Kim (2000). “Services Quality Six-Sigma case studies.” annual Quality Congress
Proceedings 54.
Sean P.G., CIT. “Understanding Six Sigma: Implications for Industry and
Education.” Journal of Industrial Technology. Volume 20, Number 4.
Shewhart, W.A. (1980). “Economic Control of Quality Manufactured Product.” Van
Nostrand.
Smith, D., Blakeslee, J., and Koonce, R. (1999). “Strategic Six Sigma.” Wiley,
Hoboken, NJ.
Summers,D.C.S. (2003). “Quality.” 3rd
edition. New Jersey. Prentice Hall. pp. 4-21,
54-101,618-624.
Womack, J.P. and Jones, D.T. (1990). “Lean Thinking, Simon and Schuster.” New
York, NY.
Further readings
1. Charles P.Quesenberry (1998). “SPC Methods for Quality Improvement.”John
Wiley & Sons, Inc.
2. P.B.Crosby (1979). Quality is Free. McGraw-Hill
3. Eckes. G, (2001). “The six sigma revolution.” New York. John Wiley & Sons.
4. XXX part production, Injection moldings manual book.
APPENDIX A
Table: - List of rejection or part from month JAN to MAC 2007
In-line (unit)per month
Model JAN FEB MARCH
BMQ-Case A 1048 1128 1301
BNX-spacer 368 574 1062
BPY-Spacer 193 561 792
BMQ-Case B 185 480 741
Stem 138 326 675
BPJ-Case A 108 294 477
BNT-Case B 73 277 461
BPJ-Case C 73 277 403
BAT-Case A 62 217 372
BBE-Case B 59 205 353
BBM-Case B 55 142 353
BNJ-Case B 55 140 350
BPJ-Case B 54 118 349
BBP-Case A 52 103 184
BBM-Case A 52 100 167
BLX-Case C 47 78 144
BHB-Case B 41 58 102
BBH-Case B 41 54 100
Bezel 21 44 77
BBE-Case A 21 44 51
BPK-Case A 17 42 45
BLP-Case C 13 38 39
BPD-Case B 13 36 35
BBP-Case B 9 27 28
BPZ-Case D 5 18 21
BPD-Holder 3 16 18
Other 470 909 1085
APPENDIX B
Table:-List of rejection type for month May to Oct 2007
BMQ-CASE A MAY '07
Machine No
Defect E01 E03 E07 E10
Black dot 77 694 545 536 1852 40.10 40.10
Scratches 4 608 490 188 1290 27.93 68.02
Oily/Dirty 0 320 330 43 693 15.00 83.03
Sht Mold 0 0 235 0 235 5.09 88.11
Part drop 28 86 100 17 231 5.00 93.12
White Mark 4 128 20 5 157 3.40 96.51
Dented 4 17 84 15 120 2.60 99.11
Silver mark 2 17 10 0 29 0.63 99.74
Burr 0 0 0 6 6 0.13 99.87
Sink Mark 0 0 5 0 5 0.11 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
total 4619
BMQ-CASE A JUNE '07
Machine No
Defect E01 E03 E07 E10
Black dot 90 714 489 519 1812 40.00 40.00
Scratches 1 673 432 178 1284 28.34 68.34
Oily/Dirty 0 12 306 43 361 7.97 76.31
Sht Mold 0 0 301 0 301 6.64 82.96
Part drop 28 86 158 17 289 6.38 89.34
White Mark 4 251 21 5 281 6.20 95.54
Dented 4 57 67 15 143 3.16 98.70
Silver mark 2 35 10 0 47 1.04 99.74
Burr 0 0 0 6 6 0.13 99.87
Sink Mark 0 0 5 0 5 0.11 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
TOTAL 4530
BMQ-CASE A JULY '07
Machine No
Defect E01 E03 E07 E10
Black dot 65 768 478 498 1809 39.65 39.65
Scratches 4 687 398 215 1304 28.58 68.24
Oily/Dirty 0 30 356 43 429 9.40 77.64
Sht Mold 0 1 326 2 329 7.21 84.85
Part drop 35 143 125 14 317 6.95 91.80
White Mark 4 195 20 5 224 4.91 96.71
Dented 5 30 45 15 95 2.08 98.79
Silver mark 3 35 3 0 41 0.90 99.69
Burr 0 2 2 4 8 0.18 99.87
Sink Mark 0 2 3 0 5 0.11 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
total 4562
BMQ-CASE A AUG '07
Machine No
Defect E01 E03 E07 E10
Black dot 67 754 545 524 1890 41.00 41.00
Scratches 4 675 432 183 1294 28.07 69.07
Oily/Dirty 0 53 357 64 474 10.28 79.35
Sht Mold 0 16 306 0 322 6.98 86.33
Part drop 23 135 83 17 258 5.60 91.93
White Mark 4 186 20 5 215 4.66 96.59
Dented 4 17 57 16 94 2.04 98.63
Silver mark 2 18 10 0 30 0.65 99.28
Burr 0 12 0 6 18 0.39 99.67
Sink Mark 0 9 5 0 14 0.30 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
TOTAL 4610
BMQ-CASE A SEPT '07
Machine No
Defect E01 E03 E07 E10
Black dot 68 742 534 465 1809 39.36 39.36
Scratches 4 653 421 208 1286 27.98 67.34
Oily/Dirty 0 54 347 54 455 9.90 77.24
Sht Mold 0 14 326 2 342 7.44 84.68
Part drop 35 143 118 8 304 6.61 91.30
White Mark 4 195 20 7 226 4.92 96.21
Dented 5 42 47 12 106 2.31 98.52
Silver mark 3 35 3 6 47 1.02 99.54
Burr 0 5 2 3 10 0.22 99.76
Sink Mark 0 7 3 0 10 0.22 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
total 4596
BMQ-CASE A OCT '07
Machine No
Defect E01 E03 E07 E10
Black dot 67 752 486 473 1778 39.27 39.27
Scratches 8 634 412 197 1251 27.63 66.89
Oily/Dirty 0 47 365 57 469 10.36 77.25
Sht Mold 0 1 319 16 336 7.42 84.67
Part drop 43 135 118 14 310 6.85 91.52
White Mark 4 165 33 12 214 4.73 96.25
Dented 5 38 53 15 111 2.45 98.70
Silver mark 4 34 4 1 43 0.95 99.65
Burr 0 2 2 4 8 0.18 99.82
Sink Mark 0 2 5 0 7 0.15 99.98
Weld line 1 0 0 0 1 0.02 100.00
Hook NG 0 0 0 0 0 0.00 100.00
Others 0 0 0 0 0 0.00 100.00
TOTAL 4528
APPENDIX C
Table: - Overall calculation for sigma level
OVER ALL PROCESS FOR SIX MONTHS (MAY '07 - OCT '07)
MACHINE 01
MONTH may june july aug sept oct
OUTPUT 74880 74880 74880 74880 74880 74880
REJECT UNIT 120 130 117 105 120 132
DPU 0.0016026 0.0017361 0.0015625 0.0014022 0.0016026 0.0017628
MACHINE 03
MONTH may june july aug sept oct
OUTPUT 74880 74880 74880 74880 74880 74880
REJECT UNIT 1870 1828 1893 1875 1890 1810
DPU 0.0249733 0.0244124 0.0252804 0.0250401 0.0252404 0.024172
MACHINE 07
MONTH may june july aug sept oct
OUTPUT 74880 74880 74880 74880 74880 74880
REJECT UNIT 1819 1789 1756 1815 1821 1797
DPU 0.0242922 0.0238916 0.0234509 0.0242388 0.0243189 0.0239984
MACHINE 10
MONTH may june july aug sept oct
OUTPUT 74880 74880 74880 74880 74880 74880
REJECT UNIT 810 783 796 815 765 789
DPU 0.0108173 0.0104567 0.0106303 0.0108841 0.0102163 0.0105369
TOTAL PER MTH
299520 299520 299520 299520 299520 299520
TOTALDEFECTS
4619 4530 4562 4610 4596 4528
DPU/ PER MONTH
0.0154213 0.0151242 0.015231 0.0153913 0.0153446 0.0151175
DMPO 3084.3 3024.8 3046.2 3078.3 3068.9 3023.5
