Minimization of defects in sewing section at Garment and Textile Factories through

DMAIC methodology of Six Sigma,

(Case: MAA Garment and Textile Factory)

By

Hewan Taye Beyene

Submitted to School of Mechanical and Industrial Engineering in partial fulfillment of the

requirement for the award of Master of Science degree in Quality Engineering and Management

Advisor: Fasil Taddesse (PhD)

Co Advisor: AssefaMisgun (MSc.)

School of Mechanical and Industrial Engineering

Ethiopian Institute of Technology-Mekelle

Mekelle University

August 2016

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Declaration

This is to certify that this title entitled “Minimization of defects in sewing section at Garment and

Textile Factories through DMAIC methodology of six sigma.” submitted for partial fulfillment

of the requirement for the award of degree of MSc in Industrial Engineering (Quality

Engineering and Management) Mekelle University through the school of Mechanical and

Industrial Engineering done by Hewan Taye Beyene under my guidance. The work contained in

this thesis has not been previously submitted for a degree at any other higher educational

institutions to the best of my knowledge and belief.

Student_________________________________________________________

Hewan Taye Beyene Date

Advisor ________________________________________________________

Fasil Taddese (PhD) Date

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Mekelle University

Ethiopian Institute of Technology-Mekelle

School of Mechanical and Industrial Engineering

Master’s program final thesis acceptance approval form

1. Student Name: Hewan Taye Beyene

Thesis Topic:Minimization of defects in sewing section at Garment and Textile Factories

through DMAIC methodology of Six Sigma, (Case: MAA Garment and Textile Factory)

2. This is to certify that Hewan Taye Beyene has incorporated all the comments forwarded to

her by the external and internal examiners during the thesis defense held on

_________________,2016

2.1. Student _________________________ __________________________

Hewan Taye Beyene Date

2.2. Advisor________________________ _________________________

Fasil Taddese (PhD) Date

2.3. Internal Examiner __________________ ________________________

Date

2.4. External Examiner ___________________ ______________________

Date

2.5. School head ______________________ _______________________

Date

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Acknowledgement

First of all, I would like to thank God of our ancestors, for what he has done to me I, definitely

believe that nothing could have been done without his guidance and help. Thank be to HollyVirgin

Mary, and His saints too, for helping me during those hard times.

I am very grateful to my advisor Dr.FasilTaddeses Assistant professor in school of Mechanical and

Industrial Engineering for exposing me to such kind of explorative and investigative thesis work. His

encouragement, excellent guidance, creative suggestions and critical comments have greatly

contributed to the thesis work. The readers who know Dr. Fasil will see his influence in this thesis.

All I can say is: Boss, thank you. I hope this rewarding co-operation will continue for many more

years. I would also like to thank my co-advisor Ato Assefa Misgun for such great knowledge and

experience in the quality domain and have given me very valuable advice along the way and I would

like also to express my deepest thanks to Mr.Goitom for the guidance he shared me and

Mr.AbrehaHaile for his continuous support and EyerusalemAdefris. I am having highly gratitude and

reverence to Professor (Dr.) Ashish Thakur, Mechanical Engineering Department for his continuous

encouragement and support during plentiful searching of research material and helped me in different

way in formatting and editing part setting my goals to enrich award master degree. I shall remain

grateful to Dr. Ashish supporting me throughout this M.Sc course duration.

During my study, I have been give due attention, special cooperation and continual support by our

coordinator Ing.Aregawi. I would also like to thank MAA Garment and Textile factory employees

and management for their cooperativeness; I sincerely appreciate their supports and participations in

carrying out this study. Many thanks goes toAtoAlula Hadush Quality manager ,AtoGebru

w/georgies garment production manager, AtoFiseha W/Gebriel assistant quality mgr, w/roAlem

G/medhin quality assurance department manager , Fiseha Belay HR and administration manager ,

KibromKiros (DGM),Daniel Gezae system and kaizen manager.

Lastly,The most important people for me in my everyday life, and therefore also important as

inspiration and support when I do my research, are my personal friends and most importantly, my

family deserves credit. The support and guidance I receive from them helps me to choose the best

path to take when the road leads in many different directions. Thank you. Finally special thanks go to

my father Ato Taye Beyene and my brother Engidawerek Taye for their encouragement, continual

support and love during my study.

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Abstract

The study explores the use of DMAIC methodology ofsix sigma to minimize the defect rate in

MAA Garment.This is a systematic approach towards defects minimization through five phases

of DMAIC methodology named Define, Measure, Analyze, Improve and Control which focuses

on reduction/elimination of four basic types of defects such as stain, skip stitch, broken stitch,

and slip out in sewing section of MAA-garment. Different six sigma tools were used in different

phases.The define phase rolls out the tools such as SIPOC which clearly show the process map

that includes suppliers, inputs, outs, customers and based on this the entire quality is judged. The

measure phase process capability was done.In analyzingphase, continuous assessment of sewing

process, with intense brainstorming sessions for the cause and effect diagram.Root causes of

those defects were detected by cause and effect analysis and Pareto analysis was done to identify

the major types of defects, the vital root causes that impact the defects by using regression,

ANOVA, why-why. The improve phase concentrates on optimizing the vital root causes which

impact the defects with their factors and levels, results of experimentation had been analyzed by

using Analysis of Variance (ANOVA) and regression for studying the variation and where does

the variation came from? In control phase, control chart has been used to control the variation

within acceptable levels in the process.Finally some potential solutions are suggested to

overcome those causes. The result of the proposed solutions of the defect level in the section has

been reduced as well as the result found to be very significant because the average amount of

broken stitch before optimization where 3.51852 and has been reduced to 1.51852 after

optimization. At the same time the prediction value of skip stitch also reduced from 14.8125 to

3.8125 as the result from the prediction shows.

.

Keywords: Garment, Defects, Rework, DMAIC, ANOVA, SIPOC DPMO, Cause and Effect

diagram, Pareto analysis

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List of contents Declaration .................................................................................................................................................... ii

Acknowledgement ....................................................................................................................................... iv

Abstract ......................................................................................................................................................... v

List of Figures…………………………………………………………………………………...viii

List of Table………………………………………………………………………………………ix

List of Acronyms ........................................................................................................................................ x

CHAPTER ONE ........................................................................................................................................... 1

INTRODUCTION ......................................................................................................................................

1.1. Background of the case company ................................................................................................. 1

1.2. Back ground of the study .............................................................................................................. 1

1.3. Problem statement ......................................................................................................................... 3

1.4. Objective ....................................................................................................................................... 4

1.4.1. General objective ...................................................................................................................... 4

1.4.2. Specific objective ...................................................................................................................... 4

1.5. Significance of the study ............................................................................................................... 4

1.6. Scope of the study ......................................................................................................................... 4

1.7. Methodology………………………………………………………………………….....4

1.7.1 Data Collection………………………………………………………………………......4

1.7.2 Data Analysis and Interpretation…………………………………………………………5

CHAPTER TWO .......................................................................................................................................... 6

LITERATURE REVIEW ......................................................................................................................... 6

2.1. Meaning of defect ......................................................................................................................... 6

2.2. Quality Improvement Tools .......................................................................................................... 6

2.3. Six sigma implementation ............................................................................................................. 7

2.4. Sewing defects .............................................................................................................................. 8

2.5. Alternative measurement method for determining the sigma level in six sigma .......................... 8

2.6. Process optimization methodology ............................................................................................. 10

2.7. Six sigma DMAIC methodology ................................................................................................ 10

2.8. Different six sigma tools ............................................................................................................. 12

CHAPTER THREE .................................................................................................................................... 14

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RESULTS AND DISCUSSION ............................................................................................................. 14

3.1. Introduction………………………………………………………………………………..14

3.2. Define Phase: .............................................................................................................................. 15

3.3. Measure Phase ............................................................................................................................ 17

3.4. Cost of Rework ........................................................................................................................... 19

3.5. Analyze Phase ............................................................................................................................. 20

3.6. Six month data causes for skip stitch .......................................................................................... 22

3.7. Why- Why analysis ..................................................................................................................... 27

3.8. Six month data causes for broken stitch……………………………………………………………33

3.9. Improve Phase ............................................................................................................................. 31

3.10. Control Phase .............................................................................................................................. 35

CHAPTER FIVE ........................................................................................................................................ 38

CONCLUSION AND RECOMMENDATION ...................................................................................... 38

4.1. Conclusion .................................................................................................................................. 38

4.2. Recommendation ........................................................................................................................ 39

REFERENCE .............................................................................................................................................. 40

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List of Figures

Figure: 1.1. Sewing section………………………………………………………………..3

Figure: 1.2. Billy T-shirt……………………………………………………………........18

Figure: 1.3. Most common sewing defects……………………………………………...20

Figure: 1.4. Binomial process capability reports for rework…………………………….22

Figure: 1.5. Pareto chart for sewing defects …………………………………………….25

Figure: 1.6. Defect amount and cumulative defect percentage of skip stitch……………26

Figure: 1.7. Cause and effect diagram for skip stich…………………………………….27

Figure: 1.8. Skip stitch…………………………………………………………………...28

Figure: 1.9.Stain………………………………………………………………………….28

Figure: 2.0. Defect amount and cumulative defect percentage of stain………………….29

Figure: 2.1. Cause and effect diagram for stain………………………………………….30

Figure: 2.2.Broken stich………………………………………………………………….30

Figure: 2.3. S/N ratio for skip stitch…………………………………………………….37

Figure: 2.4. S/N ratio for broken…………………………………………………………38

Figure: 2.5. P-chart of rework for May month…………………………………………...42

Figure: 2.6. Final P chart of rework for May month……………………………………..43

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List of Tables

Table: 1.1. Current manpower in MAA-garment and textile factory .......................................... 2

Table: 1.2. Defect rate of different sections…………………………………………………….3

Table: 1.3. Rating criteria……………………………………………………………………...17

Table: 1. 4.SIPOC……………………………………………………………………………..19

Table: 1.5. Process capability for six months………………………………………………….21

Table: 1.6. Binomial Capability study for June month………………………………………..22

Table: 1.7. Overall defects types percentage…………………………………………………..25

Table: 1.8. Defect location indicator…………………………………………………………..32

Table: 1.9. Selected parameters or skip stitch…………………………………………………34

Table: 2.0. Analysis of Variance for skip stitch……………………………………………….35

Table: 2.1. Broken selected parameters………………………………………………………36

Table: 2.2. Response table for signal to noise ratios…………………………………………36

Table: 2.3. Analysis of variance for broken…………………………………………………..37

Table: 2.4. Prediction value for broken before and after optimization……………………….39

Table: 2.5. Suggested solutions for all major defects with corresponding causes…………….41

Table: 2.6. May month defect rate…………………………………………………………….42

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List of Acronyms

SIPOC Supplier Input Process Output Customer

ANOVA Analysis of variance

DPMO Defect Parts per Million Opportunities

RCA Root Cause Analysis

VOC Voice of customer

S/N Signal Noise to Ratios

DOE Design of Experiment

USD United States Dollar

FGD Focus Group Discussion

DMAIC Define-Measure-Analyze-Improve-Control

DD Dull-Dull

DS Dull-Sharp

SD Sharp-Dull

SOP Standard Operating Procedure

WIP Work in Process

CP Process Capability

RPM Revolution per Minute

GSM Grams Square Meter

DF Degree of freedom

SMV Standard Minute Value

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CHAPTER ONE: INTRODUCTION

1.1. Back ground of the study

If a defect is detected in the final inspection, the cost of the production process for a defective

garment is totally wasted as the product cannot be exported. Sometimes the defective pieces can

be made exportable with alterations, but it costs more money, as utilizes more resources to

produce the same items. In MAA garment factory, defects usually come from sewing section.

Among those, the sewing section is the leading and most difficult in operation. In this context,

the sewing section defects such as stain, broken, skip stich slip out, wavy stiches etc., hamper the

total quality of the factory, because of these defects the rework rate reaches greater than 5%based

on the company daily rework rate and this rework rate is caused due to various reasons such as

poor quality of the thread, flagging of fabrics, needle deflection and others. Therefore the

required quality standards will not be fulfilling the VOC and to avoid this rework rate, it is

necessary to avoid defects by prioritizing them. Different authors propose several quality

strategies among them DMAIC is one and the reason DMAIC is selected itis a structured tool in

defining phase the problem can be easily defined in measuring phase.Current performance of the

company can be measured in analyze phase where the root causes of problems will be analyzed in

improve phase improving the process for eliminating the root cause and the final phase is the control

phase in order for sustaining the gains controlling the process is crucial and under all phases there are

various tools and techniques for minimizing the rework rate.

1.2. Background of the case company

Kebire Enterprises is a privately owned company established and registered in April 2001 under

the commercial laws of the Federal Democratic of Ethiopia. MAA Garment Factory began its

operation in June 2004 in the northern part of Ethiopia, Tigray, Mekelle.It is spearheaded by

dynamic local staffs combined with expatriate from Turkey, Pakistan & Philippines.The

production set-up is fully equipped with state of the art machineries & equipment from

renownedEmbedding methods and solutions to the production system has allowedblending in

more quickly in the international market. It intends to incorporate and upgrade the infrastructure,

maintaining the concern with its environmental and social responsibilities.MAA Garment and

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Textile factory is one of the modern private owned factories in Ethiopia. It is established in 2004

and equipped with state of art production facilities producing textile (yarn, griege and finished

dyed fabric), woven and knitted garments (T-shirt, Polo-shirt, trouser, Bed-sheet, Pack-out,

Pants, Pajamas etc). The vision of the company is to become an exemplary organization in local

as well as international textiles and apparel markets. To achieve this vision, the company has set

strategies on the area of developing a strong customer relationship, engaging in a continuous

product development, implementing methods for reduction in cost of production without

affecting quality of products, providing continuous personnel training and capacity building.

MAA has suppliers from domestic and international. Its market is targeted to USA & European

countries, with customers like H & M, Kik, Takko, Hahns and the like. There are domestic

(Almiko, Amibara, etc.) and overseas (Hantsman, Thies, Juki, etc.) suppliers of the raw material

used in the process in the company. The daily production is 10,000 garment pieces and to

produce these articles it has to pass the following processes production units: cutting-embroidery

and printing-sewing-packing department is the responsible entity to control and monitor the

quality status of each material, process and products of the company. To do its task properly, the

department has deployed the following qualified personnel in the sewing section.

Table: 1.1. Current manpower in MAA-garment and textile factory

S/N Department Current manpower

1 Spinning & knitting 266

2 Dyeing & finishing 78

3 Garment production 709

4 Quality assurance 131

5 Technical/maintenance 139

6 Planning & cost controller 61

7 Marketing & merchandising 11

8 Supply & procurement 7

9 Finance department 15

10 HRD department 59

11 Sample& design 10

12 System & kaizen 2

13 Deputy general manager 1

14 General manager 1

Total 1488

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Table 1.1 shows clearly the number of employees working in the MAA garment and textile

factory.

1.3. Problem statement

There are four sections under Garment wing of the company such as cutting, sewing, finishing,

embroidery and printing. Among those sections the sewing section highly suffering from rework

due to the existence of different defects.

Figure: 1.1.Sewing Section

Figure 1.1 shows the selected department from the rest sections.

Table: 1.2.Defect rate of different sections

S/N Section Rework Rate

1 Cutting section 0.5%

2 Sewing section 6%

3 embroidery and printing 2%

4 Finishing section 3.5%

However, this amount of rework rate 36489 is there the last 7 months from the section’s recorded

data. Thetrial to distinguish between the most influential and least influential defects is not found

yet. Their respective root causes are not clearly figured out. This in turn paves the way for the

problem to remain inherent and make difficulty in solving them. Therefore, this work

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incorporates the process of finding the leading defect with their particular root cause and in turn

solving them accordingly.

1.4. Objective

1.4.1. General objective

The general objective is to investigate thedefects in the sewing section and propose a

solution how to tackle itminimize the defects in sewing section by using DMAIC

Approach

1.4.2. Specific objective

To Identify and measure the frequency of occurrence of the critical defects;

To assess the contributing factors for each defect types using RCA;

To maintain improvements using appropriate control charts.

1.5. Significance of the study

The primary merits of the study goes to the Ethiopian textile industries. The researcher

believes that Ethiopian textile companies will be motivated to implement results of the

study. To be competent in the local and international markets, enhancecustomer satisfaction,

increase the productivity of the companies, and avoid poor utilization ofresources.

1.6. Scope of the study

The scope of this thesis work is to focus on MAA Garment and Textile Factoryfor minimizing

the defects in the sewing section so that overall activities of the companies affecting the rework

at the same time the quality of the product are undercontrol. The purpose of methodology will

only serve as a starting point.Thus it requires regular updating based upon the current situation

on the ground.

1.7. Methodology

1.7.1 Data Collection

In this research different methods of data collection were employed for gathering primary and

secondary data.Primary data was collected through, Check sheet were prepared for knowing the

causes of skip and stain .Questioner and direct observation for 20 days were used for identifying

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the causes for the critical defects by using 4M.Informal interview for the brainstorming session

for specifying the root causes of each defect. Secondary data was obtained from various sources

such as, journals, articles, books, internet sources, reviewing manual and reports as

references.Secondary data were used Six month production data for forming the pareto.

1.7.2 Data analysis and interpretations

The collected data were analyzed using Microsoft excel and Mini tab software for forming

ANOVA, Pareto diagram, Binomial process capability ,Control chart and DOE .SIPOC Cause

and effect diagram Why-why Population size30 participant's-25 answered because they were

related persons with the work. The Sampling size 240 Billy T-shirt were checked for the

location of the defects

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CHAPTER TWO: LITERATURE REVIEW

2.1. Fabric defects

Most of the products in the garment industry have a short lifespan. With new products, new

defects arise. Even after knowing the products in & out, it is very hard to keep defects from

finding their way to the finished product (Ojasvi,2015).According toKalayu(2014)the four

critical fabric defects (Needle line, Hole, Yarn variation and Lycra jump) are achieved and these

fabric defects need to be corrected to minimize the rejection rate by 80%. The output of the

research indicated that the existing 7.87% rejection rate can be reduced to 1.574% which resulted

in the net difference of 6.296.

2.2. Quality Improvement Tools

A large number of studies have determined major quality improvement tools According to

(Mazedul,2012;Maroof, 2012; Mashiur, 2012) their paper discusses the quality and productivity

improvement in a manufacturing enterprise through practical study. This paper deals with an

application of methodology in an industry which provides a framework to identify quantify and

eliminate sources of variation in an operational process, to optimize the operation variables,

improve and sustain process performance with well-executed control plans. The application of

the paper also improves the process performance of the critical operational process, leading to

better utilization of resources, decreases variations & maintains consistent quality of the process

output. The outcome of this observation reflected that an industry may gain higher productivity

and profitability with improved quality product by minimizing reworks activities. It also

minimizes cost and improves internal throughput time. Finally a general overview over this

development is given. Matthew(2008) stated that strong understanding of Six Sigma

management philosophy, concepts, and practices and to apply this knowledge to creating a Six

Sigma academic course or training program. This was done through three main methods:

preliminary research and data collection, the creation of a design model for Six Sigma academic

course/training program establishment, and the creation of a Six Sigma academic course/training

program syllabus.Jeannine(2005)also focuses on the joint use of two popular improvement

initiatives: Capability Maturity Model Integration (CMMI®) and Six Sigma. This report contains

a brief summary of each initiative and then outlines the connections between frameworks

commonly used in Six Sigma and the CMMI process areas (Steven, 2007)has make

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recommendations that would improve the performance of six sigma within the ford motor

company.Peter( 2007) stated that a longitudinal case study describing the evolution of ‘Six

Sigma Management’ at Siemens in Sweden. The success of the programmer was to a large

degree built on previous failures, confirming Juran’s old saying ‘Failure is a gold mine’. From

the case study, success factors for implementing Six Sigma at Siemens are identified and

compared to those given in the literature. The evaluation of sewing threads consumption in jeans

and classic pants clothingsix different input parameters are chosen and used for investigation. To

objectively evaluate their contributions, a Taguchi design analysis was applied. Jaouachi,

(2012),the research methodology concentrates the implementation of DMAIC and cause and

effective diagram are used for eliminating garment defectives and 5S Concepts for reducing

defects and motion (Vijayakumar, 2016).

2.3. Six sigma implementation

Many of the SMEs are not aware of six sigma and many do not have the resources to implement

six sigma projects. It was also found that lean sigma was not generally popular among SMEs.

Management involvement and participation, linking six sigma to customers and linking six

sigma to business strategies are the most critical factors for the successful deployment of six

sigma in SMEs according to(Christian, 2005).

Quality Improvement can play a vital role for improving productivity as well as economic

development for the country. They have used the tools of TQM such as Flow chart, Check sheet,

and Histogram, Pareto Chart, Scatter Diagram, Control Chart, Cause and effect diagram in a

garment industry of Bangladesh and it is found that a significant amount of rework and scrap per

style, per month has minimized by applying this method (Rahman, 2011).Senaviratna (2013)

Conducted to improve efficiency of the sewing process in apparel industry through the reduction

of defectives. This study is focused in three major defect types; run off stitches, unbalanced

shape and wavy seams. They have developed generalized linear models for each defect type to

find the significant factors affecting on the proportion of occurrence of defects the pros of

implementing the quality tools; review the issues related to the implementation of such tools and

finally understand the role of such tools in the framework of TQM. Kairong(2010), Detail

investigation on quality improvement of a garment factory by applying Pareto Analysis and

Cause-Effect Diagram,the aim of the study was to minimize defects that will reduce rework and

rejection rate (Tanvir, 2013).

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2.4. Sewing defects

According toDengzler( 2008) The SPC methods such as check list, pareto analysis, cause-effect

diagram and P control chart were used. The frequencies of sewing defects in the operations at the

sewing department are determined by check list. The operations which have highest sewing

defect rates and the effects of these operations to the defect rate are examined by using the pareto

analysis. The reasons of the defects are analyzed with the cause-effect diagram for the operation

which has the highest sewing defect rate. The rate of defect in the sewing department is

examined statistically by p control chart whether it’s under control or not. Sampling based

statistical quality control system is proposed in finishing section to eliminate 100% inspection by

sampling based inspection(Mohibul, 2013) .according to (Varsha, 2014) provide an easy

introduction of 7 QC tools and to improve the quality level of manufacturing processes by

applying it. An application of methodology in an industry which provides a framework to

identify quantify and eliminate sources of variation in an operational process, to optimize the

operation variables, improve and sustain processperformance with well-executed control

plans.Mazedul(2013), empirical evidence on Six Sigma implementation in service industries in

Singapore According to (Chakrabarty, 2010), Six Sigma case study analysis involving three

service organizations of Singapore the organizations are a local hospital, a construction and

related engineering service, and a consultancy service. Pantano(2006) designed to test the

application of a novel Six Sigma deployment programme specifically for SMEs. Introduction a

new notion, which we call six sigma supply chains to describe and quantify supply chains with

sharp and timely deliveries, and develop an innovative approach for designing such

networks.Garg(2004).Both total productive maintenance (TPM) and Six Sigma are key business

process strategies, which are employed by companies to enhance their manufacturing

performance. Therefore, will be to develop and implement an integrated Six Sigma Maintenance

(SSM) model for manufacturing industry (Andrew, 2008).

2.5. Alternative measurement method for determining the sigma level in six sigma

Tractable results are obtained by using statistical congruent method and concept of Taguchi

methodSong(2008) the authors analyze the principles and results of lean production and compare

the lean production philosophy with the six sigma quality process and the principles of total

quality management (TQM). At the end of the paper, it is discussed how to build the necessary

company culture for having success with these principles/management philosophies.Jens(2000)

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how organizational culture influences the implementation of different practices incorporated in

the recent Six Sigma approach as well as those associated with traditional total quality

management (TQM). (Xingxing, 2010).Pukhraj( 2011)the objective of this research is to

investigate the application and benefits of lean six sigma in the recycling industry Using XP with

Six Sigma can provide means of analyzing XP data and systematically improve process

performance. In order to prove our point, we map Six Sigma tools to activities involved at each

phase of XP and show that Six Sigma technique can be used with XP to measureand improve the

performance of XP process.Sajid(2010) identifying what criteria are considered for selecting six

sigma projects and how six sigma projects are selected in organizations in the

UK.Ricardo(2000).Application of Six Sigma usingDMAIC viz. Define, Measure, Analyze,

Improve & Control model for the defect reduction at amanufacturing concern based at Surat,

Gujarat, India(Tushar, 2010) thegrounded theory approach and the scant literature available to

propose an initial definition and theory of Six Sigma (Roger, 2007) Six sigma has enabled the

company to eliminate a wide range of long standing process variation problems. The importance

of a structured process and the importance of data collection, only when working with the facts,

rather than on “gut feeling” do the real causes become clear and solutions

effective.Andrew(2006), Six Sigma effective handling of deep rooted, 2006 the fundamentals of

Six Sigma and its use in education.Six Sigma is a set of methodologies used by businesses to

achieve extremely low failure rates in any process. (Varsha, 2006). Jones(2007)to offer a practice

guide for where to apply six sigma process improvement methodology to the corporate real

estate function (Jaruek, 2010)Six sigma programs are raging through corporations worldwide,

with some corporations citing savings in the $US billions resulting from six sigma

implementation(Bengt, 2001).Jiju(2008) the fundamental and critical differences between two of

the most powerful philosophies of modern quality management(Satya, 2009) develop an

effective implementation model which consists of six steps. The first step is to perform strategic

analysis driven by the market and the customer. A software tool that could be used at any stage

of lean implementation, including facilities with no existing.Philip(2004),sixsigma can be

implemented in manufacturing SMEs. Moreover, the motivation for adopting Six Sigma by

Australian manufacturing SMEs. It was found that a normative isomorphic change mechanism,

under institutional theory, is exclusively involved in the adoption of the Six Sigma methodology.

This study also discusses various critical success factors and impeding factors involved in the

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implementation of quality improvement initiatives, in general, as well as of Six Sigma, in

particular.Khawaja(2012), In this study an emphasis is given on human angle in context with

Indian Culture and work environment because experienceshows that for implementation of any

new initiatives in organizations, the successof implementation depends on soft skills of people of

organizations.Thomas(2011) provides an overview of success rates, success factors and existing

concepts and tools for business strategy implementation.

2.6. Process optimization methodology

Six Sigma and examines its possible applications to business strategy implementation through an

analysis of existing literature sources and expert interviews.Hongbo(2010) reviews some related

literatures to describe methodology, implementation and future researches. The present paper

summaries four issues within the sub-category of the initial Six Sigma concepts: basic concept,

DMAIC, DFSS and deployment (Henry,2011).This study adopted the Six Sigma methodology to

try to identify what factors/parameters are influencing the occurrence of web-flutter during paper

production processes at SCA Edet mill. It also highlighted other root causes for the unwanted

folding in the final product of tissue paper at the paper mill.Jiju(2009) present the results from an

empirical investigation of Six Sigma status in the Indian industry and underrepresented region of

investigation on Implementation. How both the traditional quality management and Six Sigma

literatures and identified three new practices that are critical for implementing Six Sigma’s

concept and method in an organization. These practices are referred to as: Six Sigma role

structure, Six Sigma structured improvement procedure, and Six Sigma focus on

metrics.Xingxing(2008), how the principles of total quality management can actually be used to

design and operate processes to assure supply chain security,the central theme of the quality

movement––that higher quality can be attained at lower cost by proper management and

operational design––is also applicable in supply chain security. By using the right Management

approach, new technology, and re-engineered operational processes, we can also achieve higher

supply chain security at lower cost.

2.7. Six sigma DMAIC methodology

According to Hau(2003), The quality and productivity improvement in a manufacturing

enterprise through a case study, the paper deals with an application of Six Sigma DMAIC

(Define–Measure-Analyze-Improve-Control) methodology in an industry which provides a

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framework to identify, quantify and eliminate sources of variation in an operational process in

question, to optimize the operation variables, improve and sustain performance viz. process yield

with well-executed control plans. Six Sigma improves the process performance (process yield) of

the critical operational process, leading to better utilization of resources, decreases variations &

maintains consistent quality of the process output.Tushar(2008)A few researchers

(Duraković,2012).(Benjamin(2012) conducted research on analyzing the manufacturing process

and considers the possibilities of using DMAIC methodology to the textile cutting process. The

points of discussion in this paper are the possibilities of selecting and implementing Six Sigma

projects as well as algorithms of continuous improvement after implementation of the projects.

Taking into account all characteristics of the production process, a systematic continuous

improvement process solution is designed as a first step in implementing Six Sigma quality

system. From the standpoint of feasibility and cost-effectiveness, the selected model is applicable

for small and medium-sized organizations, and is considered as a solution that will provide

positive results investigating the interactive phenomena of KM concepts with the Six Sigma

deployment process, and how KM concepts including updated elements could be integrated in a

structured, systematic and effective way with Six Sigma framework for project deployment. At

first different existing approaches related to Six Sigma and KM integration are analyzed in order

to identify the leveraging effects. Then a structured integrated conceptual model; namely

DMAIC- KM model has been proposed. (Mohan,2014).Chintan(2015) the author review the

previous published papers, and give the where the industry have to work, this paper mainly

focusing on the general overview of publication and the case industry, this paper gives which

methodology adopted by industry, discuss on the various tools and techniques used by the

company and how much benefits got by the company by using DMAIC methodology.(Thouraya,

2015)proposes a method for analyzing a process in a garment manufacturer. By applying

DMAIC Six Sigma methodology, Alsico’s factories in Tunisia expect to identify key root causes

of delivery delays. The project report outlines a method for defining, measuring, analyzing,

improving, and controlling the delivery metric, and illustrates this process with an example

(Varun, 2015).The main purpose of the work is to reduce the defects, which will also minimize

the rejection and reworks rate .This work provides the guidelines for the betterment and control

of wastes in garment industry for shorts and pants by using six sigma methodologies. The

DMAIC methodology is introduced and implemented in the Karle garment industry, by that

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major factor for the defects are determined, along with that the corrective actions are performed,

and the defective % is compared before and after the implementation of corrective actions, where

the sigma level has been increased from 2.8 to 3.38(Uddin, 2014) explores the use of DMAIC

methodology of six sigma to minimize the defects rate in a selected garment factory. This is a

systematic approach towards defects minimization through five phases of DMAIC methodology

named define, measure, analyze, improve and control.

2.8. Different six sigma tools

Quality improvement study applied at a yarn manufacturing company based on six sigma

methodologies. More specifically, the DMAIC (Define, Measure, Analyze, Improve, and

Control) project management-methodology & various tools are utilized to streamline processes

& enhance productivity.Neha(2013) implementation of the Six Sigma Methodology in an apparel

plant, that has provided an increase of 13% in the manufacturing productivity of trousers by

minimization of sewing thread breakage. This accomplishment has been obtained by reducing

the thread breakages in the process of the sewing trademark label to the rear center of a trouser

(Kürşat, 2014).Enamul(2013) study and evaluate processes of the case organization, to find out

current sigma level and finally to improve existing sigma level through productivity

improvement current sigma level has been calculated and given suggestions for improvement.

This has been done by using six-sigma DMAIC cycle. (Muhammad, 2010) is to give the

understanding of different problems in different departments in quality point of view and how to

reduce the problems by taking preventive action against any defects produce during process. For

quality of product and enhance the customer satisfaction, it is decided to more work on final end

product in the form of DMAIC tool to reduce the defects rate of product before going to

customer. Neha(2013) this article presents a quality improvement study applied at a yarn

manufacturing company based on six sigma methodologies. More specifically, the DMAIC

(Define, Measure, Analyze, Improve, and Control) project management-methodology & various

tools are utilized to streamline processes & enhance productivity how to minimize the defects

rate by introducing and implementing the DMAIC Methodology of Six Sigma into a sewing

section of a selected garment factory. This is a systematic approach towards defects

minimization through five phases of DMAIC methodology named Define, Measure, Analyze,

Improve and Control.Syed(2014), prosperity of implementing Six Sigma at Company XYZ on a

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particular product line by capitalizing on the DMAIC. According to Justin(2009), there are

various related problems which affect the processes and quality of the final product during paper

production. This study adopted the Six Sigma DMAIC methodology to try to identify what

factors/parameters are influencing the occurrence of web-flutter during paper production

processes at SCA Edit mill. It also highlighted other root causes for the unwanted folding in the

final product of tissue paper at the paper mill.Henery(2011) this research work develops an

application guideline for the assessment, improvement, and control of wastes in garment industry

using six-sigma improvement methodology. Chethan(2002) production defects in textile

manufacturing. The DMAIC approach has been followed to solve an underlying problem of

reducing process variation and the associated high defect rate. This paper explores how to use a

systematic and disciplined approach to move towards the goal of Six Sigma quality level. The

application of the Six Sigma methodology resulted in a reduction in the overall quality level

from 7.7 % to 2%. (Mohammed,2012).Srinivasan(2014)This case study narrowly focuses on

reduction/elimination of two imperative responses in spray painting process producing shock

absorbers, namely peel off and blisters using the Six Sigma Define-Measure-Analyze-Improve-

Control (DMAIC) approach that highly impacts quality at customer end.

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CHAPTER THREE: RESULTS AND DISCUSSION

3.1 Introduction

The aim of the work is to minimize the defect percentage by using DMAIC approach of Six

Sigma methodology. Based on brain storming session first out of the 3 main competitive factors

by using different criteria from the selected problems the most crucial problem has been

prioritized for doing the research.

Table: 1.3.Ratingcriteria

Rating 0-5 0-low 5-high

Selected problems Low productivity Quality

problem

Delivery date

Existing situation 4 5 3

Time condition 5 5 4

State of

Urgency

5 5 5

Cost 4 5 4

Work bottleneck 4 5 4

Complexity 4 4 4

Implementation time 4 4.5 3

Total 30 33.5 27

Rate 2 1 3

From Table 1.3, the three main competitive factors such as low productivity, quality problem and

delivery date with their corresponding selected criteria’s like Existing situation, time condition,

state of urgency, cost, work bottleneck, complexity ,implementation time the result from the

rating shows the quality problem is the leading having 33.5 rate and the second one is the low

productivity with a rate of 30 and the last becomes late delivery date with 27 rating factor so

from these the quality problem pays a significant role in the factory and doing the research in this

competitive factor can change. Since the aim of the work is to reduce the rework rate in sewing

garment production.

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Figure: 1.2. Billy T-shirt

Figure 4.1shows Billy t-shirt that were selected for conducting the research work

3.2 Define Phase:

Define is the first phase of the DMAIC methodology of Six Sigma. The purpose of this phase is

to define the problem, goal of the project and the process that needs to be improved to get higher

sigma level. There are different six sigma tools are available for define phaseSIPOC. This is a

process map includes Suppliers, Inputs, Process, Outputs and Customers. Quality is judged based

on the output of a process and the following table shows the SIPOC flow of the factory and also

it was used for understanding the clear relationship between customer and supplier

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Table: 1.4.Suppliers-input-process-output-customer (SIPOC)

Suppliers Inputs Processes Outputs Customer

Juki middle east

Fabric inspection T-shirts Edwards

Schmetz Spreading Polo Walls/walls

mart

Onursha Cutting Single jersy Obermeyer,

Mayer&cie Ticketing/bundling Bob Hanhs

Groz-Beckert kg Fusion Billy Usa

Shimasieki Cutting audit Kicki Germany

Monforts Unstitched cloth Preparatory Rooney MOC

Corino Machinery Quality check R-strech DVH apparels

Thies Thread Assembly V-strech China

Inplan Needles Quality check Basic T-

shirt Belk stores

Endeco Button Buttoning Polo T-shirt Siggi

Mahlo Zipper Thread trimming Brief Klowt Africa

Muratec Label Ironing Pajama George

Luwa air engineering

ag Man Quality check

Classical

shirt H&M

Ssse Materials Folding Classical

trouser Local

Ugolini

Pinning Casual

trouser Nj

Riter Packaging and

shipment Work wear

Blanks and

primark

Military

uniform TESCO

Caps TAKKO

Jeans shirt KIKS

Jeans

trouser HANHS

BERNE

CID

RESOURCES

Table: 1.4. shows different suppliers may provide different raw material one may be a raw

material with high quality and the other might be poor raw material and this may result high

effect on the final product for this reason the SIPOC will help us in differentiating the variability

place whether it comes from supplier, input, process, output or customer and also it will be very

easy for taking corrective action.

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Figure: 1.3.Most common sewing defects

Figure1.3.Shows that the most common defects that are encountered in the sewing section the

arrow indicates the location of the defects that are seen in Billy T-shirt

3.3 Measure Phase

At this phase, percentage of defects, capability study existing DPMO (Defect per Million

Opportunity) and Sigma Level of the selected factory were calculated the frequency of defects of

the inspected shirts was also calculated and recorded in the Table 1.5.

Table: 1.5.Process capability for six months records

Checked pieces Conforming pieces

263,101 239,710

239,487 221,947

171,949 161,364

328,892 311,165

291,936 273,352

275,269 260,256

8160 7754

1,578,794 1,475,548

Pleated

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Number of checked items = 1,578,794

Number of conforming items = 1,475,548

Number of non-conforming items = 103,246

DPMO = (Number of non-conforming items/ Number of checked items)*1,000,000

DPMO = (103,246/1,578,794)*1,000,000

DPMO = 65,396

Proportion of defectives = (DPMO/1,000,000)

Proportion of defectives = (65,396 /1,000,000) = 0.0654

Proportion of conforming units = 0.9346

Percentage of conforming units = 93.46

From the cumulative normal distribution, the number of standard deviations corresponding to

0.9346 proportion of conforming units is Z = 1.51.

Similarly, Cp can be computed from the obtained number of standard deviations as:

Cp = Z/3

Cp = 1. 51/3 = 0.503

As the computed Cp value is less than 1, it can be concluded that the process is not capable.

Table: 1.6. Binomial Capability study for June month

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 11197 10594 441 3.9%

2 12132 11511 485 4.0%

3 8160 7754 327 4.0%

4 4873 4597 245 5.0%

5 10465 9878 479 4.6%

6 5145 4870 213 4.1%

7 6670 6273 313 4.7%

8 7969 7449 402 5.0%

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The capability of a process should be constantly measured and analyzed. Capability analysis can

help you answer following questions: Is the process meeting customer specifications? How will

the process perform in the future? -Are improvements needed in the process?

Figure: 1.4. Binomial process capability reports for rework

Analysis-The outcome is given in the Table and showing %defectives is 4.36%

%defectives=no of defectives/total checked*100

2905/66611*100=0.0436*100=4.36%

DPMO=2905/66611*1,000,000=43,611.41

DPO=DPMO/100000=43,611.41/1000000=0.0436

3.4 Cost of Rework

Example: rework of 1 line/day = 80 pieces from daily output of 1200 pieces, and from this let me

categorize the rework in to different operations like Stain 30 pieces, Skip stitch 25 pieces (at

bottom hemming and cuff hemming),Brocken stitch 30 pieces (at bottom hemming and cuff

hemming),Slip out 30 pieces (at sleeve attaché and side seam).

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The estimated time per operation is for instance as per above, in garment industries reworking a

piece of garment takes 3 times more than making first time a good pieces. Having these 80

pieces of daily rework at one operation is equivalent to stitching 240 pieces at exactly that

specific operation. So let’s take an average operation time is 25 seconds. Total time on rework is

25 sec*240pieces= 6000 seconds=1.67 hours

Garment minute value is 0.03 USD/ minute

=1.67*60*.03

=3.006 USD will be the cost of one operation/ garment. If we have to rework may be critical

operation per garment we have to see SMV of a product i.e. for Billie 6.25 minutes

6.25*80*.03usd/minute =15 USD equivalent to 315 birr to make a rework of 80 pieces.

3.5 Analyze phase

The goal of the analyze phase is to go through the data to find out the root causes of the problems

and seek improvement opportunities. At the measure phase 3 major types of defects were

identified and the target of this phase is to find out all the potential causes of those defects.

Fourproblems solving six sigma tools were used at analyze phase and these were: cause effect

diagram, Regression, pareto, why-why. In order to identify the potential causes of the defects and

their respective solutions in the Brainstorming session large number of creative & useful ideas

were generated and it was arranged at the selected session and carried out by the presence of the

following members that is shown the appendix.

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Table: 1.7. Overall defects types percentage

S/N Defect Type Frequency

(Occurrence)per month

Percent Total Cumulative

percentage

1 Stain 876 29.50 29.5

2 Skip Stitch 774 26.06 55.6

3 Broken 363 11.31 66.87

4 Slip Out 288 9.70 76.57

5 Tack Missing 257 8.65 85.22

6 Label Missing 113 3.80 89.02

7 Rib Uneven 107 3.60 92.62

8 Side Seam Un Even 81 2.72 95.34

9 Size Mixed 58 1.95 97.29

10 Wavy Stitch 28 0.94 98.23

11 Contamination 14 0.47 98.7

12 Others 10 0.33 99.03

Total 2969

Table 1.7shows all defect types in sewing section

Figure: 1.5. Pareto chart for sewing defects

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The chart visually shows which defects are more significant. By using Pareto Chart major

sewing defects were identified. i. stain , ii. Skip stitch, iii. Broken Among all, only these 3

defects are responsible for 68% of total defects percentage. Observations from the pareto

Analysis stain is the most frequent defect type with 29.5%. Among other defect types

contribution of skip stitch is 26.1% and broken stitch is 12.2%. So these three defect types are

responsible for 66.87% of the total sewing defects

3.6. Six month data causes for skip stitch

The Pareto Analysis performed based on six months combined defect data from the sewing

section. From this analysis we can identify the “Vital few” areas where maximum defects

occur. The analysis is shown in figure represents defect amount and defect percentage and

cumulative defect percentage.

Figure: 1.6. Defect amount and cumulative defect percentage of skip stitch

Figure.1.6. shows all defects that where coming from the material, method, machine, and man

are listed and prioritized and the pareto indicates that the quality of the thread plays significant

role for the cause of skip stitch the second one looper to needle timing the cause that comes from

the machine also has a great effect for the skip stitch and also the others can contribute with the

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same case with the quality of the thread and looper to needle timing the only difference is in their

frequency of their occurrence. for Top machine Defect looper to needle timing is the most

frequent machine defect, needle bent or dull the second most frequent machine defect, skill

problem, long time needle usage are the vital few machine defect that play great role for the

occurrence of skip stitch further Pareto Analysis for the material defects can be seen on those top

defect positions to identify the vital few defect types that are responsible for maximum amount

of defect.

Figure: 1.7.Cause and effect diagram for skip stitch

Figure1.7. shows the cause and effect diagram for skip stitch and then the potential causes

wereidentified by online inspections and root cause analysis of defectsfrom Pareto Analysis top

three defect types such as Skipped Stitch, Broken Stitch and stain have been identified. By our

own observation and data provided by supervisors from five production lines through

questionnaires the causes for each specific defect types have been identified. Then these causes

are ordered in a hierarchy according to the frequency of the feedback provided by QC

supervisors.out of all causes the critical root causes were Absence of scheduled maintenance,

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Lack of autonomous maintenance, Spare part shortage, Lack of skill, Machineadjustment, Low

thread quality, Long time needle usage. The critical causes of each acute defect were sorted out

and the respective remedies are set as it is discussed here below.

Figure: 1.8. Skip stitch

Irregular stitching along the seam and it appears due to improper handling of cut pieces or

machine usage. Improper threading, ,Machine dirty, Wrong setting ,Long time needle usage

Needle bent or dull ,Incorrect needle insertion ,Skill problem ,Poor quality needle, Poor quality

thread ,High speed ,Handling problem.

Figure: 1.9. Stain

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Figure: 2.0. Defect amount and cumulative defect percentage of stain

From Figure 2.0 same conclusion can be drawn for the stain.After Pareto Analysis the result it is

found that top defect types are identified and also they are responsible for maximum amount of

defects. Staincan be classified into four the first one is oil stain it is caused when the oil falls on

to the fabric by various reasons and the appropriate remedies can be after oiling ensuring the oil

does not falls on the fabric as well as proper oiling .Rust stain can be caused If any rust on the

machine parts is there and to avoid this the operator should check if any rust on the machine

parts are there and then clean it as well as proper maintenance. the third type of stain dirt stain

and can be caused when there is too much dirt on the knitting floor that is flying to and the

appropriate solution would be cleaning all types of dust continuously and the final type of stain is

handling stain and it can be caused by poor handling of the fabric and can be avoided by properly

handling and there are also other several causes such as Packing, Numbering stain, Uncleaned

machine ,Lack of sop ,Transportation problem ,Standardization the detail causes and remedies

are stated in the fish bone diagram.

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Figure: 2.1. Cause and effect diagram for stain

Out of all stain causes the most critical one from all are lack of stop, poor handling, lack of skill

absence of scheduled maintenance and lack of training.

Figure: 2.2. Broken stitch

The third type of defect is broken stitches and caused when the thread run out during sewing and

broken stitch during following of the finished product, high speed, operatorcarelessness, and low

quality of thread. Low quality scissor and to minimize thread breakage using a better quality

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sewing thread .make sure sewing machines are properly maintained and adjusted for the fabric

and observe sewing operations for correct material handling techniques. Suggested Solutions,

this study tried to suggest some potential solutions to minimize the causes of defects through

Brainstorming, direct observation and literature review. The solutions with their corresponding

causes are given in table.

Table: 1.8.Defect location indicator

Defect category Defect location

Stain Back and front

Skip stitch Bottom and cuff hem

Broken Bottom and cuff hem

From Table 1.8, the place where the most critical defects found to beis at the back and front and

in the bottom and cuff hem.

3.7. Why- Why analysis

An important component of root cause analysis is a thorough understanding of “what happened.

The information-gathering process includes interviews with staffs and workers who were directly

and indirectly involved with the physical environment where the event and other relevant

processes took place, along with observation of usual work processes. This information is

synthesized into a “final understanding”, which is further used by the team to begin the “why”

portion of the analysis in a logical sequence to find a logical solution to the problem. It is one of

the many brainstorming methodology of asking “why” five times repeatedly to help in

identifying the root cause of a problem. If a problem is repeatedly questioned, each time an

alternative solution comes out which is linked to the root cause. However, asking why may be

continued till getting an agreeable solution. Five is an arbitrary figure. The theory is that after

asking “why” five times one is likely to arrive at the root cause also it wasemployed in

developing critical thinking in identifyingthe root causes of the problem & recognizing

therelationship among them. The process of askingquestions was continued till the root cause of

theproblem has been identified. (i) Why operators increase the speed? Forfinishing the work fast.

(ii) Whythey want to finish the work fast?Due to not known the effect and dissatisfaction by the

set speed. (iii) Why dissatisfaction by the setspeeds and not knowing the effect? Due tolack in

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awareness of problemand over confidence. Why over confidence and awareness problem due to

lack of continuous training and high experience (iv) why lack ofcontinuous training and high

experience? Because they are only trained when they are hired. (v) Why they are trained when

they ae hired? Due to there is high cost for training. Detail can be seen in theappendix

Table: 1.9.Parametersfor skip stitch

Parameter level A:needle time B :speed C:needle

insertion

direction

D : thread

quality

Level 1 3 3000 correct Polyester

Level 2 6 3500 incorrect Cotton

Level 3 9 4000

Level 4 12 4500

According to the Table1.9, It is clear that the needle time, speed, needle insertion and the thread

quality are factors that affect the occurrence of skip stitch. Each factors has levels to start from

the needle time in the first level it indicates changing the needle.

General Regression Analysis: Mean Skip stitch versus needle insertion, thread quality,

Regression Equation

Needle insertionThread quality

CorrectlyPolyesterMean Sk.St.= -7.3125 + 0.141667 Needle time + 0.00391667 Speeds

Correctly Cotton Mean Sk.St. = -2.1875 + 0.141667 Needle time +0.00391667 Speeds

Incorrectly PolyesterMeanSk.St. = -7.35417 + 0.141667 Needle time +0.00391667 Speeds

Incorrect Cotton Mean Sk.St.= -2.22917 + 0.141667 Needle time +0.00391667 Speed

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The controlled factors under consideration are both categorical and continuous type,the

categorical ones are needle insertion direction and thread quality whereas the needle time and its

corresponding speed are of type continuous because the latter two parameters can assume any

numerical values in their operating ranges. The above regression equation shows that when

correct -polyester becomes the combined subject, the skip stich will increase by a factor of

0.141667 and 0.00391667 for an increase of needle time and speed respectively by one unit.

Similar conclusions can be drawn in this manner for correct-cotton, incorrect –polyester and

incorrect-cotton shown above.

Table: 2.0.Analysis of Variance for skip stitch

Source Seq SS Adj SS Adj MS F P

Needle time 3.613 3.613 3.613 0.24288 0.631824

Speed 76.701 76.701 76.701 5.15688 0.044239

Needle insertion 0.007 0.007 0.007 0.00047 0.983148

Thread quality 105.063 105.063 105.063 7.06368 0.022276

Error 163.610 163.610 14.874

Total 348.993

For the ANOVA Analysis according Montgomery for a factor to be considered significant the

computed p values needs to be less than or equal to the selected alpha value. For the selected

alpha value of 0.05 and it is selected because it fits with the data the factors speed and thread

quality plays a significant role for the occurrence of skip stich as they assumed a value of

0.044239 and 0.022276 respectively.

3.8.Six month data causes for brokenstitch

Table: 2.1.Broken selected parameters

Parameter level A:scissor edge B :thread type C :fabric specification

Upper Lower

Level 1 Dull Dull Thin 2

Level 2 Dull Sharp Normal 3

Level 3 Sharp Dull Thick 4

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For broken stitch the relevant factors are scissor edge, thread type and fabric

specificationtheirrespective levels for scissor edge is dull on both edge, upper edge dull and

lower edge sharp while lower edge is dull and upper edge sharp. And for thread type the

appropriate levels are thin normal and thick while the levels of fabric specification can assume

continuous values of 2, 3 and 4 for level s 1, 2 and 3.

Table: 2.2. Response table for signal to noise ratios

Level Scissor edge Thread type Fabric Speci.

1 -13.58 -14.47 -12.88

2 -13.81 -10.06 -15.26

3 -11.47 -14.34 -10.72

Delta 2.34 4.41 4.54

Rank 3 2 1

General Regression Analysis: mean broken versus fabric speci, scissor edge

Regression Equation

Scissor thread

Edge type

dd thin mean broken = 7.42593 - 0.611111 fabric specification

dd normal mean broken = 5.31481 - 0.611111 fabric specification

dd thick mean broken = 7.09259 - 0.611111 fabric specification

ds thin mean broken = 7.42593 - 0.611111 fabric specification

ds normal mean broken = 5.31481 - 0.611111 fabric specification

ds thick mean broken = 7.09259 - 0.611111 fabric specification

sd thin mean broken = 6.64815 - 0.611111 fabric specification

sd normal mean broken = 4.53704 - 0.611111 fabric specification

sd thick mean broken = 6.31481 - 0.611111 fabric specification

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Table: 2.3.Analysis of variance for broken

Source DF Seq SS Adj SS Adj MS F P

Regression 5 11.1790 11.1790 2.23580 0.82008 0.606344

Scissor edge 2 1.2099 1.2099 0.60494 0.22189 0.813074

Thread type 2 7.7284 7.7284 3.86420 1.41736 0.368682

Fabric specification 1 2.2407 2.2407 2.24074 0.82189 0.431462

Error 3 8.1790 8.1790 2.72634

Total 8 19.3580

3.9. Improve Phase

The purpose of the DMAIC Improve phase is to discover a solution to the problem that the task

aims to address. This involves brainstorming potential solutions, selection of solutions to test and

evaluating the results of the implemented solutions.

12963

-18.0

-19.5

-21.0

-22.5

4500400035003000

IncorrectCorrect

-18.0

-19.5

-21.0

-22.5

CottonPolyster

Needle time

Mea

n of

SN

rati

os

Speed

Needle insertion Thread quality

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Figure: 2.3. S/N ratio for skip stitch

Often a pilot implementation is conducted prior to a full-scale rollout of improvements. Since a

defect is an unwanted feature in a product or absence of a desired feature or a feature existing in

distorted or wrong way. There are two main types of defects happened in the garment industry.

These are none sewing defect and sewing defect.

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The selected parameter setting for attaining smaller amount of skipped stiches, higher S/N ratio

in this case, is A1B1C1D1. This indicates that the needle time should be at its lowest level which

is three days, stitching speed at 3000 rpm, correct needle insertion and polyester thread type.

The predicted amount of skipped stiches after the process parameters are set at their optimum

levels has been determined to validate if the optimal process parameter setting brings about a

significant reduction in the level of skipped stiches. The following table shows a comparison of

the average skipped stiches before and after the optimal setting.

Figure: 2.4. S/N ratio for broken

The selected parameter setting for attaining smaller amount of broken stiches, higher S/N ratio in

this case, is A3B2C3. This indicates that the scissor edge should be both sharp and dull. , thread

type must be normal and trimming based on the specification which is 4mm. The predicted

amount of broken stiches after the process parameters are set at their optimum levels has been

determined to validate if the optimal process parameter setting brings about a significant

reduction in the level of broken stitch. The following table shows a comparison of the average

broken stitches before and after the optimal setting.

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Table: 2.4. Prediction value for broken before and after optimization

S/N Average amount of broken

stiches before optimization

(A1B3C1)

Average amount of broken

stiches before optimization

(A2B3C1)

Average amount of broken

stiches after optimization

(A3B2C3)

1 5.29630 3.51852 1.51852

S/N Average amount of skipped

stiches before optimization

(A3B2C1D2)

Average amount of skipped

stiches before optimization

(A3B2C2D2)

Average amount of skipped

stiches after optimization

(A1B1C1D1)

1 14.8542 14.8125 3.8125

Table: 2.5. Suggested solutions for all major defects with corresponding causes

Areas Causes Suggested solutions

Human Inadequate training and

operator inefficiency

Technical Training for the operators

Carelessness Improve supervision.

Machine Machine is threaded

incorrectly

or excessive thread tension

Rethread machine and maintain

proper thread tensions. Make sure the

thread

passes through the tension discs

Dull or bent sewing machine

Needle and knife.

Replace the needle and knife with a

new one.

Excessive pressure on the

presser foot

Lessen the pressure on the presser

foot. Slacken both tensions.

Machine dirty Autonomous maintenance

Wrong setting Proper setting

Long time needle usage Changing the needle frequently

Numbering stain Proper numbering

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Uncleaned machine Cleaning the machine frequently

Method Incorrect size of the needle

and

thread for operation

The size of the needle and thread

should be synchronized. Ensure both

the

needle and bottom (looper) positions

are rightly fed by the correct thread

type and size.

Incorrectly inserted needle Insert the needle on correct position.

Check that the bobbin is wound

correctly and no loose threads or

loops sticking out.

Comparatively long stitch for

the type of fabric in work.

Shorten the stitch length by means of

the stitch regulator, especially when

Sewing fine fabrics.

Poor handling Proper handling fabric

Lack of SOP Structured SOP

Lack of Transportation Use appropriate Transportation

Standardization Every work should be standardized

Packing Use Proper packaging material

Material Poor quality thread Use good quality thread.

Poor quality needle Use high quality needles from

another brand. Needle should have

high heat

Resistance capacity.

Poor quality scissor Good quality scissor

From the Table 2.5.there can be many reasons behind the problems that arise while sewing and

knowing the cause of these problems and a solution for each particular cause is essential. These

problems can be minimized by avoiding errors during handing of materials and machines by

following the right working methods.

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3.10. Control phase

Attribute control chart-p-chart were used for non-conforming (defective products) per day and a

sample of six month data has been taken periodically from the production process, and the

proportion of defective items in the sample were determined to see the proportion falls within the

control limits on the chart.

Table: 2.6.May month defect rate

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 275269 260256 11366 4%

2 9016 8476 380 4.2%

3 14976 14050 643 4.3%

4 13376 12524 618 4.6%

5 7107 6620 318 4.5%

6 9913 9253 466 4.7%

7 5791 5484 229 4.0%

9 7004 6550 371 5.3%

10 10183 9569 471 4.6%

11 12342 11680 505 4.1%

12 11087 10380 583 5.3%

13 14307 13674 505 3.5%

14 12329 11745 428 3.5%

15 5215 4939 198 3.8%

16 11088 10419 521 4.7%

17 7297 6699 450 6.2%

18 12281 11673 456 3.7%

19 6869 6543 248 3.6%

20 5782 5529 205 3.5%

21 2550 2450 87 3.4%

22 380 351 24 6.3%

23 12406 11854 459 3.7%

24 11727 11055 523 4.5%

1577

36

25 13144 12491 515 3.9%

26 14863 14262 452 3.0%

27 9161 8698 339 3.7%

29 1691 1581 41 2.4%

30 11236 10529 568 5.1%

31 12327 11707 441 3.6%

Figure: 2.5. P chart of rework for May month

From the Figure 2.5, the process were below the upper and lower limit, the process is out of

control and the cause has been detected and there were assignable cause of variation that affects

the process. Since the process being below the lower limit is good because since reducing the

rework rate is our target. process were above the upper control limit during day 10, 18,20,26,28

thethese shows that something was wrong and from the inspection results of the company shows

that in day 10 a defective cotton raw material were purchased from new supplier, in day 18

different operatorsproblems, in day 20 un proper needle adjustment problem by the operator, in

day 26 poor handling problem and in day 28 there were a spare part shortage. Already the nature

of the assignable cause of variation has been identified and after this counter measures should be

taken for making the process stable.

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37

Figure: 2.6.Final P chart of rework for May month

From Figure 2.6 assuming that Actions were taken so that the processes remain in control after

the improvement plans have been implemented, out of control state has been quickly detected &

associated special causes were determined so that actions can be taken to correct the problem

before non-conformities are produced.Now the process is in control and the company needs to

sustain the stable process and also control plans should be prepared for specifying

processmonitoring & corrective actions. To summarize in define phase the problem were

identified, in measure phase the current performance were measured, inanalyze phase the root

cause of the problem were analyzed, in improve phase to eliminate the root causes the process

were improved finally in control phase the process to sustain the gains controlling the process

due to this reason the methodology so effective.

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CHAPTER FOUR: CONCLUSION AND RECOMMENDATION

4.1 Conclusion

Most difficult thing in manufacturing is producing right quality product for all the time. Still it is

important to work on improving product quality to satisfy customers, to control production cost

and to save energy. Improving product quality is generally not difficult, but requires deliberate

and ongoing attention, and it requires the commitment of every individual within the

manufacturing company, from the person who cleans the floors to the most senior manager.

Quality of product plays an important role in any industry. Manufacturing the quality product is

mandatory to sustain in this global competitive market If a consumer does not like the quality of

the product then the chances of sales become very less. Today, clothes play a very important role

in the society. They help in creating an impression and define the human being in some aspects.

Therefore it is very important to see to it that garments are made of high quality. In garment and

textile factory, sewing is one of the major processes. It plays an important role in maintaining the

quality of clothes. Hence it is important that all defects have to be avoided during sewing any. In

this paper, various faults or defects that can take place during sewing along with different

remedies or methods to solve these defects are discussed. Minimizing defect is very important

for ensuring the quality of products. From the Pareto Chart total three defects were identified

those were responsible for more than 80% of total defects occurring in the sewing section of the

MAA garment factory for the item of T-shirt. After finding the major defects, brainstorming tool

was used to identify the probable causes and then potential root causes were identified by online

inspections and root cause analysis. This method is very effective to the minimization of defects.

As the minimization of defects is a continuous process further implementation of this

methodology will help the company enjoying more reduction on defect rate and improvement on

productivity. This study is concentrated only the sewing section, an integrated study of other

sections of the factory such as knitting, dying, cutting and finishing can be more effective on the

minimization of defects. Many garment factories in Ethiopia are not conscious about the Six

Sigma concepts and this study will start a positive surge across the garment industries and make

them more competitive.

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39

4.2 Recommendation

This research has been carried out on sewing section of MAA-garment factory to improve its

level of quality status. Currently analyzed data indicates that the rework rate is very high when it

is evaluated with the company‘s quality target. To improve its quality of product the study

focused in reducing the rework rate. The factory need to address the critical causes of defects to

improve its quality based on the following recommendations. The factory need to focus on the

critical identified fabric defects and their respective causes to improve its quality and Preventing

defects will be given more priority than correcting defects and it is recommend that Standard

Operating Procedure (SOP) is used as it is the best prevention tool to minimize defect rate while

operating production and it also keeps machine healthy and also the factory need to monitor

machine operation, machine cleaning, Replacement of dull or bent sewing machine needles and

knives with new ones , Insert the needles to the correct position, Provide adequate training to the

Operator, Improve supervision, Change faulty machine parts, Develop a proper quality

management system in order to quick detection and solution of the quality problems. Always use

good quality thread and finally The Company needs to assist further research in the section

periodically to reduce the rework rate continuously.

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40

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APPENDIX 1

Questionnaire format

S/N Employee

name

Job title List down all T-shirt

defects that you are

encountered in your

daily production

And also mention all the

possible root causes for

each defects

1

2

3

4

5

6

7

8

9

10

APPENDIX 2

Check sheet for skip stitch

S/N

Machines 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-

11

Total

1 Improper feeding

2 Pre adjustment

3 Machine setting

4 Long time usage

5 Needle bent or dull

6 Needle insertion

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47

7 Skill problem

8 Improper

threading

9 Worn defective

thread guides or

eyelets

10 Looper to needle

timing

11 Spare part shortage

12 Wear out looper

S/N manpower 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-

11

Total

1 Fatigue

2 Wrong setting

3 Increase in speed

S/N Method 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-

11

Total

1 Improper

threading

2 Machine

adjustment

3 Improper

threading

4 Operator skill

5 Types of operation

6 Tension

adjustment

1589

48

S/N Material 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-

11

Total

1 Fabric type

2 Types of

thread

3 Thick or thin

4 Neaps on the

thread

5 Quality of the

thread

APPENDIX 3

Check sheet for stain

s/n Machines 2-3 3-4 4-4 5-6 6-7 7-8 8-9 9-

10

10-

1`1

Total

1 Measuring type

2 Numbering machines

3 Lack of scheduled

maintenance

4 Machine break down

5 Un cleaned machines

S/N Method 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-

10

10-

11

Total

1 Lack of stop

2 Lack of standard

3 Transportation

4 Poor handling

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49

5 Lack of training

6 Awareness

7 Standardization

8 Work place

s/n Man 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-

11

Total

1 Skill of operation

2 Lack of operation

hygiene

3 Lack of training

4 Lack of awareness

5 Lack of ownership

6 Not satisfied

S/N Material 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-

11

Total

1 Material handling

2 Material type

3 transportation

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APPENDIX 4

Summarized monthly rework rate with their percentage

Defect rate (December)

January

Days Checked Pcs Ok Pcs Rework Pcs Rework%

1 7567 7065 436 5.8%

2 2737 2543 167 6.1%

4 7477 6967 436 5.8%

5 8721 8179 461 5.3%

6 5736 5345 326 5.7%

8 9559 8706 724 7.6%

9 6949 6361 471 6.8%

11 9777 8925 712 7.3%

12 13792 12836 839 6.1%

13 13418 12550 766 5.7%

14 13587 12524 950 7.0%

15 12167 10989 994 8.2%

16 8813 7943 692 7.9%

18 11479 10798 573 5.0%

19 11988 11146 751 6.3%

21 11386 10573 712 6.3%

22 10475 9952 668 6.2%

23 4469 4089 315 7.0%

25 10048 9153 759 7.6%

26 12356 11467 713 5.8%

27

28 13052 12265 702 5.4%

29 14306 13376 802 5.6%

30 9822 9187 542 5.5%

1592

51

February

Days Checked Pcs ok Rework Pcs Rework%

1 171949 161364 8953 5%

2 14136 13133 860 6.1%

3 9539 8917 543 5.7%

4 7768 7270 432 5.6%

5 8098 7596 433 5.3%

6 3017 2816 173 5.7%

8 191 159 28 14.7%

9 8977 8433 459 5.1%

10 6659 6127 451 6.8%

11 673 620 45 6.7%

12 85 82 3 3.5%

13 1532 1470 54 12.8%

15 1076 986 68 6.3%

16 2793 2554 202 7.2%

17 2452 2263 151 6.2%

18 4707 4376 277 5.9%

19 18131 17464 558 3.1%

20 10180 9640 470 4.6%

21 8037 7608 368 4.6%

22 14691 13738 767 5.2%

23 7264 6754 415 5.7%

24 2833 2668 128 4.5%

25 11896 10886 876 7.4%

26 11327 10495 708 6.3%

27 8850 8305 479 5.4%

28 8136 7588 480 5.9%

29 18780 17762 841 4.5%

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52

March

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 6250 5803 347 5.6%

2 5509 5143 330 6.0%

3 13985 13118 750 5.4%

4 14936 14022 738 4.9%

5 8890 8296 511 5.7%

6 8030 7516 438 5.5%

7 12797 12167 530 4.1%

8 12332 11704 551 4.5%

9 14300 13690 531 3.7%

10 10407 9892 431 4.1%

11 9819 9251 490 5.0%

12 6583 6203 321 4.9%

14 4951 4675 236 4.8%

15 10532 101277 343 3.3%

16 15080 14516 488 3.2%

17 15719 15112 521 3.3%

18 15100 14491 535 3.5%

19 12007 11533 400 3.3%

21 19031 18233 694 3.6%

22 18361 17410 849 4.6%

23 15395 15097 0 0%

24 11532 10847 557 4.8%

25 7540 7093 378 5.0%

26 9566 8888 579 6.1%

28 8455 7613 745 8.8%

29 11950 11163 682 5.7%

30 13634 12719 782 5.7%

31 11943 11110 707 5.9%

1594

53

April

Days Checked Pcs Ok Pcs Rework Pcs Rework%

1 12060 11316 634 5.3%

2 8919 8382 449 5.0%

4 14590 13516 932 6.4%

5 14822 13810 879 5.9%

6 15704 14829 769 4.9%

7 14743 14101 548 3.7%

8 9090 8438 576 6.3%

9 8899 8131 705 7.9%

10 783 633 125 16.0%

11 11695 10960 629 5.4%

12 12709 11719 836 6.6%

13 9351 8719 547 5.8%

14 7572 7085 391 5.2%

15 10795 10198 496 4.6%

16 10052 9449 522 5.2%

17 7370 7003 299 4.1%

18 11794 11010 669 5.7%

19 11342 10228 1016 9.0%

20 8353 7840 438 5.2%

21 8632 8109 439 5.1%

22 10462 9874 477 4.6%

23 10118 9626 391 3.9%

24 1610 1489 95 5.9%

25 10011 9555 374 3.7%

26 13873 12955 707 5.1%

27 14162 12991 940 6.6%

28 17084 16206 639 3.7%

1595

54

May

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 275269 260256 11366 4%

2 9016 8476 380 4.2%

3 14976 14050 643 4.3%

4 13376 12524 618 4.6%

5 7107 6620 318 4.5%

6 9913 9253 466 4.7%

7 5791 5484 229 4.0%

9 7004 6550 371 5.3%

10 10183 9569 471 4.6%

11 12342 11680 505 4.1%

12 11087 10380 583 5.3%

13 14307 13674 505 3.5%

14 12329 11745 428 3.5%

15 5215 4939 198 3.8%

16 11088 10419 521 4.7%

17 7297 6699 450 6.2%

18 12281 11673 456 3.7%

19 6869 6543 248 3.6%

20 5782 5529 205 3.5%

21 2550 2450 87 3.4%

22 380 351 24 6.3%

23 12406 11854 459 3.7%

24 11727 11055 523 4.5%

25 13144 12491 515 3.9%

26 14863 14262 452 3.0%

27 9161 8698 339 3.7%

29 1691 1581 41 2.4%

30 11236 10529 568 5.1%

31 12327 11707 441 3.6%

1596

55

June

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 11197 10594 441 3.9%

2 12132 11511 485 4.0%

3 8160 7754 327 4.0%

4 12249 11345 758 6.2%

5 4873 4597 245 5.0%

6 10465 9878 479 4.6%

7 5145 4870 213 4.1%

8 6670 6273 313 4.7%

9 7969 7449 402 5.0%

10 8198 7556 464 5.7%

APPENDIX 5

Monthly rejection and rework rate

Months

Custome

r Style

Checke

d Ok

Rejec

t

Rewor

k

Ok

%

Rejec

t %

Rewor

k %

Decembe

r

George

GD

1493 48968 45027 517 3424 92% 1% 7%

H&M

Billie 27744 25411 200 2133 92% 1% 8%

CTN

stretch 35675 32330 451 2894 91% 1% 8%

Lima 24175 21457 295 2423 89% 1% 10%

Ronny 60001 54502 700 4799 91% 1% 8%

NJ

GD

1736 476 451 2 23 95% 0% 5%

GD 10735 9533 178 1024 89% 2% 10%

1597

56

1744

Tekeze

Pack

out 1003 856 38 109 85% 4% 11%

Primark

GD

1609 28300 25896 296 2108 92% 1% 7%

Total 237077

21546

3 2677 18937

91

% 1% 8%

Months

Custome

r Style

Checke

d Ok

Rejec

t

Rewor

k

Ok

%

Rejec

t %

Rewor

k %

January

George

GD

1493 41285 38815 383 2087 94% 1% 5%

H&M

CTN

stretch 18719 17582 120 1017 94% 1% 5%

Lima 27771 24701 289 2781 89% 1% 10%

Ronny 77317 71667 834 4816 93% 1% 6%

NJ

GD

1735 5982 5632 42 308 94% 1% 5%

GD

1736 5067 4834 31 202 95% 1% 4%

Tezeni's

GD

1609 53486 49397 797 3292 92% 1% 6%

Total 229627

21262

8 2496 14503

93

% 1% 6%

Months

Custome

r Style

Checke

d Ok

Rejec

t

Rewor

k

Ok

%

Rejec

t %

Rewor

k %

February George

GD

1493 5130 4738 71 321 92% 1% 6%

H&M Billie 47728 45164 535 2029 95% 1% 4%

1598

57

CTN

stretch 21744 19538 272 1934 90% 1% 9%

Lima 11460 10183 118 1159 89% 1% 10%

Ronny 41959 39580 358 2021 94% 1% 5%

NJ

GD

1811 29761 28562 122 1077 96% 0% 4%

Total 157782

14776

5 1476 8541

94

% 1% 5%

Months

Custome

r Style

Checke

d Ok

Rejec

t

Rewor

k

Ok

%

Rejec

t %

Rewor

k %

March

George

GD

1493 63242 59110 620 3512 93% 1% 6%

H&M

Billie 68342 66125 449 1768 97% 1% 3%

Bob 244 236 0 8 97% 0% 3%

Kicki 8808 7816 47 945 89% 1% 11%

Lima 14026 13171 73 782 94% 1% 6%

Ronny 47770 45620 511 1639 95% 1% 3%

CTN

stretch 28975 26982 311 1682 93% 1% 6%

NJ

GD

1811 54552 51659 250 2643 95% 0% 5%

Be

connect

Bat

man 15750 15048 17 685 96% 0% 4%

Little

pony 5443 5103 14 326 94% 0% 6%

Primark

GD

1609 18889 17476 262 1151 93% 1% 6%

Total 326041

30834

6 2554 15141

95

% 1% 5%

1599

58

Months

Custome

r Style

Checke

d Ok

Rejec

t

Rewor

k

Ok

%

Rejec

t %

Rewor

k %

April

George

GD

1493 16346 15337 146 863 94% 1% 5%

H&M

Billie 32562 31527 264 771 97% 1% 2%

Bob 31159 30224 170 765 97% 1% 2%

Kicki 25360 22593 248 2519 89% 1% 10%

Lima 47480 44850 521 2109 94% 1% 4%

Ronny 46173 43856 549 1768 95% 1% 4%

Kik Ladies 29782 26023 531 3228 87% 2% 11%

NJ

GD

1483 39727 37643 252 1832 95% 1% 5%

Poly

concept

Men's

t-shirt 6117 5476 101 540 90% 2% 9%

Primark

GD

1609 11891 10643 121 1127 90% 1% 9%

Total 286597

26817

2 2903 15522

94

% 1% 5%

Months

Custome

r Style

Checke

d Ok

Rejec

t

Rewor

k

Ok

%

Rejec

t %

Rewor

k %

May

George

GD

1493 42609 39937 384 2288 94% 1% 5%

H&M

Billie 39004 37253 678 1073 96% 2% 3%

Kicki 4607 2786 468 1353 60% 10% 29%

Lima 52222 50079 771 1372 96% 1% 3%

Ronny 39141 37120 613 1408 95% 2% 4%

Kik

Regla

n 28734 26725 367 1642 93% 1% 6%

1600

59

NJ

GD

1811 11232 10748 64 420 96% 1% 4%

GD

1483 6866 6606 14 246 96% 0% 4%

Poly

concept

Men's

t-shirt 10202 9248 198 756 91% 2% 7%

Primark

GD

1609 10831 10283 62 486 95% 1% 4%

Tekeze T-shirt 9821 9471 28 322 96% 0% 3%

Total 255269

24025

6 3647 11366

94

% 1% 4%

APPENDIX 6

Questionnaire format (ቃለመሕተትቕጥዒ)

Personal information :(ናይግሊመረዳእታ)

Name: (ስም)___________________________

Job title :(ናይስራሕመደብ)_____________________________

Sign :(ፌርማ)__________________________________________

Make a tick mark based on the selection defect source (ኣብዝመረፅዎዓይነትችግር (✓) ቲክይግበሩ)

እዚቃለመሕተትእዚንመመርቂፅሑፍጥራሕዝጥቀመሉእምበርንካሊእነገርዝውዕልከምዘይኮነብትሕትናከፍልጠኩምይደሊ፡፡

S/

N

(ተ

.ቁ

List down all

sewing

defects(ኩሎምናይስፌ

ትሪወርክታትዝርዝር)

Machine(ብማሽንም

ክንያትዝመፁችግራት)

Material(

ብጨርቂ

ምክንያትዝ

መፁችግራት

)

Method(ብናይኣስራ

ርሓዝመፁችግራት)

Man(ብናይሰራሕተ

ኛዝመፁችግራት)

1601

60

)

1602

61

APPENDIX 7

Questionnaire format

Personal information________________________________

Name: ___________________________________________

Job title:__________________________________________

Make a tally for all defects.

S/N List down all sewing defects machine material method man

1603

62

APPENDIX 8

Table six month production report

Months Customer Style Checked Ok Reject Rework Ok

%

Reject

%

Rework

%

December George GD

1493

48968 45027 517 3424 92% 1% 7%

H&M Billie 27744 25411 200 2133 92% 1% 8%

CTN

stretch

35675 32330 451 2894 91% 1% 8%

Lima 24175 21457 295 2423 89% 1% 10%

Ronny 60001 54502 700 4799 91% 1% 8%

NJ GD

1736

476 451 2 23 95% 0% 5%

GD

1744

10735 9533 178 1024 89% 2% 10%

Tekeze Pack

out

1003 856 38 109 85% 4% 11%

Primark GD

1609

28300 25896 296 2108 92% 1% 7%

Total 237077 215463 2677 18937 91% 1% 8%

Stain Skip Brocken total Material machine man Method

5176 2405 707

27% 13% 4% 44% 25% 55% 10% 10%

Months Customer Style Checked Ok Reject Rework Ok

%

Reject

%

Rework

%

January George GD 41285 38815 383 2087 94% 1% 5%

1604

63

1493

H&M CTN

stretch

18719 17582 120 1017 94% 1% 5%

Lima 27771 24701 289 2781 89% 1% 10%

Ronny 77317 71667 834 4816 93% 1% 6%

NJ GD

1735

5982 5632 42 308 94% 1% 5%

GD

1736

5067 4834 31 202 95% 1% 4%

Tezeni's GD

1609

53486 49397 797 3292 92% 1% 6%

Total 229627 212628 2496 14503 93% 1% 6%

Stain Skip Brocken total Material machine man method

4433 3226 1168

31% 22% 8% 61% 34% 39% 15% 12%

Months Customer Style Checked Ok Reject Rework Ok

%

Reject

%

Rework

%

February George GD

1493

5130 4738 71 321 92% 1% 6%

H&M Billie 47728 45164 535 2029 95% 1% 4%

CTN

stretch

21744 19538 272 1934 90% 1% 9%

Lima 11460 10183 118 1159 89% 1% 10%

Ronny 41959 39580 358 2021 94% 1% 5%

NJ GD

1811

29761 28562 122 1077 96% 0% 4%

Total 233855 219307 1476 12144 94% 1% 5%

1605

64

Stain Skip Brocken total Material Machine Man Method

3790 3183 983

31% 26% 8% 66% 31% 42% 14% 13%

Months Customer Style Checked Ok Reject Rework Ok

%

Reject

%

Rework

%

March George GD

1493

63242 59110 620 3512 93% 1% 6%

H&M Billie 68342 66125 449 1768 97% 1% 3%

Bob 244 236 0 8 97% 0% 3%

Kicki 8808 7816 47 945 89% 1% 11%

Lima 14026 13171 73 782 94% 1% 6%

Ronny 47770 45620 511 1639 95% 1% 3%

CTN

stretch

28975 26982 311 1682 93% 1% 6%

NJ GD

1811

54552 51659 250 2643 95% 0% 5%

Be

connect

Bat

man

15750 15048 17 685 96% 0% 4%

Little

pony

5443 5103 14 326 94% 0% 6%

Primark GD

1609

18889 17476 262 1151 93% 1% 6%

Total 326041 308346 2554 15141 95% 1% 5%

1606

65

Stain Skip Brocken Total Material Machine Man Method

2285 1704 379

15% 11% 3% 29% 30% 42% 17% 11%

Months Customer Style Checked Ok Reject Rework Ok

%

Reject

%

Rework

%

April George GD

1493

16346 15337 146 863 94% 1% 5%

H&M Billie 32562 31527 264 771 97% 1% 2%

Bob 31159 30224 170 765 97% 1% 2%

Kicki 25360 22593 248 2519 89% 1% 10%

Lima 47480 44850 521 2109 94% 1% 4%

Ronny 46173 43856 549 1768 95% 1% 4%

Kik Ladies 29782 26023 531 3228 87% 2% 11%

NJ GD

1483

39727 37643 252 1832 95% 1% 5%

Poly

concept

Men's

t-shirt

6117 5476 101 540 90% 2% 9%

Primark GD

1609

11891 10643 121 1127 90% 1% 9%

Total 286597 268172 2903 15522 94% 1% 5%

Stain Skip Brocken Material Machine Man Method

4961 3687 734

32% 24% 5% 60% 36% 44% 8% 12%

1607

66

Months Customer Style Checked Ok Reject Rework Ok

%

Reject

%

Rework

%

May George GD

1493

42609 39937 384 2288 94% 1% 5%

H&M Billie 39004 37253 678 1073 96% 2% 3%

Kicki 4607 2786 468 1353 60% 10% 29%

Lima 52222 50079 771 1372 96% 1% 3%

Ronny 39141 37120 613 1408 95% 2% 4%

Kik Reglan 28734 26725 367 1642 93% 1% 6%

NJ GD

1811

11232 10748 64 420 96% 1% 4%

GD

1483

6866 6606 14 246 96% 0% 4%

Poly

concept

Men's

t-shirt

10202 9248 198 756 91% 2% 7%

Primark GD

1609

10831 10283 62 486 95% 1% 4%

Tekeze T-shirt 9821 9471 28 322 96% 0% 3%

Total 255269 240256 3647 11366 94% 1% 4%

Stain Skip Brocken Material Machine Man Method

1194 3674 799

11% 32% 7% 50% 19% 63% 7% 11%

1608

67

APPENDIX 9

Participants at the Brainstorming Session

Table: Categorizing the defect types based on 4M

Quality

Manager

Assistant manager

Quality officer

5 Section head

Production

Production manager

Assistant manager

4 section head

Maintenance

Manager

Assistant manager

Industrial engineering Planning department

IE officer

Kaizen officer System manager

(Questioner)

S/N Machine Material Method Man

Stain 25 21 20 20

Skip stitch 25 6 8 11

Broken 17 3 12 20

1609

68

APPENDIX 10

Table: Defect location indicator

Defect category Defect location

Stain Back and front

Skip stitch Bottom and cuff hem

Broken Bottom and cuff hem

Appendix 11

Forpareto six month data

Skip stitch causes

Table: Machine causes for skip stitch

Total number of defects 65049

Machine defects 32524

Machine Amount Amount

cumulative.

Cumulative.%

Looper to needle timing 5004 5004 15%

Needle bent or dull 4587 9591 29%

Skill problem 4170 13760 42%

Long time usage 3752 17512 54%

Machine setting 3336 20848 64%

Pre adjustment 2919 23767 73%

Improper feeding 2502 26269 81%

Needle insertion 2085 28354 87%

Worn defective thread guides or

eyelets

1668 30022 92%

Spare part shortage 1251 31273 96%

Wear out of looper 834 32107 99%

Improper threading 417 32524 100%

1610

69

Total number of defects 65049

Material defects 16262

Material Amount Amount cumulative. Cumulative%

Quality of the thread 5421 5421 33%

Types of thread 4337 9757 60%

Fabric type 3252 13010 80%

Thick or thin 2168 15178 93%

Neaps on the thread 1084 16262 100%

Total number of

defects

65049

Method defects 9757

Method Amount Amount cumulative. cumulative%

Machine adjustment 2788 2788 29%

Types of operation 2323 5111 52%

Operator skill 1859 6970 71%

Tension adjustments 1394 8363 86%

Improper feeding 929 9293 95%

Improper threading 465 9757 100%

Total number

of defects

65049

Man causes 6505

Man power Amount Amount

cumulative

cumulative %

increase in 3252 3252 50%

1611

70

speed

Wrong setting 2168 5421 83%

Fatigue 1084 6505 100%

Problem

causes

Amount Amount

cumulative

Cumulative %

Machine 32525 32525 50%

Material 16262 48787 75%

Method 9757 58544 90%

Man 6505 65049 100%

Total 65049

Stain causes

Total number of defects 83841

Machin defects 20960

Machine Amount Amount

cumulative

cumulative %

Un cleaned machines 6987 6987 33%

Machine break down 5589 12576 60%

Lack of scheduled

maintenance

4192 16768 80%

Machine type 2795 19563 93%

Numbering machines 1397 20960 100%

1612

71

Total number of defects 83841

Material causes 20960

Material Amount Amount

cumulative

cumulative %

Material handling 10480 10480 50%

transportation 6987 17467 83%

Material type 3493 20960 100%

20960

Total number of

defects

83841

Method defects 20960

Method Amount Amount

cumulative

cumulative %

Lack of sop 5240 5240 25%

transportation 4491 9732 46%

Poor handling 3743 13474 64%

Lack of training 2994 16469 79%

Awareness 2246 18715 89%

standardization 1497 20212 96%

Work place 749 20960 100%

1613

72

Total number of defects 83841

Man defects 20960

Man power Amount Amount

cumulative

Cumulative %

Skill of operation 5989 5989 29%

Lack of personal

hygiene

4991 10979 52%

Lack of training 3992 14972 71%

Lack of awareness 2994 17966 86%

Lack of ownership 1996 19962 95%

Not satisfied 998 20960 100%

Types of

defect

Amount Amount

cumulative

Cumulative %

Machine 20960 20960 25%

Material 20960 41921 50%

Method 20960 62881 75%

Man 20960 83841 100%

Total number

of defects

83841

1614

73

APPENDIX 12

Why –Why analysis for skip stitch

Potential causes for Skip stitch

category Why? Why? Why? Why? Why?

Machine

Improper

threading

Not

knowing

the

threading

system

Un skilled

operator

Lack of

awareness of

not knowing

the effect

Lack of

training

Spare

part

shortage

Suffering

from

higher

cost

Company

ability

Wrong

setting

Un

skilled

Operator

Technical

training

Work load Company

strategy

High

cost

carelessn

ess

Not having a

trainer

Training cost

Suffering

from hiring

employee

Improper

machine

set up

Skill gap

Awarene

ss

problem

Long

time

needle

usage

High cost

of needle

For

minimizin

g cost

Competitive

advantage

Need for High

profit

To

achieve

the

settled

target

Needle carelessne High needle The Company

1615

74

bent

Improper

needle

adjustme

nt

ss time usage considering it

as culture of

using needle

throughout its

life time

High

cost

and

Compa

ny

ability

Low

skilled

Lack of

technical

training

Work load The

strategi

c plan

of the

compan

y not

focusin

g on

mainten

ance

and not

providi

ng

expert

who

will

train

Skill

problem

Lack of

training

trainer Not skilled Not trained Compa

ny

strategy

not

focusin

g on

training

and

1616

75

lack of

experie

nce

sharing

trained Low ability

in receiving

training

personality Nature

of the

person

or not

concent

rating

Dissatisfactio

n by work

Low

salary

Dull

needle

No

preparati

on

Awarenes

s problem

and no

self

checking

Selection

of needle

versus

fabric

type

Skill gap

Mis

aligned

during

fixing

Skill gap

Awareness

problem

Life time There is no

proper

recording

1617

76

No

consisten

t

schedule

maintena

nce

Rush

productio

n

Poor

company

wise

planning

Less

feeding

Poor

follow up

and

giving

feedback

Wear out

machine

parts

Due to

preventiv

e

maintena

nce

Awarenes

s problem

Delay in

supply

Supply

chain

system

Late request

Financial

problem

Delay in

custom

process

All items not

locally

available

Life time

man Speed

increase

For

finishing

fastly

Not

knowing

the effect

Awareness

problem

Lack of

continuous

training

Trained

only

when

they are

hired

1618

77

Dissatisfa

ction by

the setted

speed

Over

confidence

High

experience

Fatigue

Work

load

Operation

system

complex

Not using

the latest

way of

working

Due High

rework

and

rejection

rate

Long time

working

Planning

problem ,hig

h order

Not assuming

the down time

To achieve

the setted

target or to

fulfill the

delivery date

Customer

satisfaction

Loyal

custom

er and

build

compan

y image

and to

increase

the

profit

of the

compan

y

For not

incurrin

1619

78

g

unneces

sary

expense

s

Skill gap Lack of

continuo

us

training

Handling

problem

Carless

attention

for work

Lack of

corrective

action

training

Disciplinary

action

material

Fabric

type

Low

GSM

The

nature of

the fabric

type

Poor

thread

quality

There is

no

Thread

quality

tester

Carelessn

ess of the

managem

ent

Not

knowing the

effect

For

minimizing

the cost

Defects

passing

from

cutting

Checker

negligenc

e and

awarenes

s

Follow up

problem

Invisible

defects

Back side

error

Method More

WIP

Poor

follow up

1620

79

Line

color

Wise

plan

Input not

required

as

planned

Lack of

SOP

Skill gap

and lack

of

experienc

e

No

consistent

training

and

follow up

1621

80

APPENDIX 13

Why-Why analysis for stain

Potential causes for Stain

Category Why? Why? Why? Why? Why? Why?

Machine Stain due to

numbering

machines

Fabric dust The

chemical

that came

from dying

for

avoiding

the neaps

The

nature of

the

process

Uncleaned

machine

No

autonomou

s

maintenanc

e

High

work load

Wrong

target

setting

Wrong

time

study

carelessne

ss

There is no

continuous

cleaning

There is no

regular

schedule

Numbering

machine

problem

Due to the

machine

design

Problem of

ink &

sticker

Poor

quality of

material

purchased

Method Lack of SOP the ability

of the

company

The

potential or

knowledge

of the

workers on

Lack of

training

Lack of

expert

1622

81

preparing

SOP

transportation Old

transportati

on

materials

Large

operation

Gap

between

department

to

department

Layout

problem

Not

planning

Nature

of the

work

Poor handling Carelessnes

s

Working

environmen

t

Poor

infrastructu

re

Not

planned

Lack of

knowled

ge

awarene

ss

standardizatio

n

No

sustainabilit

y

Work load Need for

high

demand

from

departmen

t to

departmen

t

For not

being

asked

Not

accepting

Not

thinking

as it’s the

best way

Not

showing

the

advantag

e

Oil stain due to

cutting machines

Improper

cleaning

Low

motivation

Not

identifyin

1623

82

after

corrective

maintenanc

e

while

cleaning

g impact

on

resource

Due to

lack of

hard

currency

No

scheduled

preventive

maintenanc

e

Lack of

spare parts

Delay in

purchasin

g process

Due to

negligen

ce

Due to

out of

controlli

ng (bank,

custom)

Delay in

request

No

follow

up on

stock

controlli

ng

Man Employee not

satisfied

Less salary

and

incentive

High

income

need

High

profit

Material Defects from fabric Passed due

to 4-point

system

Inspector

error

Skill gap

and

1624

83

negligence

Intentionall

y to meet

delivery

time

Defects due to

transportation

Poor

handling

Lack of

awareness

on final

impact

Damaged

trolley

Lack of

preventive

maintenanc

e

Lack of

spare

parts

No pallet or

shelf to

keep the

fabric

There is no

demand

still now

No trolley

or moving

tray in

cutting

There is no

demand

still now

Defects due to

packing materials

It is not

properly

covered

Covering

material is

not a like a

bag

There is

no

demand

still now

1625

84

APPENDIX 14

Table: December month Defect rate

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 263101 239710 20319 8%

2 6165 5654 428 6.9%

3 8934 7996 817 9.1%

4 10110 9088 895 8.9%

5 7322 6522 721 9.8%

7 8811 7943 785 8.9%

8 11178 9886 1156 10.3%

9 10303 9185 963 9.3%

10 11448 10218 1060 9.3%

11 8189 7213 886 10.8%

12 7395 6547 784 10.6%

14 8229 7388 737 9.0%

15 14275 12940 1189 8.3%

16 14437 13271 1036 7.2%

17 12924 11912 871 6.7%

18 14229 13245 848 6.0%

19 7560 6924 544 7.2%

21 10527 9670 744 7.1%

22 6860 6270 525 7.7%

24 2138 1997 101 4.7%

25 6516 6025 415 6.4%

26 4523 4122 347 7.7%

28 7880 7151 632 8.0%

29 8572 8036 474 5.5%

30 8835 8151 565 6.4%

31 10595 9887 596 5.6%

1626

85

Figure P-chart for December month

Due to 3 points (4, 24, 28) fail out of control the process is not stable due to the operator

carelessness, machine break down, needle breakage.

New revised P chart

Figure Revised-1: P-chart for December month

1627

86

Figure Revised-2: P-chart for December month

Figure Final P-Chart for December Month

1628

87

Table January Month Defect Rate

Days Checked Pcs Ok Pcs Rework Pcs Rework%

1 7567 7065 436 5.8%

2 2737 2543 167 6.1%

4 7477 6967 436 5.8%

5 8721 8179 461 5.3%

6 5736 5345 326 5.7%

8 9559 8706 724 7.6%

9 6949 6361 471 6.8%

11 9777 8925 712 7.3%

12 13792 12836 839 6.1%

13 13418 12550 766 5.7%

14 13587 12524 950 7.0%

15 12167 10989 994 8.2%

16 8813 7943 692 7.9%

18 11479 10798 573 5.0%

19 11988 11146 751 6.3%

21 11386 10573 712 6.3%

22 10475 9952 668 6.2%

23 4469 4089 315 7.0%

25 10048 9153 759 7.6%

26 12356 11467 713 5.8%

28 13052 12265 702 5.4%

29 14306 13376 802 5.6%

30 9822 9187 542 5.5%

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Figure P-chart for January month

Figure. Revised-1: P-chart for January month

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Figure Final: P-chart for January month

Table: February month Defect rate

Days Checked Pcs Ok Rework Pcs Rework%

1 171949 161364 8953 5%

2 14136 13133 860 6.1%

3 9539 8917 543 5.7%

4 7768 7270 432 5.6%

5 8098 7596 433 5.3%

6 3017 2816 173 5.7%

8 191 159 28 14.7%

9 8977 8433 459 5.1%

10 6659 6127 451 6.8%

11 673 620 45 6.7%

12 85 82 3 3.5%

13 1532 1470 54 12.8%

15 1076 986 68 6.3%

16 2793 2554 202 7.2%

17 2452 2263 151 6.2%

18 4707 4376 277 5.9%

19 18131 17464 558 3.1%

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20 10180 9640 470 4.6%

21 8037 7608 368 4.6%

22 14691 13738 767 5.2%

23 7264 6754 415 5.7%

24 2833 2668 128 4.5%

25 11896 10886 876 7.4%

26 11327 10495 708 6.3%

27 8850 8305 479 5.4%

28 8136 7588 480 5.9%

29 18780 17762 841 4.5%

Figure. P-chart for February month

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Figure Revised-1: P-chart for February month

Figure Final: P-chart for February month

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Table: March Month Defect Rate

Days Checked Pcs Ok Pcs Rework Pcs Rework %

1 6250 5803 347 5.6%

2 5509 5143 330 6.0%

3 13985 13118 750 5.4%

4 14936 14022 738 4.9%

5 8890 8296 511 5.7%

6 8030 7516 438 5.5%

7 12797 12167 530 4.1%

8 12332 11704 551 4.5%

9 14300 13690 531 3.7%

10 10407 9892 431 4.1%

11 9819 9251 490 5.0%

12 6583 6203 321 4.9%

14 4951 4675 236 4.8%

15 10532 101277 343 3.3%

16 15080 14516 488 3.2%

17 15719 15112 521 3.3%

18 15100 14491 535 3.5%

19 12007 11533 400 3.3%

21 19031 18233 694 3.6%

22 18361 17410 849 4.6%

23 15395 15097 0 0%

24 11532 10847 557 4.8%

25 7540 7093 378 5.0%

26 9566 8888 579 6.1%

28 8455 7613 745 8.8%

29 11950 11163 682 5.7%

30 13634 12719 782 5.7%

31 11943 11110 707 5.9%

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Figure-chart for March month

Figure Revised-1: P-chart for March month

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Figure Final P-chart for March month

Table:April Month Defect Rate

Days Checked Pcs Ok Pcs Rework Pcs Rework%

1 12060 11316 634 5.3%

2 8919 8382 449 5.0%

4 14590 13516 932 6.4%

5 14822 13810 879 5.9%

6 15704 14829 769 4.9%

7 14743 14101 548 3.7%

8 9090 8438 576 6.3%

9 8899 8131 705 7.9%

10 783 633 125 16.0%

11 11695 10960 629 5.4%

12 12709 11719 836 6.6%

13 9351 8719 547 5.8%

14 7572 7085 391 5.2%

15 10795 10198 496 4.6%

16 10052 9449 522 5.2%

17 7370 7003 299 4.1%

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18 11794 11010 669 5.7%

19 11342 10228 1016 9.0%

20 8353 7840 438 5.2%

21 8632 8109 439 5.1%

22 10462 9874 477 4.6%

23 10118 9626 391 3.9%

24 1610 1489 95 5.9%

25 10011 9555 374 3.7%

26 13873 12955 707 5.1%

27 14162 12991 940 6.6%

28 17084 16206 639 3.7%

Figure P chart of rework for April month

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Figure: Revised-1: P chart of rework for April month

Points 12 fail out of control

Figure: Revised-1:P chart of rework for April month

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Figure: Final P chart of rework for April mont

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1640