-A STUDY ON

“Minimizing rework to improve quality of the garment on

production floor. ”

PROJECT REPORT
Submitted in partial fulfillment of the Requirement for the degree of
Masters of Fashion Technology
(Apparel Production)

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To the
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National Institute of Fashion Technology
Chennai
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Submitted By
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Veena Jangra
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MFT/15/118
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Under the Guidance

Of
Dr. Divya Satyan
••‘ ‹ƒ–‡†’”‘ˆ‡••‘”

ABSTRACT

For any industry the production and quality management or wastages reductions
have major impingement on overall factory economy. This work discusses the
quality improvement of garment industry by applying tools such as checklist, cause
and effect diagram and control charts. 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
by using various quality tools, these tools are introduced and implemented in the
Company. Providing framework to identify, quantify and eliminate defect sources
by which the defects are determined. Along with that the corrective actions are
performed, and the defective percentage is compared before and after. The study

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also improves the process performance of the critical operational processes. The

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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.
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Key words: Reworks, Checklist, Data analysis, Cause and effect diagram, Control
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chart, DHU (Defect per hundred units).
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 CERTIFICATE

“This is to certify that this Project Report titled “Minimizing rework to improve
quality of the garment on production floor.” is based on our, Veena Jangra original
research work, conducted under the guidance of Dr. Divya Satyan Associated
professor, NIFT, Chennai towards partial fulfilment of the requirement for award
of the Master’s Degree in Fashion Technology (Apparel Production), of the
National Institute of Fashion Technology, Chennai .

No part of this work has been copied from any other source. Material, wherever
borrowed has been duly acknowledged. ”

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Signature of Researcher 1
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Signature of Guide 1
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 ACKNOWLEDGMENT

First and foremost, I would like to express my deepest appreciation to our faculty
mentor, Dr Divya Satyan , Associated Professor for the guidance and invaluable
insights provided by them throughout the duration of the project.
I would like to acknowledge our institute, National Institute of Fashion Technology,
Chennai for providing us opportunities to learn and grow in a professional and a

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personal manner.

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We would also like to express our appreciation to our industry mentor, Mr. S.S.
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Rajkumar, GM, Garment Operations, who made this project possible by providing
us an opportunity to work under him. In addition,I would like to thank Mr. Ravindra
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Jaiswal, Plant Head, SEZ unit, for constantly supporting us and motivating us to do
better at every step.
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Many thanks also go to the full time staffs that are involved in the operations of the
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production floor of the organisation such as the industrial engineers, production

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planners, quality engineers, supervisors, etc. for their support in completing this
project.

Above all, I would like to thank my families who supported me throughout the
duration of this project and allowed us to focus our attention on completing this
research.

Last but not least, I would like to thank my friends for working together and helping
each other during the duration of this project report.
 LIST OF TABLES

Table no Page no
1 defects of shift A of 6 blocks
2 defects from 8 feb 2017 to 17 feb 2017 of 2 blocks
3 top 5 defects

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4 why analysis

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5 percentage of top defects in February
6 percentage of top defects in march
7 percentage of top defects in April
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LIST OF FIGURES
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Figure no Name Page no

1 checklist
2 Pareto analysis of defect January
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3 Pareto analysis of defect February

4 Cause and effect diagram of spot
5 Cause and effect diagram of skip stitch
6 Cause and effect diagram of broken stitch
7 Cause and effect diagram of uncut thread
8 Cause and effect diagram of joint out
9 defective percentage of February
10 pie chart of defects of February
11 defective percentage of march
12 pie chart of defects of march
13 defective percentage of April
14 pie chart of defects of April
 15 bar graph of five defects.

CONTENTS

1. INTRODUCTION
1.1 Objective
1.1.1 Sub Objectives

2. REVIEW OF LITERATURE

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3. METHODOLOGY

3.1 Swot Analysis

3.2 Checklist
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3.3 Data analysis
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3.4 Pareto Chart

3.5. Cause and effect diagram

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4. RESULT AND ANALYSIS

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4.1 Checklist
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4.2 Data Analysis

4.2.1. Collection of DHU percentage of 2 blocks (1 block equal to 6

lines)

4.3 TOP DEFECTS

4.4 CAUSE AND EFFECT DIAGRAM

4.5 DHU

4.5.1 Dhu of February month

 4.5.2 Dhu of March month

4.5.3 Dhu of April Month

5. FINDING

6. CONCLUSION

7. SCOPE

8. REFERENCE

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 1. INTRODUCTION
The change seen in global economic condition is rapid, generally in an industry more focus is
given on profit margin, customer demand for high quality product and improved productivity. In
garment manufacturing, it is usual few rejected garments after shipment. Reason, most of the
manufacturers believe that garments are soft goods and non-repairable defect may occur due to
low quality raw materials or faulty process or employee casual behavior.
However, factory must have check points to control over this issue. There is no ready-made
solution that can reduce rejection percentage overnight. Each order is unique. We see a lot of
rejected garment after shipment. Most of the organization termed these garments as rejected
because those garments can’t be repaired by any means. Reworks in the garments industry is a
common works that hampers the smooth production rate and focus poor quality products having
an impact on overall factory economy.

Minimization of reworks is a must in quality and productivity improvement. Rework is a vital

issue for poor quality product and low production rate. Reworks are the non -productive

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activities focusing on any activity that customer are not willing to pay for. Non-productive
activities describe that the customer does not consider as adding value to his product. When the
study was taken place initial defect rate was counted as high as 53.84% By reacting quicker in
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minimization of reworks to make a product as per customer demand with expected quality, the
company can invest less money and more costs savings .So to reduce these re-works and increase
productivity of garment a data collection of re-works needs to be done by which a complete
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understanding of exact amount of non-productive work happening. Studying about data and
analyzing its cause effects may lead to the future control of defects.
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1.1 OBJECTIVE
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“Minimizing rework to improve quality of the garment on production floor.”

1. 1.1 Sub Objectives:

1. Review of the existing rework percentage on the production floor.

2. Identification of defects for rework in the sewing department.
3. Analysis of data collected in order to identify majorly occurring defects which lead to
rework .
4. Categorization of defects in rework .

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2. REVIEW OF LITERATURE
Case study- 1

One of the key processes performed in organizations is to minimize the re-works and increase
productivity of the sewing line. This review will include the process of reducing the re-works
oralteration happening in the various departments of garment industry. After that, some of the
popular methodologies and approaches are discussed. The current literature published by Md.
Mazedul Islam, Adnan Maroof Khan Md.MashiurRahman Khan, January 2013 gives great
emphasis to the importance of decreasing the amount of rework, which meets the overall
productivity of the industry. Generally in an industry more focus is given on profit margin,
customer demand for high quality product and improved productivity. In garment manufacturing,
it is usual few rejected garments after shipment. However, factory must have check points to

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control over this issue. There is no ready-made solution that can reduce rejection percentage
overnight. In this review works suggest how to handle this issue and bring down rejection rate to
minimum. We see a lot of rejected garment after shipment. Most of the organization termed
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these garments as rejected because those garments can’t be repaired by any means. Reworks in
the garments industry is a common works that hampers the smooth production rate and focus
poor quality products having an impact on overall factory economy. Minimization of reworks is
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a must in quality and productivity improvement. Rework is a vital issue for poor quality product
and low production rate.. Non-productive activities like this describe that the customer does not
consider as adding value to his product. By reacting quicker in minimization of reworks to make
a product as per customer demand with expected quality, the company can invest less money and
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more costs savings.

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Case study-2
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TanvirAhamad this paper presents on use of Pareto chart and cause and effect diagram in
analyzing the defect caused in garment industry. This papers aims at reducing the defect rate
caused while stitching clothes. Using these methods it was identified about 80% defect rate in
process of stitching. The top five defects was identified and analyzed. Using cause and effect
diagram causes and effect are constructed. The study provided suggestion to reduce the defect
rate. Thus this papers gives idea of how effectively minimizing the rework and defect rate.

Md .MAZIDHUL IBRAM this paper highlights on use of quality tool in minimizing the rework
in apparel industry. This paper gives idea of quality and productivity improvement in apparel
industry. The methods helps to provide the framework in identify the defect and analyze. It helps
to reduce the defect rate. This paper gives the idea of application of process performance of
critical process which leads to proper utilization of machines and time. The paper aims at
improve the productivity by minimizing cost and internal throughput.

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Case study-3

The current International Research Journal published by Varun, S.Appaiah,

Chethankumar.C.S says that for any industry the production and quality management or
wastages reductions have major impingement on overall factory economy. This work discusses
the quality improvement of garment industry by applying Pareto analysis and cause and effect
diagram. 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 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.

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Case study -4 en
As Readymade Garments sector is a large industrial sector in Bangladesh, quality improvement
can play a vital role for improving productivity as well as economic development for the country.
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This review published by Tanvir Ahmed, Raj Narayan Acharjee, MD.Abdur Rahim,
Noman Sikder, Taslima Akther, Mohd. Rifat Khan, MD.Fazle Rabbi3, Anup Saha - 2013
represents a detail investigation on quality improvement of a garment factory by applying:

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Cause-Effect Diagram.
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The aim of this study is to minimize defects that will reduce rework and rejection rate .At present
the success of the Readymade Garments sector highly depends on several factors such as
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manufacturing lead time, quality of product, production cost etc. These factors are hampered due
to various defects in the products. These defects can be repairable that leads to rework or non-
repairable that leads to rejection. Rework in the garments industry is a common work that
hampers the smooth production rate and focus poor quality products having an impact on overall
factory economy. Minimization of reworks is a must in quality and productivity improvement.
Rework is a vital issue for poor quality product and low production rate. Reworks are the non-
productive activities focusing on any activity that customer are not willing to pay for. Non-
productive activities describe that the customer does not consider as adding value to his product.
An application of pareto analysis and cause effect diagram for minimizing defect percentage in
sewing section. By reacting quicker in minimization of reworks to make a product as per
customer demand with expected quality, the company can invest less money and more costs
savings. Whereas rejection causes waste and deceases resource efficiency. In this review four

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months defect data has been collected from the management and Pareto Analysis is performed on
them.

Case study -5

Pratima Mishra and Rajiv Kumar Sharma in their study proposed a hybrid framework

(suppliers, inputs, process, output and customers define measure, analyze, improve and

control (SIPOC+DMAIC)) aimed to improving supply chain management (SCM) process

dimensions in a supply chain (SC) network. Although process dimensions related to SCM are

critical to organization competitiveness, research so far has tended to focus on supply chain

operations and reference model, balanced scorecard, total quality management, activity-based

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costing, just in time, etc., but in literature hardly any description of the SIPOCDMAIC model

to improve SCM process performance is provided. The use of statistics in DMAIC provides
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better insight into the process performance, and process control. Based upon the critical

review of literature, process dimensions (average outgoing quality limit (AOQL), average 17
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outgoing quality (AOQ), process Z, defect per million opportunity) critical to SCM performance
were identified.

A framework consisting of three phases, i.e., design, implementation and results has been
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conceptualized and a case from paint industry is investigated. Implementation framework

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makes use of SIPOC model and Six Sigma DMAIC methodology. It was observed from the
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results that selection of appropriate strategies for improving process performance based upon

experiences and use of statistical tools by cross functional teams with an effective

coordination, guarantees success. Metrics such as AOQL shows the maximum worst possible

defective or defect rate for the AOQ. Process Z helps to know about sigma capability of the

process.

Chethan Kumar. C.S, N.V.R Naidu, K.Ravindranath advocated the significance of

implementing DMAIC methodology to control defects in garments industry.

Chethan Kumar .C.S, N.V.R Naidu, K.Ravindranath illustrated the importance of using

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the lean principles to eliminate non value added wastes in garment industry.

Ploytip Jirasukprasert used six sigma and DMAIC application for the reduction of defects

in a manufacturing process.

The review provides the guidelines for the betterment and control of wastes in garment

industry for shorts and pants by using six sigma methodology. By DMAIC methodology

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 3.38.18

DHU (defects per hundred units)

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In garment production processes, quality checking stations are set for stopping defects at the

source and stopping defective garment passing to the following processes. Normally,
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checking is done for raw materials, partially stitched garments, stitched garments and finished

garments by quality checkers. In checking, quality checker detects defects in garments and
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separate defective garments from good pieces. Where there is established quality system,

quality checker records total number of defects found in the garments checked by her/him in
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a day and also she/he records the number of defective garments where those defects are
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found. Quality checking records are summarized and result is presented in DHU.
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DHU value is calculated using following formula.

Illustration: Assume that a finishing checker checked 250 garments in a day. Checker found

20 defective garments and he recorded 35 defects in those defective pieces. What is

the DHU of finishing process?

Solution: By using above formula DHU of the finishing goods is

=35*100/250

= 1419

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Tools:

The defects which are occurred at the production process in the apparel factory, affect quality

of the product and productivity of the factory and also they increase the cost of production.

For quality improvement and decreasing the cost of production, it is necessary to avoid poor

quality. In this research, it is examined to decrease the sewing defects by using quality

control tools in the apparel factory.

Checklist

The frequencies of sewing defects in the operations at the sewing department are determined

by check list.

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Data analysis

The operations which have highest sewing defect rates and the effects of these operations to
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the defect rate are examined by using the data analysis.

Cause and effect diagram

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

has the highest sewing defect rate.

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3. METHODOLOGY

3.1 SWOT Analysis

Strength:

 Higher authority in the industry welcomes new system.

 Meetings are arranged, when new methods are formulated with the
concerned people in the line.
 Given authority to complain to the higher management who doesn’t
cooperate.

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Weakness:

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 Attitude of the workers.
 Negligence towards new system. en
 Leadership of supervisor weak.

Opportunity:
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 To reduce the re-works in the line.

 Increase line efficiency.
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Threat:
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 Reluctant of operators and line supervisor for new format Operator causing defect.
 Immediately threaten to leave job if anything against their comfort zone taken place.

In next step, checklist was prepare to collect data.

3.2 Checklist
Following are the steps which were followed to collect data:

To study the defect data

 Collecting defect data for the line to understand the defect rate.

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  Collect data for other line.
 Analyze the data and suggest solutions for the defect that has high rate.

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 FIGURE 1: checklist

Observing and analyzing data:

 Data is analyzed in graph format for styles that was worked on

 Data analysis consist the percentage of defects produced by each style and their
percentage defect
 Comparing the initial defect rate and final defect rate

3.3 Data analysis

Data collected are then transformed into graph as per styles and their rate of defects

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 Data are also divided into their garment categories which specify major positions of
defects in garment.
 Defect positions also give the zones in which defect falls which stated which are more
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visible defects
 Late, DHU is grouped as per the highest defect rate to lowest defect rate
 Brainstorm all the possible causes of the problem. Causes can be written in several places
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if they relate to several categories.
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3.4 PARETO CHART

Pareto analysis is a formal technique useful where many possible courses of action are
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competing for attention. In essence, the problem-solver estimates the benefit delivered by each
action, then selects a number of the most effective actions that deliver a total benefit reasonably
close to the maximal possible one.

Pareto charts can be used in several different ways, including:

1. To analyze the frequency of problems or defects in a process

2. To analyze broad causes by examining their individual components
3. To help focus efforts on the most significant problems or causes when there are many
4. To help communicate the significance of problems or causes to others

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3.5. Cause and effect diagram.

 When the defects are known, DHU of more than 1 are stated in cause and effect Diagram.
 Brainstorm the major categories of causes of the problem. If this is difficult use generic
headings:
1. Methods
2. Machines
3. Man
4. Materials
 Write the categories of causes as branches from the main arrow.
 Brainstorm all the possible causes of the problem. Causes can be written in several places
if they relate to several categories.

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4 RESULTS AND ANALYSIS
4.1 Checklist
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Frequency of the sewing defect in the operations at sewing department.

With the help of checklist it was easy for me to find out the defects caused by each operator. This
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helped me to find out the main reason for the particular defect. This checklist contains hourly
based output and hourly defects rate. The data has been taken from two production block of
sewing section. It is very easy to determine the defect occurring from each operator. This
checklist is signed by supervisor in every hour. Supervisor has to continuously check every hour.
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If too many defects are coming from a particular operator or machine supervisor has to check
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and correct it so that this will not continue for other pieces.
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4.2. Data analysis

I have taken January month DHU data (table no:1) from company record. This data shows the
most common defects which are occurring on production floor. This data comprises of all six
blocks of the production floor. Each block has six lines each. According to this data I can
analyze top 5 defects which are responsible for the rework of the production floor.

This is company data regarding defects of shift A of 6 blocks

PARETO ANALYSIS TABLE

Defects total cumulative cumulative & efficiency

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 Spot 12909 12909 21.9060225 21.906023
uncut thread 12637 25546 43.3504726 21.44445
skip stitch 6308 31854 54.0548796 10.704407
joint out 5330 37184 64.50739899 9.0447827
broken stitch 4620 41804 72.13182642 7.839943
mending 4377 46181 79.35288761 7.4275823
Uneven 2847 49028 83.90607886 4.8312376
unbalancing 2055 51083 87.13072252 3.4872474
puckering 1786 52869 90.0005107 3.0307658
open seam 1407 54276 92.24807519 2.387619
Pleat 1038 55314 93.96116802 1.7614417
raw edges 829 56143 95.31756676 1.4067776
bottom wavy 760 56903 96.5750751 1.2896876
wrong label 386 57289 97.21698994 0.6550255

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label centre out 354 57643 97.81771284 0.6007229

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H.S damage 312 57955 98.34716354 0.5294507
needle mark 242 58197 98.75782722 0.4106637
thread tension 235 58432 99.15661219
en 0.398785
shading 196 58628 99.48921584 0.3326036
bar tack 115 58743 99.68436593 0.1951501
Hole 94 58837 99.84387992 0.159514
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label missing 32 58869 99.89818256 0.0543026
placement 32 58901 99.95248519 0.0543026
buttoning 20 58921 99.98642434 0.0339391
measurement 8 58929 100 0.0135757
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print cracking 0 58929 100 0

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mis print 0 58929 100 0

total defects 58929
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total check pcs. 717927

DHU % 8.208216
Table no:1

Pareto analysis of defects

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 14000 100
12000 90
80
10000 70
8000 60
50
6000 40
4000 30 total
20
2000 10 cumulitive &
0 0

H.S damage
broken stitch
mending

hole
Spot

wrong label

bartack

measurement
label centre out

shading

print cracking
mis print
thread tension
uncut thread
skip stich

puckering

label missing
uneven

open seam
pleat

bottom wavy

nedle mark

buttoning
unbalancing

raw edges

placement
joint out

Figure 2 Pareto analysis of defect

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4.2.1. Collection of DHU percentage of 2 blocks (1 block equal to 6 lines)
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Study of defects from 8 feb 2017 to 17 feb 2017 of 2 blocks

PARETO ANALYSIS TABLE

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defects total cumulative cumulative & efficiency
Spot 1157 1157 29.24671385 29.246714
uncut 815 1972 49.84833165 20.601618
skip stich 462 2434 61.52679474 11.678463
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joint out 317 2751 69.53993933 8.0131446

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label centre out 243 2994 75.68250758 6.1425683

pleat 172 3166 80.03033367 4.3478261
up down 147 3313 83.74620829 3.7158746
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hole 133 3446 87.10819009 3.3619818

broken stitch 132 3578 90.44489383 3.3367037
puckering 124 3702 93.5793731 3.1344793
cut stitch 112 3814 96.41051567 2.8311426
measurement 69 3883 98.15470172 1.744186
open seam 60 3943 99.67138524 1.5166835
oil stain 13 3956 100 0.3286148
uneven 0 3956 100 0
total defects 3956
total check pcs 74100
DHU % 5.33%
Table no :2

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This data (table no:2) I have collected for my project work. This shows the DHU percentage of
only two blocks. This I have collected to find out the top defects on the production. This data is
just a comparison with the company data.

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Pareto analysis of defects en
14000 100
12000 90
80
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10000 70
8000 60
50
6000 40
4000 30 total
20
2000
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10 cumulitive &
0 0
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label missing
skip stich

H.S damage
broken stitch
mending

wrong label

hole
bartack
Spot

label centre out

shading

measurement
print cracking
mis print
thread tension
uncut thread

puckering
uneven
joint out

open seam
pleat

bottom wavy

nedle mark

buttoning
raw edges

placement
unbalancing
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Figure 3 Pareto analysis of defects

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4.3 TOP 5 DEFECTS

january month top

defects

total check pcs. 717927

defects total percentage

Spot 12909 1.79809368
uncut thread 12637 1.76020682
skip stich 6308 0.87864086
joint out 5330 0.74241532
broken stitch 4620 0.64351947
mending 4377 0.60967201

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uneven 2847 0.39655842
unbalancing 2055 0.2862408
puckering 1786 0.24877181
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february month top

defects
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total check pcs. 575404

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defects total percentage

uncut thread 9042 1.57141765
spot 6295 1.09401394
mending 4663 0.81038714
skip stich 4255 0.73948043
joint out 4083 0.70958839
broken stitch 3289 0.57159839
uneven 1824 0.31699467
unbalancing 1201 0.20872291
open seam 1172 0.20368298

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After comparing January month dhu and February month dhu , top five defects are found.which
have the most occurring frequency among all defects. And these defects are:

Spot

Uncut thread

Skip stitch

Joint out

Broken stitch

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Table no 3 top 5 defects
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4.4 Cause and effect diagram

Sewing defects

4.4.1 Spot

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Cause of the defect
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A. Due to wrong handling by operator
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B. Due to pieces falling/laying on the floor improper transport if AM practices are not followed
properly
C. If operator hands are having Turmeric powder sticked
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D. Due to high WIP in Case of white fabric

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E. If hand gloves are not used

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F. If plastic bags are not used in case of high WIP

G. If cut-parts coming from cutting already having dirt marks

Effect caused by defect

A. If garment wash is not there then the spot may lead to rejection of the garment.

Remedy for the defect

A. The operators hand should be always clean
B. The floor should be clean always
C. A.M. Practices should be properly followed

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D. Operators should be taught to avoid use of turmeric powder on face
E. For white fabrics & light color fabrics extra care is required.
F. Bags should be used in case of high WIP

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Figure 4 Cause and effect diagram of spot

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 4.4.2 Skip Stitch
Skip stitch is observed when the hook irregularly fails to pick up the needle thread owing to a
number of causes.

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Cause of the defect
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A. Bent/ incorrectly fitted needle
B. Inappropriate needle being used
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C. Wrong threading
D. Loosely wound thread in the bobbin
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E. Too big/ too small thread loop

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F. Thread tensioned springs not working properly

G. Too small lopper setting

Effect caused by defect

A. Affects the garment look
B. Can lead to open seam
C. Affects the seam strength
Remedy for the defect
A. Ensure correct needle size is used
B. Proper threading procedure must be followed

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C. Ensure proper working of thread tensioned springs
D. Maintain correct looper setting.

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Figure 5 Cause and effect diagram of skip stitch

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4.4.3 Broken Stitch

This defect can be seen when a cut is observed in the stitch.

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Cause of the defect
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A. High tension levels in the tensioner.
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B. Sewing thread and bobbin thread under high tension.

C. The operator while trimming the uncut threads makes it so close,that the trimmer cuts the
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stitch.
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D. Inaccurate placement of the needle. Uneven winding on the bobbin case

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Effect caused by defect

A. Broken stitches makes the stitch insecure and may lead to unraveling of the stitch.

B. While reworking for broken stitches in O/L and F/L m/c's, the fitting of the garment will go
off due to reduction in actual measurements.

C. Reworking on a low gsm fabric will lead to the tearing of the fabric.

D. In reworking, the stitch need to be completely removed and restitching need to be done. In
doing so, there is a possibility of needle holes getting big which is another defect.

20 | P a g e
 i
na
en
Ch
T

Figure 6 Cause and effect diagram of broken stitch

IF
N

Remedy for the defect

A. Correct tension levels.

B. Proper winding of bobbin threads in bobbin case.

C. Effective handling of the trimmer by the operator.

D. Accurate placement of the needle.

21 | P a g e
 4.4.4 Uncut Thread

This Defect is generally found when the threads are protruding out from the stitch ends

i
na
en
Cause of the defect
Ch
A. Bad Condition of trimmer
B. Operator not using trimmer correctly
C. Bad blade condition in case of UBT
T

D. Improper positioning of blade in case of UBT

IF
N

Effect caused by defect

A. Bad appearance/unfinished garments
B. While cutting the uncut threads it may effect stitch strength.

22 | P a g e
 i
na
en
Ch
T
IF
N

Figure 7 Cause and effect diagram of uncut thread

Remedy for defect

A. Check the blade condition(UBT)

B. Use UBT wherever possible

C. Check Trimmer Condition

23 | P a g e
 4.4.5 Joint Out

Joint out is observed when the seam of the two pannel are not matching.

i
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Cause of the defect
en
A.Improper pressure foot
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B.uneven width during handling
C.new operator
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D.mis match notch

E.improper feed time and looper time
IF

F.slippery material
N

Effect caused by defect

A. Affects the garment look

24 | P a g e
 i
na
en
Ch
T

Figure 8 Cause and effect diagram of joint out

IF
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REMEDY FOR THE DEFECT

A. proper notch

B. self checking method

C. proper handling by the operator

D. Maintain correct pressure foot setting

25 | P a g e
 Waste Root causes Solutions

Lack of motivation
Unskilled operators
Incapable processes
Training and
No adherence to standards/procedures motivation for
In conducive work environment operators ; Right First
Rework/ Defects Machine breakdown/delays Time i.e. RFT ;
Improper machine settings Establishing of proper

Operator negligence checkpoints ; Load

levelling

i
Large batch sizes

na
Incorrect assessment of pieces

Table no 4 why analysis

en
Ch
T
IF
N

26 | P a g e
4.5 DHU

4.5.1 Dhu of February month

total defects 38876
total check pcs. 575404
DHU % 6.86700127

Defective %

25
20
15
10

i
na
5
0 effeciency

thread…
label centre…
raw edges

H.S damage
mending

broken stitch

wrong label

hole

bartack

print cracking
skip stich

shading

measurement
label missing

mis print
spot
uncut thread

puckering
joint out

uneven

open seam

pleat
bottom wavy

buttoning
unbalancing

placement
needle mark

en
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Figure 9 defective percentage of February

The above figure show the defective percentage of each defects occurred in February
month. In this we can see the top defects.
T
IF

defects total percetage

N

uncut thread 9042 1.57141765

spot 6295 1.09401394
skip stich 4255 0.73948043
joint out 4083 0.70958839
broken stitch 3289 0.57159839
Table 5 percentage of top defects in February

27 | P a g e
 DHU%
12% uncut thread
15%
34% spot
16% skip stich
23%
joint out
broken stitch

Figure 10 pie chart of defects of February

i
4.5.2 Dhu of March month

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total defects 35649
total check pcs. 655404 en
DHU % 5.439241
Ch
Defective %
20
15
T

10
IF

5
0 effeciency
label centre…

measurement
bottom wavy

H.S damage
mending

broken stitch

thread tension
hole
wrong label

shading
bartack

print cracking
mis print
uncut thread

skip stich

puckering
uneven
spot

open seam

pleat

label missing

buttoning
unbalancing

raw edges

placement
joint out

needle mark
N

Figure 11 defective percentage of march

defects total percentage

uncut thread 6042 0.92187414
spot 5995 0.91470299
skip stich 4227 0.64494571

28 | P a g e
 joint out 3098 0.47268555
broken stitch 2990 0.45620716
Table 6 percentage of top defects in march

DHU%
13%
uncut thread
27% spot
14%

19% skip stich

27%
joint out

i
broken stitch

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Figure 12 pie chart of defects of march en
4.5.3 Dhu of April Month
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total defects 15117

total check pcs. 312372
T

DHU % 4.839422
IF

Defective %
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25
20
15
10
5
0 effeciency
puckering

H.S damage

hole
mending

broken stitch

label centre out

wrong label

bartack
thread tension
uncut thread

skip stich

shading

measurement
print cracking
uneven

label missing

mis print
spot

buttoning
raw edges

bottom wavy

placement
joint out

unbalancing
open seam

pleat

needle mark

Figure 13 defective percentage of April

29 | P a g e
 defects total percentage
uncut thread 3150 1.00841305
spot 2567 0.8217766
skip stich 1678 0.53718003
joint out 1263 0.40432561
broken stitch 1134 0.3630287
Table 7 percentage of top defects in April

DHU%
12%
uncut thread
32%

i
13% spot

na
17% skip stich
26%
joint out
broken stitch
en
Figure 14 pie chart of defects of April
Ch
T

Its been inferred from the above tables and pie chart that over all DHU percentage has
IF

been reduced from 8.2 % to 4.8 %. Percentage of top five defects also been decreased.
N

defects february march april total

Spot 6295 5995 2567 14857
uncut thread 9042 6042 3150 18234
skip stich 4255 4227 1678 10160
joint out 4083 3098 1263 8444
broken stitch 3289 2990 1134 7413
total 26964 22352 9792 59108

30 | P a g e
 10000 9042
9000
8000
7000 6295
5995 6042
6000
5000 4255 4083 4227
4000 3289 3098 2990 3150
2567
3000
1678
2000 1263 1134

i
1000

na
0
1 2 3
Spot uncut thread skip stich
en joint out broken stitch

Figure 15 bar graph of five defects.

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It is been inferred from above bar that in February month uncut thread is the highest
defect and lowest defect is broken stitch.
T
IF
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31 | P a g e
5 FINDING
 Improper operator training caused rework.
 Absence of inline quality checkpoints lead to late identification of defects.
 Certain critical operations were more prone to defects than others.
 Improvement in operation process reduced rejections.

6 CONCLUSIONS

 Reduced rejections and reworks and Improvement in quality through inline quality
check.
 Reducing number of defects leads to less throughput time.
 DHU has been reduced from 8 % to 4 %

i
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 Individual defects rate has been reduced.
Minimizing defect is very important for ensuring the quality of products. The importance of the
en
garment industry in the economy of Bangladesh is very high. The perceived quality of a garment
is the result of a number of aspects, which together help achieve the desired level of satisfaction
for the customer. However, we should bear in mind that 1% defective product for an
Ch
organization is 100% defective for the customer who buys that defective product. So
manufacturing the quality product is mandatory to sustain in this global competitive market. Our
first objective is to identify the top positions where maximum defects occur and second is to
identify the top defect types in those positions. Keeping this in mind we have performed Pareto
T

Analysis.
IF

. Then the hierarchy of causes for each defect types are organized and the causes of those defect
types are shown individually using Cause-Effect Diagram. Finally we have provided some
N

suggestions so that the management can apply them to minimize the frequency of those defects.
Thus we can effectively minimize reworks, rejection rate and waste of time that will ultimately
increase productivity.

32 | P a g e
7 SCOPES

 Decrease in level of rejections and reworks and reduced fabric usage.

 Increased productivity and reduction in lead time and cost reduction and increased profit
 Decrease in re-work ultimate lead to profit of industry. Profits can be utilized in bringing
more advanced machines which can lead to minimum re-work

8. REFERENCE
[1] BGMEA B2B Web Portal, http://www.bgmea.com.bd/home/pages/Strengths (date of
retrieval: May 15, 2012)
[2] Production Process of RMG – Essays, http://www.studymode.com/essays/Production-
Process-Of-Rmg-580425.html (date of retrieval: May 15, 2012)

i
[3] Md. Mazedul Islam, Adnan Maroof Khan and Md. Mashiur Rahman Khan, “Minimization of

na
Reworks in Quality and Productivity Improvement in The Apparel Industry”, International
Journal of Engineering and Applied Sciences, January 2013. Vol. 1, No.4,
[4] Mohiuddin Ahmed and Nafis Ahmad, “An Application of Pareto Analysis and Cause-and-
en
Effect Diagram (CED) for Minimizing Rejection of Raw Materials in Lamp Production
Process”, Management Science and Engineering, Vol. 5, No. 3, 2011, pp.87-95,
[5] Learn About Sewing Problems/Problems Of Sewing/Different Type Of Sewing Problems,
Ch
http://textileeducationtips.blogspot.com/2013/03/learn-about-sewing-problemsproblems-of.html
(date of retrieval: July 19, 2012)
[6] Common Defects In Denim Jeans Sewing,
http://www.denimsandjeans.com/denim/manufacturing-process/common-defects-indenim-jeans-
sewing/ (date of retrieval: July 19, 2012)
T

[7] Defects of Sewing, http://www.jukiindia.com/item-list/defcts_of_sewing.pdf (date of

IF

retrieval: July 19, 2012)

[8] Abhishek Kumar, “Apparel Standards Specifications and Quality Control, Cutting, Sewing
& Finishing Defects”, National Institute of Fashion technology,
N

http://share.pdfonline.com/7926bbe6d71d4dcbb81ec5c1a603df26/Abhishek.htm (date of
retrieval: July 19, 2012)
[9] Glossary of Defect Terminology Used in the Garment Industry,
http://www.garmento.org/quality/Glossary_of_Defect_Terminology.pdf (date of retrieval: July
19, 2012)
[10] Garment defects, http://www.slideshare.net/2008000400034/garment-defects (date of
retrieval: July 19, 2012)
[11] William J. Kolarik, Creating Quality, International Editions 1995, McGraw-Hill Inc., page:
173-192 [12] Ahsan Akhtar Hasin, Quality Control and Management, First Edition 2007,
Bangladesh Business Solutions, page: 30-50
[13] Dung, Nguyen Hoang Phuong, “Improving the quality of service, Case: International SOS
Viet Nam”, Bachelor’s Thesis of Degree Programme in International Business, Lahti University
of Applied Sciences, Winter 2010,

33 | P a g e
 [14] Perzyk, M. (2007), “Statistical and Visualization Data Mining Tools for Foundry
Production”, Foundry Commission of the Polish Academy of Sciences, 7 (3), page: 111 – 116.
[15] Chandna, P., & Chandra, A. (2009), “Quality Tools to Reduce Crankshaft Forging Defects:
An Industrial Case Study”, Journal of Industrial and Systems Engineering, 3 (1), page: 27-37.
[16] Mahto, D., & Kumar, A. (2008), “Application of Root Cause Analysis in Improvement of
Product Quality and Productivity”, Journal of Industrial Engineering and Management, 01 (02),
page: 16-53.
[17] Khekalei, S. N., Chatpalliwar, A. S., & Thaku, N. (2010), “Minimization of Cord Wastages
in Belt Industry Using DMAIC”, International Journal of Engineering Science and Technology,
2 (8), page: 3687-3694.
[18] Tips for Better Topstitching,http://www.threadsmagazine.com (date of retrieval: July 25,
2012)
[19] A&E Visual Examples of Stitch & Seam Defects - Garmento.org
http://www.garmento.org/quality/a&estitchseamdefects.pdf (date of retrieval: July 25, 2012)
[20] Alterations | Sew for dough http://sewfordough.wordpress.com/category/alterations (date of
retrieval: July 25, 2012)

i
[21] Major/Minor/Critical defects: what are they? http://www.asiaqualityfocus.com/blog/major-

na
minor-critical-defects (date of retrieval: July 25, 2012)
http://textilelearner.blogspot.in/2011/08/quality-control-system-in-garments_2589.html read on 1 feb
2017. en
http://www.fibre2fashion.com/industry-article/3055/quality-systems-for-garment-
manufacture?page=4 read on 2 feb 2017.
Ch
An introduction to quality control for the apparel industry Pradip V. Mehta - 1985 .

Managing quality in the apparel industry, Pradip V. Mehta, Satish K. Bhardwaj – 1998.
T

Various Methods of Inspection Systems for Apparels; K. Sakthivel, online publication.

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Garments & Technology; Prof.M. A. Kashem-2009

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Technology of Clothing Manufacture 2nd ed, Harold Carr and Barbara Latham.

TQM: Towards Zero Defects By: C. K. Karekatti http://en.wikipedia.org/wiki/Six_Sigma

http://www.onlineclothingstudy.com/2013/08/10-reasons-garments-having-poor-quality.html
https://www.somo.nl/wp-content/uploads/2004/01/Critical-Issues-in-the-garment-Industry.pdf
https://www.intouch-quality.com/blog/top-6-most-common-garment-defects
http://www.cedc.ro/media/MSD/Papers/Volume%201%20no%202%202009/5.pdf
https://www.slideshare.net/lasithalck/quality-controlaspectsofgarmentexports
http://www.isca.in/IJMS/Archive/v2/i8/2.ISCA-RJMS-2013-042.pdf
http://cscanada.net/index.php/mse/article/download/j.mse.1913035X20110503.320/1909

34 | P a g e
QUALITY CHECK POINTS IN CUTTING DEPARTMENT

1. CUT PARTS CHECKING OR AUDIT:

 Checkers check the cut parts
2. BUNDLE CHECKING:
 Random checking of bundles by Roaming QC.
3. MARKER CHECKING:
 Supervisors check the marking.

NOTE: THERE SHOULD BE REJECTION OF THE PANELS AT THIS POINT ONLY BEFORE GOING TO THE
SEWING DEPARTMENT.

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REJECTION DUE TO CUTTING IN SEWING DEPARTMENT

na
 Cost of the stitched garment is more than a panel.
 By rejecting whole garment en
 Time loss
 Money loss in terms of resources, equipments.
Ch

EXAMPLE:

IN EXISTING SYSTEM WE FOUND .36 % COMPLETE GARMENT REJECTION FOR FABRIC FAULT IN PANEL
T

WHICH IS NOT REPLCED FROM THE CUTTING.

IF

Cutting section inputs 40,000 pieces in sewing line per day.

N

Then,

=40000* .3%

=120 pieces per day

=(120 *26) 3120 pieces monthly

1 piece cost Rs 98 (approx)

For 3120=RS 305,760

QUALITY CHECK POINTS IN SEWING DEPARTMENT:

1. INLINE INSPECTION:
 There should be an inline inspection for critical operations in every line.
 100% checking is done for partially stitched garments .
 Defects free garments are forwarded to the next next process.
2. ROAMING INSPECTION:
 Roaming QC checks pieces at each operation.
 Randomly checks the pieces.
3. END LINE INSPECTION:
 Checkers check the stitched garment at the end of the line.
 100% checking is done here.
 It contains the record of the alteration and DHU.
4. AUDIT :
 This is to assure that 100% inspected garments are sent to finishing .

i
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 All defective pieces are repaired before sending to the finishing.
 This is very essential for the garment industry.
en
NOTE : THERE SHOULD BE A QUALITY PERFORMANCE SHEET OF EACH OPERATOR.
Ch
SOLUTION: TRAFFIC LIGHT INSPECTON SYSTEM:

 In this system each operator is given a card for measuring their quality performance.
 Checkers will check the pieces , according to the inspection checker will mark red or green on
T

the card following rating criteria.

IF
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