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Analysis of Major Defects Position and Percentage in Sewing Lines of a Garments Factory with the Help of Pareto Chart, Cause Effect Diagram and Sigma Level

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Bangladesh is a developing country and 83% of the foreign currency comes from the garment sector. Garment export business is a vital issue for our country. To upgrade the position in the ranking and to make the position strong in the world contest we have to compete with world class competitors by maintaining 100% quality. So, we have to ensure that our production process management systems are the best and always under development and also capable of producing best quality product. This project work represents a study on defects in the sewing lines of a garments factory by Pareto chart, cause effect diagram and six sigma to find out the major defects and their percentage and also find out the defect standard level with the help of total amount of defects per million garments. It is studied in "Comfit Composite Knit Limited" which is a 100% export oriented knit garment factory. We worked in the sewing section for our project purpose and collected three months total defects data with all sewing lines, analyzed the defects by Pareto chart and identified 7 major defect positions where 78.94% of total defects occur. After that we worked in separate line to find out the defect condition. We observed in case of short sleeve polo shirt and sweat top tee sewing defects. After Pareto analysis, it was found out 9 major defects which contain 11.86% defect position areas where 54.02% of defects occur in case of polo shirt and in case of sweat top tee 5 major defects which contain 11.29% defect position area where 53.31% of defects occur, it was analyzed 6 common major defects and causes of these defects. Then it was shown by cause effect diagram and given some recommended remedies for these causes. After analysis the defects by sigma calculator we got sigma level 2.78 for all production lines, sigma level 2.88 for short sleeve polo shirt production line and sigma level 2.75 for sweat top tee production line which were inside the standard defect range.
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International Journal of Scientific & Engineering Research, Volume 8, Issue 7, July 2017
ISSN 2229-5518
IJSER © 2017
http://www.ijser.org
Analysis of Major Defects Position and
Percentage in Sewing Lines of a Garments
Factory with the Help of Pareto Chart, Cause
Effect Diagram and Sigma Level
Tarikul Islam, Shadman Ahmed Khan, Mahdiul Hasan Sakib, Asif Sakib, Abu Bakar Siddiquee
Abstract— Bangladesh is a developing country and 83% of the foreign currency comes from the garment sector. Garment export business
is a vital issue for our country. To upgrade the position in the ranking and to make the position strong in the world contest we have to
compete with world class competitors by maintaining 100% quality. So, we have to ensure that our production process management
systems are the best and always under development and also capable of producing best quality product. This project work represents a
study on defects in the sewing lines of a garments factory by Pareto chart, cause effect diagram and six sigma to find out the major defects
and their percentage and also find out the defect standard level with the help of total amount of defects per million garments. It is studied in
“Comfit Composite Knit Limited” which is a 100% export oriented knit garment factory. We worked in the sewing section for our project
purpose and collected three months total defects data with all sewing lines, analyzed the defects by Pareto chart and identified 7 major
defect positions where 78.94% of total defects occur. After that we worked in separate line to find out the defect condition. We observed in
case of short sleeve polo shirt and sweat top tee sewing defects. After Pareto analysis, it was found out 9 major defects which contain
11.86% defect position areas where 54.02% of defects occur in case of polo shirt and in case of sweat top tee 5 major defects which
contain 11.29% defect position area where 53.31% of defects occur, it was analyzed 6 common major defects and causes of these defects.
Then it was shown by cause effect diagram and given some recommended remedies for these causes. After analysis the defects by sigma
calculator we got sigma level 2.78 for all production lines, sigma level 2.88 for short sleeve polo shirt production line and sigma level 2.75
for sweat top tee production line which were inside the standard defect range.
Index Terms—Cause Effect Analysis, Defects, DPMO, Pareto Chart, Quality, Rework, Sigma Calculator.
—————————— u—————————
1 INTRODUCTION
he garments industry has played a vital role in developing
the socio economic condition in Bangladesh [1] [2]. De-
spite of its modest beginning in 1970s the apparel industry
in Bangladesh has grown to become one of the largest contrib-
utors to the export revenue of the country representing its to-
tal exports [1]. Moreover the apparel industry also contributes
around 83% of country's export earnings of Bangladesh. Being
the single largest employer in the manufacturing sector, the
apparel industry provides at least 20 million of Bangladeshi's
employment directly as well as indirectly. The quality of gar-
ments is vital to its survival in an increasingly competitive
apparel industry in order to maintain the production of high
quality garments and improved productivity in the apparel
industry [3] [5].
As the world economic condition is changing in a rapid mo-
tion. Generally in an industry more focus is given on profit
margin, customer demand for high quality product and im-
proved productivity. In garment manufacturing, it is usual
that there will the few rejected garments after shipment. Rea-
sons are most of the manufacturers believed that garments are
soft goods and non-repairable defect may occur due to low
quality raw materials or faulty process or employee casual
behavior. There is no ready-made solution that can reduce
defect percentage overnight [4] [5].
But this paper work suggests how to handle such problems
and bring down defects rate to minimum with quality produc-
tion. As we see a lot of defective garments after shipment,
most of the organization termed these garments as rejected
because those garments can't be repaired by any means [7].
Defect in the garments industry is a common phenomenon
that hampers the smooth production rate and focus on poor
quality products having an impact on overall factory econo-
my. Minimization of defects is a must in quality and produc-
tivity improvement. Rework is a vital issue for poor quality
product and low production rate [13]. Reworks are the non-
productive activities focusing on any activity that customers
are not willing to pay for. Non productive activities describe
that the customers 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 quali-
ty, the company can invest less money and more costs savings
[3][6]. Therefore, a study was carried out in the garment in-
dustry named “Comfit Composite Knit Limited” in sewing
section to identify defects so as to eliminate them for saving
time, cost and improved product quality.
T
————————————————
·Tarikul Islam is currently working as a Lecturer at Port City Internation
University (PCIU), Bangladesh and pursuing M.Sc (Engg.) degree in
Bangladesh University of Textiles (BUTEX), Dhaka, Bangladesh. PH-
01722202322. E-mail: matarikul.islam2014@gmail.com
·Shadman Ahmed Khan is currently working as a Lecturer at Bangladesh
University of Business &Technology, Dhaka, Bangladesh, PH-
01931230197. PH-01123456789. E-mail: shadmante26@gmail.com
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http://www.ijser.org
2 MATERIALS AND METHODS
2.1 Materials
1) Fabric Cut panel. 2) Sewing Thread. 3) Filament Thread. 4)
Button. 5) Interlining. 6) Short Sleeve Polo Shirt. 7) Sweat Top
Tee. 8) Sigma Calculator.
2.2 Methods
The quality tools which are used in this project are aimed at
identifying analyzing and implementing the defects in the
sewing line. Though defects are occurred in various depart-
ments in a garment factory, but we have concentrated only in
the sewing line. Quality is a main issue of a garment factory.
So we have to concentrate on defects. Defects can occur for
various reasons. Because of this defects rework time and cost
increases. This study includes theatrical idea about sewing line
layout, various defects, defect occurring position, Pareto chart,
cause-effect diagram, sigma level defects per million opportu-
nities. For this research we have selected a 100% export orient-
ed knitting garments factory named “Comfit Composite Knit
Limited”. This segment includes understanding about the
quality control system and how they perform when defects
occur and analysis the various data. Then analyzing the data
we have gathered idea about the defects and try to find a solu-
tion how to minimize the defects. Last of all we have tried to
implement to reduce defect percentage. The methodology
steps are given below:
Step 1: Factory Selection
For our research work first we have to select a factory from
where we have to collect data. After a lot of searching we have
been allowed by a 100% export oriented factory which is situ-
ated at Mirzapur, Dhaka.
Step 2: Data Collection
After selecting the factory we have selected the sewing de-
partment for our project. We have observed the quality control
system of the department. We have collected various defects
data for the month of August 2015 to October 2015 with 12
production lines for our project. This data is obtained by QC
man by 100% inspection in the end line QC. We also collected
the total garments checked in that time.
Step 3: Analysis Data
In this Step, we’ve to analyze the collected data to know about
the defects amount and percentage of defects. We have to cal-
culate the wise total defect data and three months total defects
data Men we've to analysis separate production line defects
for SS Polo Shirt and Sweat Top Tee.
Step 4: Analysis Data by Parero Chart & Cause Effect Dia-
gram
In this step, we've to analyze the defect data by pareto chart
tor to identify the major defects that occurs 80% area. From the
chart we have to find the defect position where the most de-
fects occur. Then we've to analyze major defects by cause-
effect diagram to identify the causes and sub-causes. Also to
identify for which masons the major defects occur i.e. man,
machine, material and method.
Step 5: Analysis Data by Sigma level
In this step, we've to analyze the total defects and the total
checked garments by sigma calculator to know the about the
defect standardization i.e. the sigma level and defogs per mil-
lion opportunity.
Step 6: Some Suggestions for Implementation
In this step, we've given some suggestion how to minimize
the defects that occurred in the sewing lines.
3. RESULTS AND DISCUSSION
3.1 Data Analysis and Observation by Pareto Chart
3.1.1 Three Months Defect Chart for SS Polo Shirt
Figure 1: Three Months Defect Chart for SS Polo Shirt
3.1.2 Observations from the Analysis
i. UNCUT THREAD is the most frequent defect with as much
as 23.16% of the total defects of SS Polo,
ii. BROKEN STITCH is the second most frequent defect with
14.86% of the total.
iii. Among other defects contribution of DOWN STITCH is
11.87%, UNEVEN STITCH is 11.22%, RAW EDGE is 7.86%,
and OIL SPOT is 7.2%.
iv. These six major defects are the “vital few” where 76.17% of
total defects occur.
Now we have analysis defects by SS Polo Shirt part wise. For
this, we are using side seam, bottom hem and placket joint
defects data.
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3.1.3 Major Concern Area foe SS Polo Shirt
Total Defects Position = 35+35+4+4+5 = 83
3.1.4 Three Months Defect Chart for Top Tee
Figure 2: Three Months Defect Chart for Top Tee
3.1.5 Observations from the Analysis
i. UNCUT THREAD is the most frequent defect with as much
as 24.88% of the total defects of Sweat Top Tee,
ii. UNEVEN STITCH is the second most frequent defect with
11.28% of the total.
iii. Among other defects contribution of DOWN STITCH is
10.20%, RAW EDGE is 10.12%, BROKEN STITCH is 9.5% and
OIL SPOT is 8.33%,
iv. These six major defects are the “vital few” where 74.31% of
total defects occur.
Now we have analysis defects by Sweat Top Tee part wise.
For this, we are using side seam, shoulder joint and neck joint
defects data.
3.1.6 Major Concern Area foe Sweat Top Tee
Total Defects Position = 27+27+2+2+3 = 61
3.1.7 Result of Pareto Chart
Total Number of Defects = 8150
Total Number of Defects in Major Concerning Area = 4345
Percentage of defects in major concerning area,
= 4345/8150
= 0.5331 X 100%
= 53.31%
There are 20 types of defects where uncut thread and uneven
stitch can occur at 27 positions. Rest 18 types of defects can
occur at 27 positions.
So, the number of total concerning area is [27+27+ (18×27)] =
540 which is responsible for total amount of defects.
But we have identified total 61 concerning areas by Pareto
Analysis which is responsible for 53.31% defects.
Total number of concerning area = 540
Total number of major concerning area = 61
Percentage of major concerning area,
= 61/540
=0.1129 X 100%
= 11.29%
So by concentrating only on 11.29% areas 53.31%of total de-
fects can be reduced.
3.1.8 Defect Percentage of Two Production Lines
Total Defects Occurred by 12 Production Lines = 98240
Total Defects Occurred by SS Polo Shirt Production Line
= 7811
Total Defects Occurred by Sweat Top Tee Production Line
= 8150
Two Lines Combined Total Defects = 7811+8150 = 15961
Defect percentage of Two Production Line,
= 15961/98240
= 16.24% of total defects percentage.
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3.2 Major Defects Analysis by Cause-Effect Diagram and
Recommended Remedies
3.2.1 Cause-Effect Diagram for Uncut Thread
Figure 3: Cause-Effect Diagram for Thread Uncut
Table 1: Cause-Effect Diagram for Uncut Thread
Causes Suggested Solutions
Operator ineffi-
ciency Provide adequate training to the opera-
tors
Improper trimming Provide thread cutter to every operator
and make used to.
Improper finishing Improve quality inspection system.
3.2.2 Cause-Effect Diagram for Broken Stitch
Figure 4: Cause-Effect Diagram for Broken Stitch
Table 2: Cause-Effect Diagram for Broken Stitch
Causes Suggested Solutions
Inappropriate thread ten-
sion Tension of the thread properly ad-
justed.
Needle plate, pressure
foot, needle holes may
have sharp edges
Inspect the needle point at regular
intervals and check for sharp or
burred points.
Weak thread Select good quality thread which is
free from flaws.
Wrong needle size and
thread size Needle size and thread size should
be synchronized.
Excessive abrasion or
chemical degradation of
thread during washing
Special care should be taken dur-
ing washing.
3.2.3 Cause-Effect Diagram for Uneven Stitch
Figure 5: Cause-Effect Diagram for Uneven
Table 3: Cause-Effect Diagram for Uneven
Causes Suggested Solutions
Operator speeding up
machine too rapidly Control the speed of machine, use
right needle and correct feed con-
trol.
Operator holding back or
pulling fabric through in
variance with correct
machine feed
Improve the skill of operator, use
good quality sewing thread, and
provide standard quality specifica-
tion.
Never pull on the fabric while sew-
ing, let it be taken up by the ma-
chine.
3.2.4 Cause-Effect Diagram for Raw Edge
Figure 6: Cause-Effect Diagram for Raw Edge
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Table 4: Cause-Effect Diagram for Raw Edge
Causes Suggested Solutions
Improper seaming Give proper training to the opera-
tors.
Operator carelessness Improve supervision.
Improper folding Improve or change folding system.
3.2.5 Cause-Effect Diagram for Oil Spot
Figure 7: Cause-Effect Diagram for Oil Spot
Table 5: Cause-Effect Diagram for Oil Spot
Causes Suggested Solutions
Slick out of oil from the
machine and drop on the
fabric and spotted.
Check the sewing machine
regularly.
3.2.6 Cause-Effect Diagram for Down Stitch
Figure 7: Cause-Effect Diagram for Down Stitch
Table 4: Cause-Effect Diagram for Down Stitch
Causes Suggested Solutions
Improper seaming Give proper training to the opera-
tors.
Operator carelessness Improve supervision.
Improper folding Improve or change folding system.
3.3 DPMO and Sigma Level Analysis
For this analysis, we have used sigma software calculator. If
we give input the total garments checked amount, no. of de-
fects amount, opportunity/unit and standard sigma shift then
we find the defects percentage (%), defects per million oppor-
tunities (DPMO) and sigma level.
Figure 8: DPMO and Sigma Level Analysis
3.3.1 Observation
From the analysis, we find that defects level lies between
the 2 to 3 sigma levels. So Defect Standard of this factory is
medium quality. Here Sigma Level of full Production Lines is
2.78, SS Polo Shirt Production Line is 2.88, and Sweat Top Tee
Production Line is 2.75.
From the Bar-line graph chart we understand that in SS Po-
lo Shirt Production Line the DPMO decreases, so the Sigma
Level increases. That means the defects occurred in this line is
less.
3.4 Results
For Pareto Chart Analysis
·From the three months combined defect chart with
full production line we find 7 major defects which
contains 78.94% of total defects.
·From the SS Polo Shirt Production line we find 9 ma-
jor defects which contain 11.86% defect position area
where 54.02% of major defects occur.
·From the Sweat Top Tee Production line we find 5
major defects which contain 11.29% defect position
area where 53.31% of major defects occur.
For Sigma Level Analysis
·For full production line, we find sigma level 2.78.
·For SS Polo Shirt Production line, we find sigma level
2.88.
·For Sweat Top Tee Production line, we find sigma
level 2.75.
4 CONCLUSION
Quality is a serious issue for an export oriented garment’s
factory. In order to take a strong position in the global compe-
tion, it is necessary to keep 100% quality on the product. Now-
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a- days, buyers are more concern on quality. If any fault is
found, they will have to cancel the full order. So, it is a great
responsibility for a quality department in a garment factory. In
this view, we are tried to analysis the quality condition in the
“Comfit Composite Knit Limited”. Garments factory has many
departments, so a short time we are not able to observe the all
departments. So, we concentrate on sewing department only.
Here we have studied on the defects and tried to find out the
major defects with their concerned area. We have used Pareto
chart to find out the major defects by 80/20 rules. Then we
have found the defect position where the major defects oc-
curred. From this analysis, we find 6 common major defects
which are occurred in all the production lines. After that we
have used sigma calculator to find out DPMO and Sigma lev-
el. From this we have easily understood the defect conditions
of the sewing lines of this factory. Then we have used the
cause-effect diagram to find out the causes and sub-causes
which are responsible for major defects. But it is our great
lacking that we are not able to implement anything in the sew-
ing line because of production load. But we have suggested
some suggestions to the QC manager. If our suggestions are
applied on the lines, we hopeful that the defect percentage,
rework cost and time will decrease and the productivity will
increase.
REFERENCES
[1] T. Ahmed, N. R. Acharjee, M. A. Rahim, N. Sikder, T. Akther, M. R. Khan, M.
F. Rabbi, and A. Saha, “An Application of Pareto Analysis and Cause-Effect
Diagram for Minimizing Defect Percentage in Sewing Section of a Garment
Factory in Bangladesh”, International Journal of Modern Engineering Research
(IJMER), vol. 3, no. 6, pp. 3700-3715, 2013.
[2] M. M. Islam, M. A. Khan, and M. R. Khan, “Minimization of Defects
in the Sewing of Apparel Industry, Research Journal of Management
Science, vol. 2, no. 8, pp. 10-15, 2013.
[3] M. M. Islam, M. A. Khan, and M. M. R. Khan, “Minimization of Re-
works in Quality and Productivity Improvement in apparel Indus-
try”, International Journal of Engineering and Applied Sciences, vol. 1, no.
4, pp. 147-164, 2013.
[4] S. M. Uddin, and C. M. L. Rahman, “Minimization of Defects in the
Sewing Section of a Garment Factory through DMAIC Methodology
of Six Sigma”, Research Journal of Engineering Sciences, vol. 3, no. 9, pp.
21-26, 2014.
[5] D. Haughey, Pareto Analysis Step by Step,
https://www.projectsmart.co.uk/pareto-analysis-step-by-step.php
[6] Comfit Composite Knit Ltd., http://www.youthbd.com/comfit-
composite-knit-ltd, 2015.
[7] Process Control Techniques, https://www.isixsigma.com/process-
sigma-calculator/process-sigma-calculator-assumptions
[8] Process Sigma Calculator, http://www.isixsigma.com/process-
sigma-calculator
[9] M. Yunus, and T. Yamagata,“Dynamics of the Garment Industry in
Low-Income Countries: Experience of Asia and Africa”, Institute of
Developing Economies and Japan External Trade Organization ,
Chousakenkyu Houkokusho, vol. 6, pp. 1-28, 2012.
[10] Sigma Performance Levels, http://www.isixsigma.com/sigma-
performance-levels – One to Six Sigma.htm
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... Total Defects are the number of complaints that have not been resolved, while Total Opportunities are the number of complaints received by helpdesk. After calculating the DPMO value, the next step is to determine the sigma level by using a sigma calculator [30,31] as shown in Figure 2. ...
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Helpdesk plays an important role in a company to maintain customer satisfaction. To ensure help-desk’s solutions effectiveness, the service quality must be evaluated and monitored periodically. In this study, we evaluated the quality service of help-desk service by finding out the sigma level to discover the root of the problems. We used complaint data from a help-desk service of an IS/IT department in a public university between 2016 and 2017. We performed sub-methodology DMAIC (Definition, Measurement, Analysis, Improvement, and Control) in Six-Sigma and adopted COBIT 5 framework in the improvement phase to enhance the service quality. The SIPOC diagram shows that the help-desk service was at a high-level. The DPMO value in 2016 was 30.153 and it rose to 80.155 in 2017. It caused the sigma level to drop from 3.37σ in 2016 to 2.90σ in 2017. From the Pareto chart, we know that complaints regarding SIAD and network account for as much as 51.72% and 23.82%, respectively. Therefore, the cause of the problem must be found, according to the Pareto principle. The root causes of this problem are categorized into policy, procedure, plant/technology, and people. Meanwhile, COBIT 5 presents solutions to policy and procedure problems by providing best practices on standard operating procedures through domain DSS02 Manage Service Requests and Incidents and domain DSS03 Manage Problems. The combination of Sig-Sigma and COBIT 5 is able to evaluate the service quality of the help-desk service. The method in this research can be used to evaluate service quality in other organizational divisions.
... Major Defects Position and Percentage in Sewing Lines of a Garments Factory with the Help of Pareto Chart, Cause Effect Diagram and Sigma Level in this paper by using the quality tools for analysing and implementing defects on the sewing line. From Pareto Chart Analysis 7 major defects found which contains 78.94% of total defects, 9 major defects which contain 11.86% defect position area where 54.02% of major defects occur (Tarikul Islam, Analysis of Major Defects Position and Percentage in Sewing Lines of a Garments Factory with the Help of Pareto Chart, Cause Effect Diagram and Sigma Level, July 2017) [8]. 5S (Sort, Set in order, Shine, Standardize, Sustain) and PDCA (Plan-Do-Check-Act) to identifying swing defect in a particular product and minimize the rework rate. ...
... Major Defects Position and Percentage in Sewing Lines of a Garments Factory with the Help of Pareto Chart, Cause Effect Diagram and Sigma Level in this paper by using the quality tools for analysing and implementing defects on the sewing line. From Pareto Chart Analysis 7 major defects found which contains 78.94% of total defects, 9 major defects which contain 11.86% defect position area where 54.02% of major defects occur (Tarikul Islam, Analysis of Major Defects Position and Percentage in Sewing Lines of a Garments Factory with the Help of Pareto Chart, Cause Effect Diagram and Sigma Level, July 2017) [8]. 5S (Sort, Set in order, Shine, Standardize, Sustain) and PDCA (Plan-Do-Check-Act) to identifying swing defect in a particular product and minimize the rework rate. ...
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An Application of Pareto Analysis and Cause-Effect Diagram for Minimizing Defect Percentage in Sewing Section of a Garment Factory in Bangladesh
  • T Ahmed
  • N R Acharjee
  • M A Rahim
  • N Sikder
  • T Akther
  • M R Khan
  • M F Rabbi
  • A Saha
T. Ahmed, N. R. Acharjee, M. A. Rahim, N. Sikder, T. Akther, M. R. Khan, M. F. Rabbi, and A. Saha, "An Application of Pareto Analysis and Cause-Effect Diagram for Minimizing Defect Percentage in Sewing Section of a Garment Factory in Bangladesh", International Journal of Modern Engineering Research (IJMER), vol. 3, no. 6, pp. 3700-3715, 2013.
Minimization of Defects in the Sewing of Apparel Industry
  • M M Islam
  • M A Khan
  • M R Khan
M. M. Islam, M. A. Khan, and M. R. Khan, "Minimization of Defects in the Sewing of Apparel Industry", Research Journal of Management Science, vol. 2, no. 8, pp. 10-15, 2013.
Dynamics of the Garment Industry in Low-Income Countries: Experience of Asia and Africa
  • M Yunus
  • T Yamagata
M. Yunus, and T. Yamagata,"Dynamics of the Garment Industry in Low-Income Countries: Experience of Asia and Africa", Institute of Developing Economies and Japan External Trade Organization, Chousakenkyu Houkokusho, vol. 6, pp. 1-28, 2012.