Abdur Rahman
Shahjalal University of Science and Technology, Bangladesh
E-mail: airdipu@gmail.com
Salaha Uddin Chowdhury Shaju
Shahjalal University of Science and Technology, Bangladesh
E-mail: sust007@gmail.com
Sharan Kumar Sarkar
Shahjalal University of Science and Technology, Bangladesh
E-mail: sharan.sarkar303@gmail.com
Mohammad Zahed Hashem
Shahjalal University of Science and Technology, Bangladesh
E-mail: zahedhashem@gmail.com
S. M. Kamrul Hasan
Shahjalal University of Science and Technology, Bangladesh
E-mail: sm.hasankamrul@gmail.com
Ranzit Mandal
Jahangirnagar University, Bangladesh
E-mail: zit966@gmail.com
Umainul Islam
Snowtex Outwear Ltd, Bangladesh
E-mail: umainul77@hotmail.com
Submission: 17/03/2017
Revision: 27/04/2017
Accept: 08/05/2017
ABSTRACT
This paper demonstrates the empirical
application of Six Sigma and Define-Measure-Analyze-Improve-Control (DMAIC)
methodology to reduce product defects within a garment manufacturing
organization in Bangladesh which follows the DMAIC methodology to investigate
defects, root causes and provide a solution to eliminate these defects. The
analysis from employing Six Sigma and DMAIC indicated that the broken stitch
and open seam influenced the number of defective products. Design of
experiments (DOE) and the analysis of variance (ANOVA) techniques were combined
to statistically determine the correlation of the broken stitch and open seam
with defects as well as to define their optimum values needed to eliminate the
defects. Thus, a reduction of about 35% in the garments defect was achieved,
which helped the organization studied to reduce its defects and thus improve
its Sigma level from 1.7 to 3.4.
Keywords: Six Sigma; DMAIC; Defects;
Garment; Bangladesh
1. INTRODUCTION
Six Sigma was proposed first by the Motorola
company in the mid-1980s as an approach to improve production, productivity and
quality, as well as reducing operational costs (BHOTE; BHOTE, 1991) which has
been traditionally used to measure the variation in a process (OMACHONU; ROSS,
2004). In the Six Sigma’s terminologies, the Sigma level is denoted as a
company’s performance (PYZDEK; KELLER, 2010). Particularly, a Six Sigma level
refers to 3.4 defects per million opportunities (DPMO) (STAMATIS, 2004).
Brue and Howes (2005) told that Six
Sigma is a management philosophy and strategy as well as a problem-solving and
improvement methodology that can be applied to every type of process to
eliminate the root cause of defects besides being a measure of variability and
organization’s quality performance. In general, some authors argue that the
main benefits that an organization can gain from applying Six Sigma are: cost
reduction, cycle time improvements, defect elimination, an increase in customer
satisfaction and a significant rise in profits (DALE; WIELE; IWAARDEN, 2007;
BREYFOGLE; CUPELLO; MEADOWS, 2001).
Markarian (2004) suggests that not
only can the process improvement generated by Six Sigma be used in
manufacturing operations, but also it is the case for the project presented in
this paper as well as it can also be expanded to improve business sectors such
as logistics, purchasing, legal and human resources. Kumar et al. (2008) state that
although Six Sigma is normally used in defects reduction (industrial
applications), it can also be applied in business processes and to develop new
business models.
Banuelas et al. (2005) claim that
other benefits such as an increase in process knowledge, participation of
employees in Six Sigma projects and problem solving by using the concept of
statistical thinking can also be gained from the application of Six Sigma. To
illustrate this point, during the utilization of Six Sigma in this research project,
several tools and techniques were employed.
One of the Six Sigma’s distinctive
approaches to process and quality improvement is DMAIC (GARZA-REYES, et al.
2010). The DMAIC model refers to five interconnected stages i.e. define,
measure, analyze, improve and control that systematically help organizations to
solve problems and improve their processes. Dale et al. (2007) briefly defines
the DMAIC phases as follows:
What is the
problem? |
What data is
available? |
What are the
root causes of the problem? |
Do
we have the right solutions? |
What do we
recommend? |
What is the
scope? |
Is the data
accurate? |
Have the
root causes been verified? |
How will we
verify the solutions work? |
Is there
support for our suggestion? |
What key
metric is important? |
How should we
stratify the data? |
Where should
we focus our efforts? |
Have the
solutions been piloted? |
What
is our plan to implement? |
Who are the
stakeholders? |
What graphs
should we make? |
What
clues have we uncovered? |
Have we
reduced variation? |
Are result
sustainable? |
Define
– this stage within the
DMAIC process involves defining the team’s role, project scope and boundary,
customer requirements and expectations and the goals of selected projects (GIJO;
SCARIA; ANTONY, 2011).
Measure – this stage includes selecting the
measurement factors to be improved (OMACHONU; ROSS, 2004) and providing a
structure to evaluate current performance as well as assessing, comparing and
monitoring subsequent improvements and their capability (STAMATIS, 2004).
Analyze – this stage centers on determining the
root cause of problems (defects) (OMACHONU; ROSS, 2004), understanding why
defects have taken place as well as comparing and prioritizing opportunities
for advance betterment (ADAMS; GUPTA; WILSON JR. 2003).
Improve – this step focuses on the use of
experimentation and statistical techniques to generate possible improvements to
reduce the amount of quality problems or defects (OMACHONU; ROSS, 2004).
Control – finally, this last stage within the
DMAIC process ensures that the improvements are sustained (OMACHONU; ROSS,
2004) and that ongoing performance is monitored. Process improvements are also
documented and institutionalized (STAMATIS, 2004).
DMAIC resembles the Deming’s
continuous learning and process improvement model plan-do-check-act (PDCA) (DEMING,
1993). Within the Six Sigma’s approaches, DMAIC assures the correct and
effective execution of the project by providing a structured method for solving
business problems (HAMMER; GODING, 2001).
Pyzdek (2003) considers DMAIC as a
learning model that although focused on executing improvement activities,
emphasizes the collection and analysis of data previously to the execution of
any improvement initiative. This provides the DMAIC’s users with a platform to
take decisions and courses of action based on real and scientific facts rather
than on experience and knowledge as it is the case in many organizations,
especially small and medium size enterprises (GARZA-REYES, et al. 2010).
Statistically, Six Sigma refers to a
process quality measurement and the nearest specification limit is at least six
times the standard deviation of the process (FURSULE; BANSOD; FURSULE, 2012).
At present, the application of Six Sigma can be found in areas ranging from
facility management and maintenance functions (HOLTZ; CAMPBELL, 2004), online
market research (RYLANDER; PROVOST, 2006), supply chain improvement (KNOWLES,
et al. 2005), such non-manufacturing areas as healthcare management (REVERE;
BLACK, 2003), managerial accounting ALBRIGHT; LAM, 2006), and human resources
management (WYPER; HARRISON, 2000).
The formulation and identification
of useful theories related to Six Sigma development have also been proposed (LINDERMAN,
et al. 2003). In the Six-Sigma program, sigma stands for standard deviations
from the mean of a data set, in other words a measure of variation among the
data set, while Six-Sigma stands for six standard deviations from the mean.
People in industries from manufacturing to service are witnessing the growth of
a strategic continuous improvement concept called Six-Sigma (HARRY, 1998).
Six Sigma is a business improvement
strategy used to improve profitability, to drive out waste, to reduce costs and
improve the effectiveness and efficiency of all operational processes that meet
or exceed customer’s expectations (ANTONY; BANUELAS, 2001).
Product Design is a process of
creating a new product from an organization or business entity for its
customer. Being part of a stage in a product life cycle, it is very important
that the highest levels of effort are being put in the stage (SHAHRIZAL, 2013).
Pointed out many components of
successful Six-Sigma implementation as upper management support, organizational
infrastructure, training, tools, link to human resource based actions
measurement system and information technology infrastructure (HENDERSON; EVANS,
2000).
Highlighted that continuous
improvement techniques are the recognized way of making significant reduction
in production costs (HOERL, 2001). Finally, the objective of Six-Sigma is to
reduce the variation in the process and defects of the final product (GEOFF,
2001).
1.1.
Background
of the study
First the line defect rate was more
than 60%, whereas the project defect rate is 43% respectively. Because of all
buyers wants to check AQL level 2.5, the target would be project defect rate
reduces less than 2%. If we want to pass our good garments for shipment within
Buyer required AQL 1.5% or 2.5%, we must fix upon an average 2% defect rate in
a line or factory.
1.2.
Methodology
We have used Six Sigma and Define-Measure-Analyze-Improve-Control
(DMAIC) methodology to reduce product defects. Design of experiments (DOE) and
the analysis of variance (ANOVA) techniques were combined to statistically
determine the correlation between the variable. We have done cause and effects
diagram and Pareto analysis.
1.3.
Case
study of Six Sigma and DMAIC application
DMAIC is a data-driven quality
strategy used to improve the defect rate or processes. It is an integral part
of a Six Sigma initiative, but in general can be implemented as a standalone
quality improvement procedure or as part of other process improvement
initiatives such as lean. DMAIC procedure is applied to our project for better
tools and techniques used in the driven line for reducing defects rate.
2. DEFINE
Revere and Black (2003) suggest that
a Six Sigma project should be selected based on company issues related to not
achieving customer’s expectations. The chosen projects should be focused on
having a significant and positive impact on customers as well as obtaining
monetary savings. Regarding to these suggestions, the problem selected to be
tackled through this project was to reduce quality defects on the product,
which clearly comprise both an impact on the customer’s expectations and
important savings for the organization studied. According to the Linderman et
al. (2003) listening to customers is critical for a business to be successful.
So, the voice of the customer (VOC) concept, which means identifying what the
Table 1: Summary of the project.
Project Title: |
Defects reduction in garment products |
Background and reasons for
selecting the project: |
Vast number of garment products has been rejected by customers
due to defective. This problem causes several types of losses to the company,
i.e. time, materials, capital as well as it creates customer’s
dissatisfaction, which negatively affects the organization’s image. |
Project Goal: |
To reduce the defects by 35% after applying Six Sigma into the
garments manufacturing process. |
Voice of the Customer (VOC): |
Product’s quality. |
Team members: |
Production manager, an experienced shop-floor operator and the
improvement project leader. |
Expected Financial Benefits: |
A considerable cost saving due to the defect reduction. |
Expected Customer Benefits: |
Receiving the product with the expected quality. |
Customers want and serving
priorities to their needs (HARRY, 1998), was used in this project to define,
based on customer requirements we have select project’s objective. From this
point, voice of customer also ensured that the project problem, which was
defects reduction, became first priority for the improvement team and the organization.
A project summary, which is a tool
used to document the targets of the project and other parameters at the outset (LINDERMAN, et al. 2003) which was employed to state and present the project’s
information structure as well as the summary of the project, VOC, goal and the
team’s role in this research project. The summary of the project is presented
in Table 1.
3. MEASURE
The ‘measure’ phase of the DMAIC
problem solving methodology consists of establishing reliable metrics to help
monitoring progress towards the goal (PYZDEK, 2003), which in this research
consisted of reducing the number of quality defects in the garments
manufacturing process. Particularly, in this project the ‘measure’ phrase meant
the definition and selection of effective metrics to clarify the major defects
which needed to be reduced (OMACHONU; ROSS, 2004).
We were using two metrics to compare
the ‘before and after’ states of the garments manufacturing process when
conducting the Six Sigma’s projects. After defining the total number of
defects, Sigma level of the garments manufacturing process was calculated. Here
we have selected the C-14 line for the pilot run. The project was started from
1st November, 2016.
And its duration was taken 90 days, which
ends on 31st January, 2017. The project was TQM base. All party’s
involvement to reduce the project defect rate less than 2% is our goal which
will impact our quality and efficiency.
Table2: Defects summary before the improvement.
Type
of defects |
Number
of defects |
Percentage
of defects |
Broken |
412 |
48.53 |
Skip |
211 |
24.85 |
Open |
195 |
22.97 |
Puckering |
31 |
3.65 |
Total |
849 |
100 |
As a next step, a Pareto analysis
[36, 37] was carried out to identify the utmost occurring defects and
prioritize the most critical problem which was required to be tackled. The
collected data was generated in the form of a Pareto chart, which is
illustrated in Figure 1. The Pareto chart shown in Figure 1 indicated that the
highest rate of defects was caused by breaking stitch which contributed to over
48.52 percent of the overall number of defects.
Therefore, the
improvement team and the organization decided to initially focus on the
reduction of the broken stitch defect. The broken stitch defect rate was then translated into the
Sigma levels as 1.7 Sigma. The calculation of the Sigma metrics allowed the
improvement team and organization to have a more detail and operational
definition of the current state of the garments manufacturing process as well
as the Six Sigma’s goal in terms of the garments process improvement.
These are shown in Table 3. The next
stage in the Six Sigma project and following the DMAIC methodology, consisted
in analyzing the root causes of this problem as well as identifying an
appropriate solution.
Figure
1: Pareto for project line defect before implementation.
Table3: Manufacturing process – Current and Expected States.
Major
Types of Defects |
Number
of Major Defects |
Sigma
Levels |
||
C* |
E* |
C* |
E* |
|
Broken |
412 |
174 |
1.7 |
3.4 |
C*
= Current process performance E* = Expected process performance
after the completion of the six-sigma project
4. ANALYZE
This phase in the DMAIC improvement
methodology involves the analysis of the system, in this case the manufacturing
process that produces the garment product to identify ways to reduce the gap
between the current performance and the desired goal (GARZA-REYES, et al. 2010).
To do this, an analysis of the data is performed in this phase, followed by an
investigation to determine and understand the root cause of the problem (BREYFOGLE
III; CUPELLO; MEADOWS, 2001).
Figure
2: Cause and effect diagram for scope area.
Henderson
and Evans (2000) defines that to gain an enhanced comprehension and understanding
of the garment production process is a main requirement for improvement. An
analysis was carried out to identify the root causes of the broken stitch
defect.
Several
brainstorming sessions were conducted to identify based on the improvement team
member’s experience, probable causes as to why the problem in product occurred.
To illustrate and categorized the probable causes of the problem, a
cause-and-effect diagram (Figure 2) was constructed.
The
cause-and-effect diagram, also known as Ishikawa or Fishbone diagram, is known
as a systematic questioning technique for seeking the root causes of problems (ANTONY;
BANUELAS, 2001) by providing a relationship between an effect and all plausible
causes of such effect (OMACHONU; ROSS, 2004). Once completed, the diagram helps
to uncover the root causes and provide ideas for further improvement (DALE;
WIELE; IWAARDEN, 2007).
There are
five main categories normally used in a cause-and-effect diagram which is known
as 5M, namely: machinery, manpower, method, material and measurement (DALE;
WIELE; IWAARDEN, 2007) plus an additional parameter environment. The possible
root causes brainstormed are illustrated in the cause-and-effect diagram shown
in Figure 2. After considering all possibilities, it was found that some stages
and operations i.e. improper threading, poor clamping or insufficient pressure
(flagging), wrong size needle, wrong type of needle for the material within the
garments manufacturing process had an impact on causing the broken stitch.
5. IMPROVE
After the
root cause(s) has been determined, the DMAIC’s improve phase aims at
identifying solutions to reduce and tackle them (OMACHONU; ROSS, 2004).
Stamatis (STAMATIS, 2004) suggests the use of design of experiments (DOE),
which is defined as a statistical technique to investigate effects of multiple
factors (KUMAR, et al. 2008; BANUELAS; ANTONY; BRACE, 2005), in the improve
phase.
By Garza-Reyes, et al. (2010),
benefits of DOE be enhancing process yields, decreasing variability and
lowering the overall expenses. The DOE technique was used to investigate
whether the assumed correlation was statistically significant or not. An
experiment was designed to investigate whether the parameters had a negative
effect on the process, causing defect products. To do this and to analysis the
experiment’s results, the analysis of variance (ANOVA) was used. ANOVA is a
statistical model for comparing differences
Table 4: Analysis of Variance (ANOVA).
Source |
Degrees of Freedom |
Adj SS |
Adj MSS |
F-Value |
P-Value |
Defect |
4 |
93.53 |
23.38 |
7.60 |
0.000* |
Parts |
2 |
2.24 |
01.12 |
0.36 |
0.695 |
Process |
20 |
76.62 |
03.83 |
1.25 |
0.213 |
Error |
399 |
1227.04 |
3.075 |
|
|
Lack-of-Fit |
75 |
185.82 |
2.478 |
0.77 |
0.913 |
Pure Error |
324 |
1041.21 |
3.214 |
|
|
Total |
425 |
1489.03 |
|
|
|
*5% level of Significance
Among means of more than two
populations (GIJO; SCARIA; ANTONY, 2011). However, if there are two sources of
data that need to be investigated, ANOVA, which is a statistical methodology
for analyzing the effect of the factors, is required (GIJO; SCARIA; ANTONY,
2011). The results of ANOVA analysis are shown in Table 4.
Analysis of Variance tells that the
overall variation is accounted by the average response variables. The above
analysis shows that the assume hypothesis is statistically significant to be
P-value < 0.05. So, there is a significant effect among the complete
process. Another hypothesis tells the mean difference between the individual
treatment mean. Some treatments have a statistically significant mean different
effect that means they are highly correlated to occur defect. They are Broken
stitch, Open seam, Arm hole and Side pocket.
6. CONTROL
The real strength of the DMAIC steps
is in the Control step. Whole teams do a lot of arduous work to improve the
process and results and then implementation of the improved process don’t go
smoothly. There is pressure to move on, time is not spent on having a smooth
transition and the buy-in for full implementation just is not quite there.
The result is that sustaining the
improvement realized in the improve step becomes difficult. The purpose of the
control step is to ensure a successful implementation of the team’s
recommendation so that long-term success will be attained. Then the improved
process will be flow charted and these new methods will become the new standard
operating procedures.
Results will continue to be tracked
so that any drift back to previous results can be monitored and addressed in a
proactive manner. The control step is about the transfer of responsibilities
and establishing plans for long-term process control.
7. RESULT
From the
figure 3 we see that initial project Defect Rate (DR) was too high, that is 43
to 39 percent and which was gradually decreasing day after day within one
month. Finally, it shows the 7 percent defect rate at the end of one month.
Figure
3: Project defect rate(DR) before implimentation.
We see from
the figure 4 that initial project Defect Rate (DR) was too high that, is 17 to
14 percent and which was gradually decreasing day after day within the deadline.
Finally, it shows the 2 percent defect rate at the end of the project deadline.
Also from the
figure 5 shows that, the initial Sigma level of the project was defined 1.7 and
also shows that it is increasing day by day after implementing necessary steps
for the defect reduction project. At the end of the project is being seen that
we have achieved the 3.4 Sigma which one is good but not best.
Figure
4: Project defect rate(DR) after impimentation DMAIC.
Figure
5: Project Sigma level.
Figure
6: Process Capability (Cpk) & Z (Sigma).
Also from the
figure 6 shows that is the other tool for reducing the process variability and
to improve the quality based product which is process capability (Cpk) and
Sigma. It tells that the Cpk value is about 0.88 too low, that means process
variability is so high besides Z (sigma) is also about 2.88 too low. Every
businessman or manufacturers desire 1.33 .
8. CONCLUSION
The primary goal of this project is
to identify action initiatives that make up the help of conducting the project
in the next step to reduce the defect rate at 2%, which is the main objective
of the project and to increase the productivity and quality goods.
The Defect Reduction Project report
shows that if it has been taken proper steps, then many defects are reduced by
only applying some scientific method and shows that process capability (Cpk) is
an effective tool to reduce the variability and to increase the productivity
and ensure the more quality product.
At the end of our project deadline,
we have been able to achieve the desired 2% defect rate. Finally, we can say
that all types of assignable causes are able, to control by reducing defects
and continuous improvement process.
Acknowledgment: We want to give thanks to the
Managing Director of Snowtex Outerwear Ltd. S M Khalid Hasan for giving the
opportunity and inspiration to run the project.
Conflict of Interest: We declare that there is no
conflict of interest.
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