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
Submission: 12/09/2017
Revision: 06/03/2018
Accept: 15/03/2018
ABSTRACT
The
aim of this paper is to demonstrate the empirical application of Six Sigma and
Define-Measure-Analyze-Improve-Control (DMAIC) methodology to reduce product
defects within a garments manufacturing organization in Bangladesh which
follows the DMAIC methodology to investigate defects, root causes and provide a
solution to eliminate these defects. 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. The
analysis from employing Six Sigma and DMAIC indicated that the broken stitch
and open seam influenced the number of defective products. Thus, a reduction of
about 35% in the garments defect was achieved after the implementation of DMAIC
methodology, 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 Methods;
Defects Reduction; Garments Sector; Bangladesh
1. INTRODUCTION
Six Sigma was proposed by Motorola,
in the mid-1980s, as an approach to improve production, productivity and
quality, as well as reducing operational costs (BHOTE; BHOTE, 1991). The
Sigma’s name originates from the Greek alphabet and in quality control terms,
Sigma (σ) has been traditionally used to measure the variation in a process or
its output (OMACHONU; ROSS, 2004).
In the Six Sigma’s terminology, 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), or in other words, to have a process which only
produces 3.4 defects per every one million products produced.
Besides being a measure of
variability and organization’s quality performance, Brue and Howes (2005)
mention that Six Sigma is also 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. Some authors argue that the
main benefits that an organization can gain from applying Six Sigma are: cost
reduction, cycle time improvements, defects elimination, an increase in
customer satisfaction and a significant rise in profits (DALE; WIELE; IWAARDEN,
2007; BREYFOGLE III; CUPELLO; MEADOWS, 2001).
Markarian (2004) suggests that not
only can the process improvement generated by Six Sigma be used in
manufacturing operations, as it is the case for the project presented in this
paper, but it can also be expanded to improve business sectors such as
logistics, purchasing, legal and human resources. In addition, Kumar et al (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 (BANUELAS; ANTONY; BRACE, 2005). Claim that other benefits such as (i) an
increase in process knowledge, (ii) participation of employees in Six Sigma
projects and (iii) 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.
Therefore, skills in the use of
these tools were built up within the staff of the Thai organization studied.
Consequently, people involved in the project enhanced their knowledge and
skills. As a reason, not only does an organization itself gain benefits from
implementing Six Sigma in terms of cost savings, productivity enhancement and
process improvement, but individuals involved also increase their statistical
knowledge and problem-solving skills by conducting a Six Sigma project.
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 (DALE; WIELE;
IWAARDEN, 2007) briefly defines the DMAIC phases as follows:
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 and providing a structure to evaluate current performance as well as
assessing, comparing and monitoring subsequent improvements and their
capability.
Analyze – this stage centers in determining the root cause of
problems (defects), understanding why defects have taken place as well as
comparing and prioritizing opportunities for advance betterment (ADAMS; GUPTA;
WILSON, 2003).
Improve – this step focuses on the
use of experimentation and statistical techniques to generate possible
improvements to reduce the amount of quality problems and/or defects.
Control – finally, this last stage within the DMAIC process ensures
that the improvements are sustained and that ongoing performance is monitored.
Process improvements are also documented and institutionalized.
DMAIC resembles the Deming’s
continuous learning and process improvement model PDCA (plan-do-check-act) (DEMING,
1993). Within the Six Sigma’s approach, 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 “doing” (i.e. executing improvement
activities), also 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 side enterprises (SMEs) (GARZA-REYES
et al., 2010).
2. SIX SIGMA AND DMAIC APPLICATION
DMAIC is a data-driven quality
strategy used to improve 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.
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? |
DMAIC
procedure is apply to our project for better tools and techniques used in the
driven line for reducing defect rate.
2.1.
Data
Here we select 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 at 31st January 2017. The project was TQM base. All
parties’ involvement to reduce the project defect rate less than 2% is our goal
which will impact our quality and efficiency.
There were some tools and techniques
use for the defect reduction project. Some six sigma tools help to reduce the
defect by using proper SOP of these tools. The project mainly follows DMAIC
process. In individual stages of DMAIC six sigma tools helps to clear the path
of the defect reduction.
2.2.
Background
of the study
First the line defect rate is more
than 60% whereas the project defect rate is 43% respectively. Because of all
buyer 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 have to fix up on an average 2% defect rate
in a line or factory. It was found from the statistical analysis.
2.3.
Assumption
Our focusing point is operator as a
first QI. Individual process checking is much easier rather than whole body
checking. Also, here TLS (traffic light system) maintained properly. We also
focused on preventive maintenance of the machine by maintaining various machine
components life time. The project defect root-cause would be identified on time
and involve all party’s awareness. It will be helpful to reduce the defect
rate.
2.4.
Scope
The scope would be identified from
the fishbone diagram. Here 6M’s (Men, Machine, Material, Mother Nature, Method
& Measurement) involved to analyze the project. It will help to reach the
root-cause direction. It will much easier to problem finding and solving.
2.5.
Milestone
The desired goal is to achieve
project defect rate less than 2%. Also, the milestone is to complete the
project within desired time so that all project related work is visualized by
the Gantt chart accordingly.
2.6.
Impact
statement
If the Defect rate is decreasing day
by day, the required output will increase much higher than the present
situation or vice-versa. Also, it would be helped to improve the workers
performance as well as line efficiency.
2.7.
Success
Measurement
The reduction of project defect rate
increasing success of this project. The measurement of defect rate would be
calculated from the overall line defect through entry into the excel data
sheet. This measurement sheet also helps to calculate the DPMO of the project.
It would be helpful to know the line sigma level. Also, apply to know the process capability and sigma
level of the project too.
2.8.
Implementation
of Six Sigma
Six Sigma can be a great success or
failure, depending on how it is implemented. Implementation strategies can vary
organization to organizations, depending on their distinct culture and
strategic business goals. After completing a needs assessment and deciding to
implement Six Sigma, an organization has two basic options: Implement a Six
Sigma program or initiative and create a Six Sigma infrastructure.
2.9.
The
Metrics of Six Sigma
Much confusion exists relative to
the metrics of Six Sigma. The sigma level (that is, sigma–quality level)
sometimes used as a measurement within a Six Sigma program includes a ±1.5s
value to account for typical shifts and drifts of the mean, where s is the
standard deviation of the process. This sigma–quality level relationship is not
linear. In other words, a percentage unit improvement in parts per million
(ppm) defect rate (or defect per million opportunities [DPMO] rate) does not
equate to the same percentage improvement in the sigma–quality level. Three
common measures of process performance are - Defects per Unit (DPU),
Defects per Million Opportunities (DPMO) and Parts per Million Defective (PPM).
The key to understanding the difference between these terms is to understand
the difference between a defect and a defective item:
A defect refers to a flaw or
discrepancy on an item where more than one flaw (defect) can be found. For
example, a hospital admission form contains several fields of information that
can be missing or incorrect, so a given form can have more than one defect.
This means that a sample of 10 forms can show more than 10 defects.
An item is said to be defective when
the decision is made that the item is not acceptable, based either on one
characteristic or the accumulation of multiple defects. This means that a
sample of 10 items can show a maximum 10 defective units.
Defects per unit (DPU) – the average
number of defects per unit of product.
(1)
For example, when 26 defects (flaws)
are found on 10 units of product, the DPU is 26/10 or 2.6 defects per unit.
Defects per Million Opportunities
(DPMO) – a ratio of the number of defects in 1 million opportunities when
an item can contain more than one defect. To calculate DPMO, you need to know
the total number of defect opportunities.
Parts
per Million Defective (PPM) –
the number of defective units in one million units. (PPM is typically used when
the number of defective products produced is small so that a more accurate
measure of the defective rate can be obtained than with the percent defective).
2.10.
Process
Capability ()
Process
Capability is one of best tools for determining six sigma by continuous
improvement process. Process capability means that how the process is capable
to do its job. To obtain the efficiency we have to measure the capability of the
respective process which one has been performed by the machine or operator.
Every process has been done its own capability by the machine or operator
performance. It has a statistical formula based on USL (Upper specification
limit) and LSL (Lower specification limit). If we want to determine the value, then we should guess the ULS and LSL.
They are not a fixed value it will vary from process to process and phenomenon
to phenomenon. It depends on the respective person who will deal with in quality
issue or production team. The formula is given below-
Where,
is the standard deviation.
2.11.
Process
Capability Index ()
Process
capability index is referred to . It means
the expected process capability around the target value. Hence, we can say that
is nothing but the quality index. Here,
(6)
And
(7)
Where,
and are the mean and standard deviation. Now we
can write the formula of as,
(8)
It
is known as the process capability index. If we want to reach in 6 sigma, then
we have to more take care of every process and on its capability. It is one of
the paramount statistical continuous improvement measuring tools by which we
can know the three things together such as process capability, variability and
sigma. Evrey businessmen or manufacturers desire 1.33 Cpk and 6 sigma in the
long run.
3. METHODOLOGY
In
our defect reduction project, we use the DMAIC procedure to know the desired
sigma level as well as our defect rate position. It also helps us for further
improvement and what type of way we should follow the project. It is very
important to know the level of project completion so that DMAIC procedure helps
us to know the level of the project. Here every phase of the DMAIC has
different tools which we apply for go to the sustainable phase for controlling
the desired outcome of the project. Either it was defect rate or processes. The
project have run on three months which also visualize by gantt chart.
The
DMAIC process easily lends itself to the project approach to quality
improvement encouraged and promoted by Juran (JURAN; GODFREY, 1992). The
flowchart shows the DMAIC project how could go for right track and this chart
help us for further decision what it is (BORROR, 2009).
In
this project DMAIC procedure followed for how quality of the garments can be
increased. It helps the sort of the project daily where it is and how can go
for further initiative. To meet the objectives of the project various quality
tools and statistical anaysis has been done at the different stages. At first
we have dealed with normal data sheet so that we we want to know the situaton
of initial defect rate and along with
the defects terms which defects are most occuring and find the root causes of
that defects.
Figure 1: Flow chart of DMAIC project.
Besides
ANOVA and regression analysis have been apllied to narrow the causes by using
hypothesis and further action has been taken to resolve the defects from the
production line to enhence the productivity and improve quality of the finished
products. The regression model is given as,
Where,
is the dependent variable, and is the coefficients, is the error term.
4. RESULTS AND DISCUSSION
The
voice of the customer (VOC) concept, which means identifying what the customers
want and serving priorities to their needs, 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
organization.
A
project summary, which is a tool used to document the targets of the project
and other parameters at the outset 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.
Table 1: Summary of
the project.
Project
Title: |
Defects reduction in garment products |
Background and reasons for selecting the project: |
Huge 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 experience shop-floor operator and the
improvement project leader. |
Expected
Financial Benefits: |
A considerable cost saving due to the defects reduction. |
Expected
Customer Benefits: |
Receiving the product with the expected quality. |
Table2: 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
Pareto
analysis was carried out to identify the utmost occurring defects and priorities
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 2.
The Pareto chart shown in Figure 2 indicated that the highest rate of defects
was caused by broken stitch which contributed to over 48.52 percent of the
overall number of defects.
Therefore,
the improvement team and 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 2:
Pareto chart for defect.
The
figure 3 shows that initial line Defect Rate(DR) was too high that is 64 to 62
parcent and which was gradually decreasing day after day within one month.
Finally it shows the 24 parcent defect rate at the end of one month.
Figure
3: Project line defect rate (DR) before
implimentation.
The
figure 4 shows that initial project Defect Rate (DR) was too high that is 43 to
39 parcent and which was gradually decreasing day after day within one month.
Finally it shows the 7 parcent defect rate at the end of one month.
Figure 4:
Project defect rate (DR) before implimentation.
The
figure 6 shows that initial line Defect Rate(DR) was too high that is 47 to 43
parcent and which was gradually decreasing day after day within the dead line.
Finally it shows the 14 parcent defect rate at the end of the project dead
line.
Figure 5:
Cause and effect diagram for scope area.
The
figure 7 shows that initial project Defect Rate(DR) was too high that is 17 to
14 parcent and which was gradually decreasing day after day within the dead
line. Finally it shows the 2 parcent defect rate at the end of the project dead
line.
Figure 6:
Line defect rate (DR) after impimentation DMAIC.
The
figure 8 shows that the initial Sigma level of the project was defined 1.7 and
also shows that it is increasing day bay day after implimenting necessary steps
for defect reduction project. At the end of the project is being seen that we
have achived the 3.4 Sigma which one is good but not best.
Figure 7:
Project defect rate(DR) after impimentation DMAIC.
Figure 8:
Project Sigma level.
The
figure 8 shows that the another tool for reduction the process variablity 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
variabilty is so high besides Z(sigma) is also about 2.88 too low. Evrey
businessmen or manufacturers desire 1.33 Cpk.
Figure 9:
Process Capability(Cpk)& Z(Sigma).
Analysis
of variance (Table 3) tells that the overall variation is accounted by the
average response variables. Above analysis shows that the assume hypothesis is
statistically significant to be P-value < 0.05. So, there is a significant
effect among the full process. Another hypothesis tells the mean difference
among the individual treatment mean.
Table 3: Analysis of
Variance (ANOVA).
Source |
DF |
Adj SS |
Adj MS |
F-Value |
P-Value |
Defect |
4 |
93.53 |
23.383 |
7.60 |
0.000* |
Parts |
2 |
2.24 |
1.119 |
0.36 |
0.695 |
Process |
20 |
76.62 |
3.831 |
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
Some
treatments have a statistically significant mean difference effect that means
they are highly correlated to occur defect. From the Table 4, they are Broken
stitch, Open seam, Arm hole and Side pocket. The fitted regression model is,
Response =
2.246-0.658*Broken Stitch-0.531*Open Seam+0.750*Arm Hole+0.632*Side Pocket
Table 4: Fitting the
regression model.
Term |
Coefficient |
SE
Coefficient |
T-Value |
P-Value |
VIF |
|
Constant |
2.246 |
0.196 |
11.45 |
0.000** |
|
|
Defect |
Broken
Stitch |
-0.658 |
0.177 |
-3.71 |
0.000** |
2.21 |
Open
Seam |
-0.531 |
0.240 |
-2.21 |
0.027 |
2.89 |
|
Puckering |
0.223 |
0.487 |
0.46 |
0.648 |
5.89 |
|
Skip
Stitch |
-0.020 |
0.210 |
-0.09 |
0.926 |
2.65 |
|
Parts |
Inside |
-0.148 |
0.173 |
-0.85 |
0.394 |
2.48 |
Shell
Part |
0.091 |
0.177 |
0.52 |
0.607 |
3.26 |
|
Process |
Arm
Hole |
0.750 |
0.277 |
2.71 |
0.007** |
2.29 |
Back
Side |
-2.32 |
1.69 |
-1.38 |
0.170 |
30.09 |
|
Collar |
-0.308 |
0.360 |
-0.85 |
0.393 |
2.66 |
|
Cuff |
0.170 |
0.427 |
0.40 |
0.690 |
3.24 |
|
Eyelet/Button |
-0.071 |
0.992 |
-0.07 |
0.943 |
11.12 |
|
Front
Side |
1.68 |
1.20 |
1.39 |
0.164 |
15.81 |
|
Hem |
0.188 |
0.467 |
0.40 |
0.688 |
3.50 |
|
Hood |
-0.035 |
0.340 |
-0.10 |
0.918 |
2.66 |
|
Label
Main/Care |
-0.50 |
1.20 |
-0.42 |
0.675 |
15.78 |
|
Side
pocket |
0.632 |
0.362 |
1.75 |
0.041** |
3.02 |
|
Loop |
0.610 |
0.726 |
0.84 |
0.402 |
6.55 |
|
Neck
JNT/TS |
-0.485 |
0.584 |
-0.83 |
0.407 |
4.61 |
|
Placket |
0.407 |
0.314 |
1.30 |
0.196 |
2.70 |
|
Pocket |
-0.472 |
0.333 |
-1.42 |
0.157 |
2.74 |
|
Side
Seam |
-0.291 |
0.533 |
-0.55 |
0.585 |
4.03 |
**5% level of Significance
5. 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 in order to reduce the defect rate at 2% which is the main
objective of the project and to increase the productivity and quality goods.
To that end, 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 also 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, our project dead line we
have been able to achieve the desired 2% defect rate. Finally, we can say that
all types off assignable causes are able to control by reducing defect 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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