Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Statistical Quality Control Approach in Typical Garments
Manufacturing Industry in Bangladesh: A Case Study
*
Md. Mohibul Islam and **Md. Mosharraf Hossain
Garments industry is the most important economic sector in
Bangladesh. It earns a lot of foreign currency annually by exporting
garments. To export garments in global market generally it faces high
competition on regarding on the product price with comparing to
neighboring countries. To survive in the global market and proceeds, it
is now urgently necessary to manufacture the garments in most
economical way. There are various cost factors that are related with
manufacturing garments among them quality inspection cost is one of
them. Traditionally it is noted that most of the garments factory in
Bangladesh is practicing100% end line quality inspection to maintain
the quality. It is exercised both of the manufacturing section and the
finishing section. For this 100% inspection there are needed both time
and a large number of quality inspectors who are consumed high
currency. In this case study a sampling based statistical quality control
system is proposed in finishing section to eliminate 100% inspection
by sampling based inspection.
Keywords: Statistical quality controls, control chart, run test, sampling, inspection
and competitive global market.
1. Introduction
The garments manufacturing industry is a large and most export oriented field of
Bangladesh in terms of output, export and employment. At present these
manufacturing industries are earning foreign currency about three quarters of total
exports and the industry is a symbol of the country‟s dynamism in the world
economy. Manufacturing cost of a garments products partially depend on quality
inspection cost. The survival competition in term of manufacturing cost versus selling
price of these fields is increasing day by day in competitive global market. As
knitwear industry is labor incentive field so there is little chance to improve these field
by technological change rather there are vast scopes of improvement of these areas
by applying various scientific approach. It is noted that according to lean
manufacturing notion quality inspection is a necessary but non value added activity
so it is needed to reduce. It may be possible to reduce this non value added activities
from the manufacturing processes by applying statistical quality control system. As
this Industry is a symbol of the country‟s economy and there are ample opportunities
to improve this field so the authors feel great interest to work this area. Comfit
Composite Knit Limited is one of the most typical knitwear composite factories in
Bangladesh where garments sewing & finishing section are available. Sewing and
finishing sections are the most important section of this factory.
*
Md. Mohibul Islam, Department Industrial and Production Engineering, Rajshahi University of
Engineering and Technology, Rajshahi 6204, Bangladesh, Email: mohibul05ipe@yahoo.com
**Md. Mosharraf Hossain, Department of Industrial and Production Engineering, Rajshahi University
of Engineering and Technology, Rajshahi 6204, Bangladesh, Corresponding Author, Email:
mosharraf80@yahoo.com
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
In this study it is found in current system for checking quality of the product 100%
inspection is done both the sewing and finishing section. It is not only time
consuming but also expensive for the product. Under considering these factors the
authors feel great interest to implement the statistical quality control system in
finishing section of this factory and check the quality of the product regarding on
sampling based.
2. Literature Review
C.N. Nnamani1, and S.H. Fobasso1 (2013) have mentioned that in the
manufacturing environment, quality improves reliability, increases productivity and
customer satisfaction. Quality in manufacturing requires the practice of quality
control. Under this paper he investigated the level of quality control in Lifespan
pharmaceutical limited, makers of Lifespan Table Water. His study involves
inspection of some randomly selected finished products on daily bases. By using
attributes control chart (P-chart and NP-chart) he shown that there are many points
that fall out of the control limits and finally comment that the process is out of control
and need to process verification. But it is noted that in this paper process potential
index did not find out and also nature of data pattern did not checked, is it random or
nonrandom but this will be mentioned in this paper. Rallabandi srinivasu, g.
Satyanarayana reddy and srikanth reddy rikkula (2011) have mentioned that
Statistical Process Control (SPC) methods have been widely recognized as effective
approaches for process monitoring and diagnosis. Statistical process control
provides use of the statistical principals and techniques at every stage of the
production. Statistical Process Control (SPC) aims to control quality characteristics
on the methods, machine, products, equipments both for the company and operators
with magnificent seven. Some simple techniques like the “seven basic quality control
(QC) tools” provide a very valuable and cost effective way to meet these objectives.
They have mentioned the importance of statistical quality control approach
theoretically and mentioned various statistical QC tools but they have not
implemented a specific tool in manufacturing environment practically. Actually their
paper is only theory based. But this paper not only focus theoretical statement but
also practical implementation.
George Utekhin (2008) has published a paper which was made with a purpose to
analyses statistical technique using in quality management Practices. 34 enterprises
and organizations have been analyzed. He has analyzed the various quality related
problem regarding on ISO 9001: 2000 and find out the arena where statistical
technique may be used to resolve the problem. This paper has mentioned only areas
where Statistical technique may apply but any specific tool has not practiced. S.
Raghuraman et al.(2012) have reviewed the history of Statistical tools such as
Control charts, Histogram, Cause and Effect diagram combined with both Statistical
Process Control and Process capability Indices and how these are helpful in
enhancing the process by continuous monitoring through inspection of the samples.
But in this paper practical data is absence and it has emphasized only theoretical
importance of statistical tools. Dorde Vukelic et al. (2008) have published a paper
where importance is emphasized for applying statistical quality control methods to
evaluation of process stability and capability. There is a preview of structure and
functioning of the developed applicative software for statistical process control. In
2
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
this paper practical data is incorporated clearly but one thing that in case of control
chart, the nature of randomness of used data pattern did not check by any test and
that will be done in this paper and it is the distinct characteristic of this paper from
others. From this literature review it is observed that both of the quality control chart
and its data pattern analysis is not done at a time by these references papers. So,
the authors feel interested to do this work. The reminder of this paper is organized as
follows. Section 3 & 4 defines the research methodology and mathematical
statement, section 5 defines the data collection and result analysis. Discussion is
mentioned at section 6. Finally conclusion have mentioned at section 7 of this paper.
3. Research Methodology
Implementation of sampling based quality control system is the main essence of this
study. To achieve this objective existing sewing and finishing section is critically
observed on regarding on quality aspects then the process potential index (cp) is to
be determined to check the process capability to produce goods at right dimensions.
After justifying process capability, variable control charts ( ̅ and ̅ ) are to be drawn
regarding on various dimensions such as product length, width etc. After drawing the
control chart the data pattern is to be checked by run test to justify the randomness
properties of the taken data within the lower and upper control limit. Finally a
sampling based quality control system is to be proposed for checking quality
instead of 100% inspection.
4. Mathematical Statement
To complete this study the mathematical statements which are to be used are
mentioned below4.1 Process Potential Index, (Cp)
Where, cp =
=
̅
Here, Allowable process spread = Upper specification limit- Lower specification limit
= USL- LSL, ̅ = sample standard deviation and 𝛔 = population standard deviation.
4.2 Variable Control Chart
This chart is being developed with respect to dimension of length and width of the
products. Mean of the length or width is to be expressed by center line and their
upper and lower limits are to be expressed by upper specification limit and lower
specification limit. Center Line (mean),
̅ = µ = ̿,
̅ = ̿ +3𝛔,
̅ = ̿ - 3𝛔, for ̅
̅
̅
̅
chart: 3sigma control chart, CLR = , UCLR = D4, LCLR = D3
4.3 Run Test
Basically run test is to be done for checking the randomness property of the data
pattern. If the data pattern stay between the USL and LSL but follow a specific
pattern periodically then it is an indication that there exit some assignable causes
that are responsible for occurring this periodic pattern. These assignable causes
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
then should be found out. When it is examined that the data that stay between USL
and LSL and any kind of pattern does not follow with respect to various no. of
sample then it is an indication that the process is running well and there exit only
natural variation. To determine whether any patterns are present in control chart
data, one must transform the data into both above (As) and below (Bs) and up (Us)
and down (Ds), and then count the number of runs in each case. These numbers
must then be compared with the number of runs that would be expected in a
completely random series. For both the median and the up/down run tests, the
expected number of runs is a function of the number of sample in the series. The
formulas are, E(r) med = +1, E(r) u/d =
, Where, N is the number of sample, and
E(r) is the expected number of runs. The actual number of runs in any given set of
observations will vary from the expected number, due to chance. Chance variability
is measured by the standard deviation of runs. The formulas are,
=√
=√
,
, in practice, it is often easiest to compute the number of standard
deviations, z, by which an observed number of runs differs from the expected
number. This z value would then be compared to the value ±3 (z for 99.73%), z that
exceeds the desired limits indicates patterns are present. The computation of z takes
the form, ztest =
, For median and up/down
tests, one can find z using these formulas-, Median: z =
(
√
)
( )
√
, Up and down: z =
, Where, N = Number of sample. r = Observed number of runs of either
As and Bs or Us and Ds, depending on which test is involved.
5. Data Collection and Result Analysis
Data has been collected from Comfit Composite Knit Limited from its garments
finishing section. Product name: Knitwear Polo shirt, product style: Nap-20, Order
No. 8001-007, Buyer: C & A, and product size medium.
From table (1), sample standard deviation ̅: 0.32, Therefore population standard
deviation 𝛔 = ̅ √ = 0.32*√ = 0.72, Cp =
=
= 1.30, for ̅ 3sigma
̅
control chart obtained: CL = 72.29, UCL = CL+3 σ = 72.29+3*0.72 = 74.43, LCL =
CL-3 σ = 72.29-3*0.72 = 70.14, CLR = ̅ = 1.7, UCLR = ̅ D4, = 1.7*2.114 = 3.60,
LCLR = ̅ D3 = 1.7*0 = 0
From table (2), sample standard deviation ̅: 0.38, Therefore population standard
deviation 𝛔 = ̅ √ = 0.38*√ = 0.84, Cp =
=
= 1.09, CL = 44.22,
̅
UCL = CL+3 σ = 44.22+3*0.84 =46.74, LCL = CL-3 σ = 44.22-3*0.84 = 41.7, CLR = ̅
= 1.7, UCLR = ̅ D4, = 1.7*2.114 = 3.60, LCLR = ̅ D3 = 1.7*0 = 0
From table (3), sample standard deviation ̅: 0.64, Therefore population standard
deviation 𝛔 = ̅ √ = 0.64*√ = 1.43, Cp =
=
= 1.04, CL = 89.86, UCL
̅
4
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
= CL+3 σ = 89.86+3*1.43 = 94.15, LCL = CL-3 σ = 89.86-3*1.43 = 85.57, CLR = ̅ =
3.1, UCLR = ̅ D4, = 3.1*2.114 = 6.55, LCLR = ̅ D3 = 3.1*0 = 0
From table (4), Sample standard deviation ̅: 0.26, Therefore population standard
deviation 𝛔 = ̅ √ = 0.26*√ = 0.58, Cp =
=
= 1.60, CL = 22.24, UCL
̅
= CL+3 σ =22.24+3*0.58 = 23.98, LCL = CL-3 σ = 22.24-3*0.58 = 20.5, CLR = ̅ =
1.4, UCLR = ̅ D4, = 1.4*2.114 = 2.96, LCLR = ̅ D3 = 1.4*0 = 0From table (5): sample
standard deviation ̅ : 0.29, Therefore population standard deviation 𝛔 = ̅ √ =
0.29* √ = 0.64, Cp =
=
= 1.43, CL = 15.08, UCL = CL+3 σ =
̅
15.08+3*0.64 = 17.00, LCL = CL-3 σ = 15.08-3*0.64 = 13.16, CLR = ̅ = 1.3, UCLR =
̅ D4, = 1.3*2.114 = 2.75, LCLR = ̅ D3 = 1.3*0 = 0
From table (6): sample standard deviation ̅: 0.36, Therefore population standard
deviation 𝛔 = ̅ √ = 0.36*√ = 0.80, Cp =
= 1.16, CL = 17.98,
̅
UCL = CL+3 σ = 17.98+3*0.80 = 20.38, LCL = CL-3 σ = 17.98-3*0.80 = 15.58, CLR =
̅ = 1.2, UCLR = ̅ D4, = 1.2*2.114 = 2.54, LCLR = ̅ D3 = 1.2*0 = 0
From table (7 and 8): For product body length, observed, r = A/B = 19 runs and r =
U/D =16 runs, for product body width, observed, r = A/B = 15 runs and r = U/D =13
runs, for product sleeve length, observed, r = A/B = 14 runs and r = U/D =17 runs, for
product sleeve width, observed, r = A/B = 16 runs and r = U/D =12 runs, for product
neck length, observed, r = A/B = 14 runs and r = U/D =17 runs, for product neck
width, observed, r = A/B = 16 runs and r = U/D =11 runs,
N
1
2
3
4
5
̅
72.7
72.4
72.0
72.4
72.4
Table (1): Actual requirement of product body length: 73.5-71cm.
N
N
N
N
̅
̅
̅
̅
1.0
6 72.9 1.5 11 72.3 2.5 16 72.4 2.0 21 72.4
2.0
7 71.8 1.5 12 72.1 1.5 17 72.5 2.0 22 72.4
1.5
8 72.5 1.5 13 72.0 1.5 18 72.5 2.0 23 72.4
1.0
9 72.1 2.0 14 72.2 2.0 19 71.9 1.5 24 72.2
1.0 10 71.8 1.5 15 72.1 2.0 20 72.6 1.5 25 72.3
1807
∑
̅ and ∑
∑
∑
̅
72.29
̿=
and ̅ =
1.5
2.0
2.5
2.0
1.5
43
1.7
̅ Control Chart for product length is mentioned below-
̅ Control Chart for product length is mentioned below5
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
N
1
2
3
4
5
̅
45.1
43.9
44.6
43.9
43.8
Table (2): Actual requirement of product body width: 45.5-43cm.
N
N
N
N
̅
̅
̅
̅
1.5
6
44 3.5 11 43.7 1.5 16 44.3 2.0 21 44.6 2.0
2.0
7 44.3 1.5 12 43.7 2.5 17 43.9 1.5 22 43.9 1.0
2.0
8 43.7 2.0 13 43.8 1.5 18 43.7 1.0 23 44.1 1.5
1.5
9 44.1 2.0 14 44.6 1.0 19 44.1 1.5 24 45.2 1.5
1.5 10 44.5 1.0 15 44.6 1.0 20 45.1 1.5 25 42.5 2.0
1104 41.5
∑
̅ and ∑
∑
∑
̅
44.2 1.7
̿=
and ̅ =
̅ Control Chart for product width is mentioned below-
̅ Control Chart for product width is mentioned below-
6
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
N
̅
1
2
3
4
5
90.3
89.7
89.8
90.2
91.4
Table (3): Actual requirement of product sleeve length: 91-87cm.
N
N
N
N
̅
̅
̅
̅
1.5
3.0
3.5
4.0
10.0
6
7
8
9
10
90.1
89.5
89.9
89
89
3.5
3.0
3.0
3.0
2.5
11
12
13
14
15
89.4
89.9
89.2
90.3
89.1
2.0
3.5
3.0
3.5
2.5
16
17
18
19
20
89.2
89.1
90.3
90.0
90.7
∑
21
22
23
24
25
̅ and ∑
∑
̿=
3.0
3.0
1.5
3.0
2.5
̅
∑
and ̅ =
90.5
89.2
90.2
90.63
89.6
2246
89.86
1.5
2.5
2.0
2.5
3.5
76.5
3.1
̅ Control Chart for product sleeve length is mentioned below-
̅ Control Chart for product sleeve length is mentioned below-
Table (4): Actual requirement of product sleeve width: 23.5-21cm.
N
1
2
3
4
5
̅
22.5
22.1
22.5
22.0
22.7
1.0
1.5
2.5
1.0
1.5
N
6
7
8
9
10
̅
22.5
22.1
21.9
21.9
22.1
2.0
1.5
2.0
1.0
1.0
N
11
12
13
14
15
̅
22.2
22.3
22.5
22.6
22.2
1.0
1.5
1.5
1.0
1.0
̅
N
16
17
18
19
20
∑
22.2
22.2
21.9
22
22.1
1.5
1.0
2.0
1.5
1.5
N
21
22
23
24
25
̅
12.3
12.7
13.3
13.5
13.9
1.0
2.0
0.5
1.0
1.0
̅ and ∑
555.7 31.0
̿=
∑
̅
and ̅ =
∑
22.24
1.4
7
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
̅ Control Chart for product sleeve width is mentioned below-
̅ Control Chart for product sleeve width is mentioned below-
Table (5): Actual requirement of product neck length: 16.5-14cm.
N
1
2
3
4
5
̅
15.8
14.9
15.4
15.1
15.2
0.5
1.5
2.0
1.5
1.0
N
6
7
8
9
10
̅
15.8
15.2
14.6
15.3
14.7
1.0
1.5
1.0
1.5
2.0
N
11
12
13
14
15
̅
15.3
15.5
15.2
14.8
14.9
1.0
1.5
1.0
1.0
1.0
̅
14.4
15.0
15.0
14.6
14.6
N
16
17
18
19
20
∑
̿=
∑
1.0
1.5
1.0
1.0
1.0
N
21
22
23
24
25
̅
15.0
15.0
15.2
15.4
15.1
1.5
1.5
1.5
1.5
1.5
̅
377
33
15.08
1.3
̅ and ∑
and ̅ =
∑
̅ Control Chart for product neck length is mentioned below-
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
̅ Control Chart for product neck length is mentioned below-
Table (6): Actual requirement of product neck width: 19.5-17mm.
N
1
2
3
4
5
̅
18.0
18.2
18.2
18.2
18.0
0.0
2.0
1.0
2.0
0.0
N
6
7
8
9
10
̅
18.0
17.8
17.8
18.2
18.6
2.0
2.0
2.0
2.0
2.0
N
11
12
13
14
15
̅
17.8
18.2
19.0
18.0
18.0
2.0
2.0
0.0
0.0
0.0
̅
N
16
17
18
19
20
∑
17.6
17.4
17.0
17.6
17.8
1.0
1.0
0.0
1.0
2.0
̅
N
21
22
23
24
25
17.8
18
18.2
18.2
18.2
2.0
0.0
2.0
1.0
2.0
̅ and ∑
449.8 31
̿=
∑
̅
and ̅ =
∑
17.98 1.2
̅ Control Chart for product neck width is mentioned below-
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
̅ Control Chart for product neck width is mentioned below-
Summary of the data analysis for CP, CL, UCL and LCL, (from table: 1-6)
Product's
̅
USL LSL
cp
̅
Dimension
Product
Length
73.5 71
0.32 1.30 72.29
(cm)
Product
Width
45.5 43
0.38
1.09 44.22
(cm)
Sleeve
Length
91
87
0.64 1.43 89.86
(cm)
Sleeve
Width
23.5 21
0.26 1.60 22.24
(cm)
Neck
Length
16.5 14
0.29 1.60 15.08
(cm)
Neck
Width
19.5 17
0.36 1.43 17.98
(mm)
̅
̅
̅
̅
̅
74.43
70.14
1.70 3.60
0
46.74
41.7
1.70 3.60
0
94.15
85.57
3.10 6.55
0
23.98
20.5
1.40 2.96
0
17
13.16
1.30 2.75
0
20.38
15.58
1.20 2.54
0
10
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Data analysis for run test:
Table (7): Counting above and below median runs
Sample
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Body Length
x-bar A/B U/D
72.7
A
72.4
B
D
72
B
D
72.4
B
U
72.4
B
U
72.9
A
U
71.8
B
D
72.5
A
U
72.1
B
D
71.8
B
D
72.3
B
U
72.1
B
D
72
B
D
72.2
B
U
72.1
B
D
72.4
B
U
72.5
A
U
72.5
A
U
71.9
B
D
72.6
A
U
72.4
B
D
72.4
B
U
72.4
B
U
72.2
B
D
72.3
B
U
Body Width
x-bar A/B U/D
45.1
A
43.9
B
D
44.6
A
U
43.9
B
D
43.8
B
D
44.0
B
U
44.3
A
U
43.7
B
D
44.1
B
U
44.5
A
U
43.7
B
D
43.7
B
U
43.8
B
U
44.6
A
U
44.6
A
U
44.3
A
D
43.9
B
D
43.7
B
D
44.1
B
U
45.1
A
U
44.6
A
D
43.9
B
D
44.1
B
U
45.2
A
U
42.5
B
D
Sleeve Length
x-bar A/B U/D
90.3
A
89.7
B
D
89.8
B
U
90.2
A
U
91.4
A
U
90.1
A
D
89.5
B
D
89.9
B
U
89.0
B
D
89.0
B
U
89.4
B
U
89.9
B
U
89.2
B
D
90.3
A
U
89.1
B
D
89.2
B
U
89.1
B
D
90.3
A
U
90.0
A
D
90.7
A
U
90.5
A
D
89.2
B
D
90.2
A
U
90.6
A
U
89.6
B
D
Summary of the data analysis of median run test of A/B and U/D) (from table: 7-8)
σmed =
z=
σu/d =
z=
E(r) u/d
E(r) med =
Product's
( )
(
)
+1
=
Dimension
√
√
√
Product
Length (cm)
Product
Width (cm)
Sleeve
Length (cm)
Sleeve
Width (cm)
Neck
Length (cm)
Neck Width
(mm)
√
13.5
2.45
2.25
16.33
2.03
-0.163
13.5
2.45
0.61
16.33
2.03
-1.64
13.5
2.45
0.20
16.33
2.03
0.33
13
2.45
1.02
16.33
2.03
-2.13
13.5
2.45
0.20
16.33
2.03
0.33
13.5
2.45
1.02
16.33
2.03
-2.62
11
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Data analysis for run test:
Table (8) : Counting above and below median runs
Sample
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Sleeve width
x-bar A/B U/D
22.5
22.1
22.5
22.0
22.7
22.5
22.1
21.9
21.9
22.1
22.2
22.3
22.5
22.6
22.2
22.2
22.2
21.9
22.0
22.1
22.5
22.6
22.2
21.9
22.0
A
B
A
B
A
A
B
B
B
B
B
A
A
A
B
B
B
B
B
B
A
A
B
B
B
D
U
D
U
D
D
D
U
U
U
U
U
U
D
U
U
D
U
U
U
U
D
D
U
Neck Length
xA/B U/D
bar
15.8
A
14.9
B
D
15.4
A
U
15.1
B
D
15.2
A
U
15.8
A
U
15.2
A
D
14.6
B
D
15.3
A
U
14.7
B
D
15.3
A
U
15.5
A
U
15.2
A
D
14.8
B
D
14.9
B
U
14.4
B
D
15.0
B
U
15.0
B
D
14.6
B
D
14.6
B
D
15.0
B
U
15.0
B
D
15.2
A
U
15.4
A
U
15.1
B
D
Neck Width
xA/B U/D
bar
18.0
A
18.2
A
U
18.2
A
U
18.2
A
U
18.0
A
D
18.0
A
U
17.8
B
D
17.8
B
U
18.2
A
U
18.6
A
U
17.8
B
D
18.2
A
U
19.0
A
U
18.0
A
D
18.0
A
U
17.6
B
D
17.4
B
D
17.0
B
D
17.6
B
U
17.8
B
U
17.8
B
U
18.0
A
U
18.2
A
U
18.2
A
U
18.2
A
U
6. Discussion
It is known that if the process potential index cp > 1then it is assumed that process is
capable to produce the product with specification limit. From practical data analysis it
is observed that the process potential index cp is always greater than 1. So the
considered production process is capable to produce the product within control limit
i.e. as per requirement. It is also noticed that after observing control chart for ̅ and ̅
for µ ±3𝛔 control limit, all observed data points stay within the upper and lower
control limit that indicate the process is in control. Again from the run test (where z =
± 3) it is found that all ztest values both zA/B and zu/d are exist within the range ±3 it
indicate that median test above/below and up/down test do not reveal any pattern. It
exposed that data patterns are randomly exist within the upper and lower control
limit. So, from this analysis it is found that the production process is capable to
produce the product as per the requirement. Hence, the proposed sampling based
quality control approach may be applied under this considered manufacturing
environment instead of 100% inspection.
12
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
7. Conclusion
Now a day, it is really a great challenge for the garments manufacturer to
manufacture the garments economically facing varieties risks in Bangladesh. This
Industry is mostly based on the human performance and little chance to develop the
technical aspect. For this reason time is the major constraint to utilize the workforce
with limited resources. This problem may be alleviated by following a better scientific
approach. In the essence of this study is recommended that sampling based quality
control approach is working well in Comfit Composite Knit Limited and should be fully
practice at their factory and may be suggested to other garments manufacturing
industries of Bangladesh.
References
Nnamani ,C.N.and S.H. Fobasso 2013, „Statistical Quality Control of Manufactured
Products (Case Study of Packaging at Lifespan Pharmaceutical Limited)‟,
International Journal of Pure and Applied Sciences and Technology, ISSN 2229
– 6107, 15(1), pp. 20-30.
Dorde Vukelic, Janko Hodolic, Tone Vrecic and Peter Kogej 2008, “Development of
a System for Statistical Quality Control of the Production Process”, Mechanical
Engineering, Vol. 6, No 1, 2008, pp. 75 – 90.
George Utekhin 2008, „Use of statistical techniques in quality management systems‟,
The 8th International Conference “Reliability and statistics in transportation and
communication - 2008”
Benneyan, JC, Lloyd, RC and Plsek, P E 2003, “Statistical process control as a tool
for research and healthcare improvement”, Quality Improvement Research,
2003; 12:458–464
Rallabandi srinivasu, Satyanarayana reddy, G and srikanth reddy rikkula 2011,
„Utility of quality control tools and Statistical process control to improve the
Productivity and quality in an industry‟, International Journal of Reviews in
Computing, ISSN: 2076-3328, 3vol5.
Raghuraman,S, Thiruppathi,K, Praveen Kumar, J and Indhirajith, B
2012,
„Enhancement of quality of the processes using Statistical tools- a review‟,
International Journal of Engineering Science & Advanced Technology, Volume2, Issue-4, 1008 – 1017.
Sotiris Bersimis, Stelios Psarakis and John Panaretos 2006, “Multivariate Statistical
Process Control Charts: An Overview”, Quality and Reliability Engineering
International, vol. 3, pp. 517–543.
William H. Woodall 2000, „Controversies and Contradictions in Statistical Process
Control‟, Journal of Quality Technology, October 12–13, 2000.
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