Statistical Quality Control Approach in Typical Garments Manufacturing Industry in Bangladesh: A Case Study

Transcription

1 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, mohibul05ipe@yahoo.com **Md. Mosharraf Hossain, Department of Industrial and Production Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh, Corresponding Author, mosharraf80@yahoo.com 1

2 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

3 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 (c p ) 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 below- 4.1 Process Potential Index, (C p ) Where, c p = = 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 =, UCL R = D 4, LCL R = D 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 3

4 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, z test = tests, one can find z using these formulas-, Median: z = ( ), For median and up/down ( ), 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 , Buyer: C & A, and product size medium. From table (1), sample standard deviation : 0.32, Therefore population standard deviation σ = = 0.32* = 0.72, C p = = = 1.30, for 3sigma control chart obtained: CL = 72.29, UCL = CL+3 σ = *0.72 = 74.43, LCL = CL-3 σ = *0.72 = 70.14, CL R = = 1.7, UCL R = D 4, = 1.7*2.114 = 3.60, LCL R = D 3 = 1.7*0 = 0 From table (2), sample standard deviation : 0.38, Therefore population standard deviation σ = = 0.38* = 0.84, C p = = = 1.09, CL = 44.22, UCL = CL+3 σ = *0.84 =46.74, LCL = CL-3 σ = *0.84 = 41.7, CL R = = 1.7, UCL R = D 4, = 1.7*2.114 = 3.60, LCL R = D 3 = 1.7*0 = 0 From table (3), sample standard deviation : 0.64, Therefore population standard deviation σ = = 0.64* = 1.43, C p = = = 1.04, CL = 89.86, UCL 4

5 = CL+3 σ = *1.43 = 94.15, LCL = CL-3 σ = *1.43 = 85.57, CL R = = 3.1, UCL R = D 4, = 3.1*2.114 = 6.55, LCL R = D 3 = 3.1*0 = 0 From table (4), Sample standard deviation : 0.26, Therefore population standard deviation σ = = 0.26* = 0.58, C p = = = 1.60, CL = 22.24, UCL = CL+3 σ = *0.58 = 23.98, LCL = CL-3 σ = *0.58 = 20.5, CL R = = 1.4, UCL R = D 4, = 1.4*2.114 = 2.96, LCL R = D 3 = 1.4*0 = 0From table (5): sample standard deviation : 0.29, Therefore population standard deviation σ = = 0.29* = 0.64, C p = = = 1.43, CL = 15.08, UCL = CL+3 σ = *0.64 = 17.00, LCL = CL-3 σ = *0.64 = 13.16, CL R = = 1.3, UCL R = D 4, = 1.3*2.114 = 2.75, LCL R = D 3 = 1.3*0 = 0 From table (6): sample standard deviation : 0.36, Therefore population standard deviation σ = = 0.36* = 0.80, C p = = 1.16, CL = 17.98, UCL = CL+3 σ = *0.80 = 20.38, LCL = CL-3 σ = *0.80 = 15.58, CL R = = 1.2, UCL R = D 4, = 1.2*2.114 = 2.54, LCL R = D 3 = 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, Table (1): Actual requirement of product body length: cm. N N N N N and = and = Control Chart for product length is mentioned below- Control Chart for product length is mentioned below- 5

6 Table (2): Actual requirement of product body width: cm. N N N N N and = and = Control Chart for product width is mentioned below- Control Chart for product width is mentioned below- 6

7 Table (3): Actual requirement of product sleeve length: 91-87cm. N N N N N and = and = Control Chart for product sleeve length is mentioned below- Control Chart for product sleeve length is mentioned below- and = Table (4): Actual requirement of product sleeve width: cm. N N N N N and =

8 Control Chart for product sleeve width is mentioned below- Control Chart for product sleeve width is mentioned below- and = Table (5): Actual requirement of product neck length: cm. N N N N N and = Control Chart for product neck length is mentioned below- 8

9 Control Chart for product neck length is mentioned below- and = Table (6): Actual requirement of product neck width: mm. N N N N N and = Control Chart for product neck width is mentioned below- 9

10 Control Chart for product neck width is mentioned below- Summary of the data analysis for C P, CL, UCL and LCL, (from table: 1-6) Product's USL LSL c Dimension p Product Length (cm) Product Width (cm) Sleeve Length (cm) Sleeve Width (cm) Neck Length (cm) Neck Width (mm) 10

11 Data analysis for run test: Table (7): Counting above and below median runs Sample Body Length Body Width Sleeve Length No. x-bar A/B U/D x-bar A/B U/D x-bar A/B U/D A A A B D 43.9 B D 89.7 B D 3 72 B D 44.6 A U 89.8 B U B U 43.9 B D 90.2 A U B U 43.8 B D 91.4 A U A U 44.0 B U 90.1 A D B D 44.3 A U 89.5 B D A U 43.7 B D 89.9 B U B D 44.1 B U 89.0 B D B D 44.5 A U 89.0 B U B U 43.7 B D 89.4 B U B D 43.7 B U 89.9 B U B D 43.8 B U 89.2 B D B U 44.6 A U 90.3 A U B D 44.6 A U 89.1 B D B U 44.3 A D 89.2 B U A U 43.9 B D 89.1 B D A U 43.7 B D 90.3 A U B D 44.1 B U 90.0 A D A U 45.1 A U 90.7 A U B D 44.6 A D 90.5 A D B U 43.9 B D 89.2 B D B U 44.1 B U 90.2 A U B D 45.2 A U 90.6 A U B U 42.5 B D 89.6 B D Summary of the data analysis of median run test of A/B and U/D) (from table: 7-8) Product's E(r) med = σ med = z = E(r) σ u/d = z = u/d ( ) ( ) Dimension +1 = Product Length (cm) Product Width (cm) Sleeve Length (cm) Sleeve Width (cm) Neck Length (cm) Neck Width (mm)

12 6. Discussion Data analysis for run test: Table (8) : Counting above and below median runs Sleeve width Neck Length Neck Width Sample No. x- x- x-bar A/B U/D A/B U/D A/B U/D bar bar A A A B D 14.9 B D 18.2 A U A U 15.4 A U 18.2 A U B D 15.1 B D 18.2 A U A U 15.2 A U 18.0 A D A D 15.8 A U 18.0 A U B D 15.2 A D 17.8 B D B D 14.6 B D 17.8 B U B U 15.3 A U 18.2 A U B U 14.7 B D 18.6 A U B U 15.3 A U 17.8 B D A U 15.5 A U 18.2 A U A U 15.2 A D 19.0 A U A U 14.8 B D 18.0 A D B D 14.9 B U 18.0 A U B U 14.4 B D 17.6 B D B U 15.0 B U 17.4 B D B D 15.0 B D 17.0 B D B U 14.6 B D 17.6 B U B U 14.6 B D 17.8 B U A U 15.0 B U 17.8 B U A U 15.0 B D 18.0 A U B D 15.2 A U 18.2 A U B D 15.4 A U 18.2 A U B U 15.1 B D 18.2 A U It is known that if the process potential index c p > 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 c p 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 z test values both z A/B and z u/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

13 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 , 15(1), pp 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 George Utekhin 2008, Use of statistical techniques in quality management systems, The 8th International Conference Reliability and statistics in transportation and communication 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: 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: , 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, Volume- 2, Issue-4, Sotiris Bersimis, Stelios Psarakis and John Panaretos 2006, Multivariate Statistical Process Control Charts: An Overview, Quality and Reliability Engineering International, vol. 3, pp William H. Woodall 2000, Controversies and Contradictions in Statistical Process Control, Journal of Quality Technology, October 12 13,