The Journal of The Textile Institute
ISSN: 0040-5000 (Print) 1754-2340 (Online) Journal homepage: http://www.tandfonline.com/loi/tjti20
Implementation of an operating procedure for quality control at production level in a RMG industry and assessment of quality improvement Chowdhury Jony Moin, A. B. M. Sohail ud Doulah, Mohammad Ali & Ferdous Sarwar To cite this article: Chowdhury Jony Moin, A. B. M. Sohail ud Doulah, Mohammad Ali & Ferdous Sarwar (2017): Implementation of an operating procedure for quality control at production level in a RMG industry and assessment of quality improvement, The Journal of The Textile Institute, DOI: 10.1080/00405000.2017.1358412 To link to this article: http://dx.doi.org/10.1080/00405000.2017.1358412
Published online: 10 Aug 2017.
Submit your article to this journal
Article views: 18
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tjti20 Download by: [Bangladesh University of Engineering and Technology ]
Date: 21 September 2017, At: 01:38
The Journal of The Textile Institute, 2017 https://doi.org/10.1080/00405000.2017.1358412
Implementation of an operating procedure for quality control at production level in a RMG industry and assessment of quality improvement Chowdhury Jony Moina, A. B. M. Sohail ud Doulaha, Mohammad Alib and Ferdous Sarwarc
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
a
Department of Textile Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh; bDepartment of Industrial and Production Engineering, Bangladesh University of Textiles, Dhaka, Bangladesh; cDepartment of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
ABSTRACT
Appropriate operating procedure plays a vital role in any production process for improving the quality of the final product An export oriented knit apparel manufacturing industry in Bangladesh was assessed initially and an Operating Procedure for Quality Conformation (OPQC) was applied at production process for quality improvement. The improvements were assessed twice. First assessment was done at seventh month and second assessment at ninth month after implementation of OPQC. Actions were taken considering the feedbacks from each assessment. As a result fabric (in terms of garment) loss % from cutting to final inspection was reduced and found 5.42, 4.65 and 1.19 at initial, first and second assessment, respectively. Assessment by process capability analysis shows the improvements of Cp and Cpk from 0.38 to 0.78 and −0.35 to 0.44 from start to end of process at second assessment. Similarly Taguchi quality loss (% of order value) was reduced in each assessment and was found 10.88, 9.66 and 3.98% at initial, first and second assessment respectively. Moreover, process capability analysis and Taguchi quality loss analysis directed the manufacturer to the next steps for quality improvement.
Introduction The rapidly changing business behaviors, such as global competition, declining profit margin, customer demand for high quality products, product variety, reduced lead-time etc. necessitate the manufacturing industries to adapt the changing business conditions. The demand for higher quality products with lower price is increasing. The competitiveness in markets and consumers pressure has forced many firms to rely on global sourcing for business advantages (Su, Gargeya, & Richter, 2005). As a result manufacturers need to improve their process through producing right-first-time-quality and reduction of resources losses. Companies are concerned about continuous improvement of their productivity, product quality, working environment, operational performance etc. to meet internal and external consumers’ demand. In recent years, it has been intensified that implementation of world class strategies such as JIT (Just-InTime) (Lubben, 1988), TPM (Total Productive Maintenance) (Nakajima, 1988), Lean manufacturing (Reeb & Leavengood, 2010), Agile manufacturing (Yusuf, Sarhadi, & Gunasekaran, 1999), Supply chain management (Handfield & Nichols, 1999), TQM (Total Quality Management) (Dean & Evans, 1994), Six sigma (Evans & Lindsay, 2014), Time-based strategy of Quick Response (Kincade, Cassill, & Williamson, 1993) etc. lead the companies to achieve continuous improvement of productivity, high quality products, the shortest possible lead time, improved operational performance etc.
CONTACT Chowdhury Jony Moin © 2017 The Textile Institute
[email protected],
[email protected]
ARTICLE HISTORY
Received 2 May 2016 Accepted 18 July 2017 KEYWORDS
Apparel; quality improvement; quality assessment; process capability analysis; Taguchi loss function
In apparel manufacturing quality characteristics or specifications of products are defined in product development stage through the negotiation between buyers and manufacturers. In this industry, main raw material is fabric; others are different types of trimmings/accessories which are processed in a sequential way to produce complete apparels. To measure the quality of textile products measurable (laboratory test), graded characteristics (visual inspection) and combination of both like quality value function (Dadashian, Monfared, & Nasrabadi, 2009) are available. Apparel manufacturer usually do laboratory tests such as shrinkage test, color fastness tests etc. according to the buyers’ requirements and batch/lot wise before start the production. Meanwhile inspection is done during the production process. But it is very usual that few apparels are rejected due to poor quality. This is the result of poor quality raw materials or faulty process or employee’s casual behaviors (machine and/or human error) etc. The study was conducted for a true apparel manufacturer (export oriented knit apparel producer) who has received orders from buyer’s end and purchases raw materials, produces styles from the raw materials on his own premises and then sells the styles to buyers (Glock & Kunz, 2005). The methodologies for quality improvement found in literature are very similar in a broad sense and follow four common steps, those can be described as, STEP–1: identify the problem and the causes of problem i.e. initial assessment, STEP–2: plan for quality improvement, STEP–3: elimination of the problem addressing the causes
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
2
C. J. MOIN ET AL.
and STEP–4: assessing the improvement. Here, STEP–1, 2 and 4 are also alike for all organizations but STEP–3: requires specific modifications in processes. Hence, it differs from organization to organization due to variations in processes. In this study, it was found that quality control suffers in each process of the studied apparel manufacturer and Fabric loss from cutting to final inspection due to quality was significantly high and inconsistence. It directly affects product quality and losses at production considerably. In this circumstance, the author found that there were remarkable inconsistencies in the operating procedure of quality controllers. To overcome this, the author introduced an Operating Procedure for Quality Conformation (OPQC) at STEP–3 of his quality improvement methodology. This OPQC was implemented through trained quality personnel and then the improvement was measured employing quantitative procedures, process capability analysis and Taguchi loss function at STEP–4.
Literature review Earlier SQC, SPC, QA and QC were considered in management and decision-making levels, but now also used in production level. All of these methodologies i.e. quality control and improvement methodologies can assess and guide for quality products and their improvement. However, there is a need of directions for 5W2H (who, what, when, where, why, how and how much) (Hasin, 2007) actions which will ensure quality products and their improvement. To fulfill this gap, scientific methodologies like the Shewhart Cycle, the Deming Cycle, the PDCA (Plan-DoCheck-Act) Cycle, and the PDSA (Plan-Do-Study-Act) Cycle were gifted by quality gurus (Moen & Norman, 2006). Nowadays more comprehensive methodologies such as TQM (Heizer & Render, 2006), Six-sigma (Eckes, 2003), Lean (Feld, 2000) and combination of them are practiced for continuous improvement of quality in textiles and RMG sector. Many articles deploy multiple tools of different methodologies (TQM, Lean, Six-sigma and others) together to improve the quality and productivity. Among the 7 basic TQM tools, pareto chart (Kumar & Naidu, 2012; Uddin & Rahman, 2014; Vijayakumar & Robinson, 2016), cause effect diagram (Gupta & Bharti, 2013; Uddin & Rahman, 2014; Vijayakumar & Robinson, 2016), control charts (Gupta & Bharti, 2013; Kumar & Naidu, 2012) were widely used for identification of most noticeable problems, identifying the causes of most noticeable problems and checking the process whether control or not respectively. Literature also shows application of Six-sigma, lean and combined methodologies on textiles and RMG sector for quality improvement such as Six-sigma methodology, DMAIC was used to eliminate or reduce the defects of yarn (Gupta & Bharti, 2013), lean six sigma methodology to control absenteeism in garment industries (Kumar & Naidu, 2012), DMAIC and lean tool 5S used to minimize manufacturing defectives of garments industry (Vijayakumar & Robinson, 2016). The basic approach is to eliminate the causes of faults, rather than just correcting faulty work through proactive actions for process control. Some authors enrich the literature for Process control issues (Check points and control points) of apparel industry for pattern making, spreading, cutting, fusing, sewing, pressing, packaging and quality evaluation of apparel and accessories (Carr & Tyler, 2000; Das, 2009; Mehta, 1985). Operations of process control issues also
need training and development of technical staffs which is also constructed for textile and RMG sector in literatures (Geršak, 2013; Purushothama, 2012). With the application of advanced manufacturing technologies a Knowledge-Based Process Control System was found which eliminate the inconsistencies between operator opinions and the reduction of training expenditures for yarn brushing process and quality improvement (Tang, Pickering, & Freeman, 1993a, 1993b, 1993c). A knowledge-based system, Sewing Defects Analysis System (SDAS) was designed to the classification and diagnosis of garment manufacturing defects (Dastoor, Radhakrishnaiah, Srinivasan, & Jayaraman, 1994). As every improvement needs to be measured, researchers also practice both qualitative and quantitative measures such as SQC (Montgomery, 2007), SPC (Montgomery, 2007), Taguchi quality function (Taguchi, 1986) etc. Statistical quality control tools like control charts are able to assess and identify the status of process in a manner of ‘ok’ or ‘not ok’. Acceptance sampling with AQL also assesses and identifies the status of a lot/batch in similar manner. But assessment of products against specifications and as well as process against benchmark or standard by Process capability analysis and Taguchi quality function provide quantitative results which are needed as a guide of further improvement.
Process capability analysis Process capability indices, such as Cp and Cpk have been widely used as statistical tools to assess the manufacturing process performance. This is also a widely used statistical process control technique, to determine the ability for manufacturing process between tolerance limits and engineering specifications providing quantitative measures of process potential and performance (Statisti & Tehnike, 2009). It indicates the directions and magnitude of corrections and overall improvement. So, the producer can take steps to improve or redesign the process. For the process capability analysis following, four capability indices were widely used (Montgomery, 2007). (1) Cp = Process Potential Index. (2) Cpk = Process Performance Index. (3) Cpu = Upper Process Performance Index. (4) Cpl = Lower Process Performance Index. Cp: Process potential index is related to process variability. It is the ratio between the allowable process spread and actual process spread. If a process run with X̄ = mean of the observations, σ = standard deviation of observations, USL = upper specification limit and LSL = lower specification limit, then Allowable process spread Cp = Actual process spread . Cpk: Process performance index is the measurement of process capability with respect to mean. It measures capability of the process at the specification limit, which has the highest chance of a part going beyond the limit. And naturally, that side will have the highest chance, to which the process has shifted. So, the performance of the process is measured both on the upper side and lower side of the specification by Cpu and Cpl, respectively, and process performance index is the minimum of them.
Cpu =
Allowable upper process spread USL − X̄ = . Actual upper process spread 3𝜎
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
THE JOURNAL OF THE TEXTILE INSTITUTE
3
Figure 1. Relative values of allowable vs. actual process spread.
Cpl =
STEP 1 Study on present quality status
Allowable lower process spread X̄ −LSL = . Actual lower process spread 3𝜎
Cpk = minimum (Cpu, Cpl). A process is considered ‘capable’ if the process spread is less than or equal to the specification limits. If it goes sufficiently beyond these specification limits, the process is judged ‘not capable’. Figure 1 represents the relative values of allowable vs. actual process spread of four capable processes at 3σ, 4σ, 5σ and 6σ level with Cpu, Cpk and ppm or defective percentage out of specification. The Process capability analysis is extensively applied to determine the ability to manufacture products within the specification limits. It can be applied in all stages of manufacturing like planning, designing, product cycle, process etc. Such as banking service (Chen, 2008), turning operation (Statisti & Tehnike, 2009), pharmaceutical production (Chowdhury, 2013), product and process design (Bargelis, 2007) and so on.
Quality loss analysis Taguchi loss function is generally preferred to be used in modeling the expected costs. The basis of the Taguchi quadratic loss function is incurred when the quality characteristics of a product deviate from the target value. Taguchi loss function is shown below:
L = k × (Y − T)2 =
} A{ 2 𝜎 + (Y − T)2 . 2 d
(1)
where L = Symbolizes loss function; k = constant; A = the loss/ cost of exceeding specification limits (e.g. the cost to scrap a unit of output) (Hasin, 2007). σ = standard deviation; d = the allowable tolerance from the nominal value that is used to determine specification limits; Y = the observed value of the quality characteristics; and T = the target value of quality characteristic. Application of Taguchi loss functions is also widely used in decision-making, process engineering and designing such as for supplier selection & evaluation (Ordoobadi, 2009; Sivakumar, Kannan, & Murugesan, 2015), electronic assembly operation (Antony, 2001), steel bar cutting (Nalbant, Gökkaya, & Sur, 2007), glass fiber processing (Palanikumar, 2008), and so on. Moreover, the specific amount of losses can be computed for all possible values of the process average by Taguchi’s loss function (Suh, 1992).
STEP 4 Quality assessment
STEP 2 Plan for quality improvement
STEP 3 Implementation of OPQC
Figure 2. Study framework.
Methodology The framework of this study was designed following the quality improvement methodologies found in literature and given in Figure 2. Though the framework guided as a continuous cycle; the study described only two cycles. In apparel manufacturing there are various processes in the entire manufacturing. Usually the process sequence is Raw material processing (fabric inspection and testing), Cutting (pattern making, marker making, spreading, lay cutting, sorting and bundling), Sewing, Finishing (pressing, hang tagging, packing, cartoning), Final inspection and Delivery. The initial process input is fabric and this is the main raw material. Usually fabric is measured in length. So for the convenience of the study it was converted to garments quantity through fabric consumption per garments. Due to the nature of the data, actual data of order quantity and processing quantity were converted to percentage against the actual order quantity to normalize the values of each order quantity. All data were collected following the sampling procedure of suggested OPQC. These normalized data were assessed by applying quantitative procedure, process capability analysis using Minitab 17®and Taguchi loss function. The results were interpreted against the target and specification limits. Table 1 shows the target and specification limits for the quality assessment of the production. And Table 2 presents the manufacturer assumption for loss that incurs in case of the process deviation from the target. This loss was divided into two – for processing and section wise. It was convenient for manufacturer to assume the loss as percentage of order value because materials and processing cost is directly related to the order value not to order quantity.
4
C. J. MOIN ET AL.
Table 1. Target and Specification limits for the quality assessment.
First cycle
Parameters Target
STEP–1: Initial assessment for quality status of the different processes was studied. Thirty orders were observed and average quantity per order was 3407 pieces. Process input and output quantity for each order such as Cutting, Sewing, and Final inspection (instead of finishing) were recorded and data were assessed following the below framework (Figure 3). STEP–2: Addressing the problems of rejection quantity the corresponding process were selected (Raw materials Inspection, Cutting, Sewing & Finishing) for quality improvement and concerned people were targeted for training. STEP–3: An OPQC at production level (divided into three modules) was prepared for quality improvement and concerned people were trained in three modules. This OPQC was implemented through trained quality personnel and monitored by respective section in-charge of the process.
Specification limit Equal to buyer’s order quantity +3% of the target value −3% of the target value
Upper specification limit Lower specification limit
Normalized value 100 103 97
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
Table 2. Loss for unit deviation of product from target. Process From raw materials processing up to cutting From cutting up to sewing From sewing up to finishing From finishing up to final inspection
Loss (% of order value) (processing) (0.1)%
Loss (% of order value)a (section wise) 1.1%
(0.1 + 0.2)%
(1.1 + 0.2)%
(0.1 + 0.2 + 1.0)%
(1.1 + 0.2 + 1.0)%
(0.1 + 0.2 + 1.0 + 0.5)%
(1.1 + 0.2 + 1.0 + 0.5)%
a
Including raw materials cost.
Fabric inspection
Cutting
GL %, PCA & TL
Sewing
GL %, PCA & TL
Finishing
GL %, PCA & TL
Final inspection
Shipment
GL %, PCA & TL
Figure 3. Assessment framework for STEP–1 and STEP–4. Notes: GL % = Garment Loss %, PCA = Process Capability Analysis & TL = Taguchi Loss.
Table 3. Operating procedure for fabrics quality conformation. Process Raw material processing
Conformation way Fabric inspection
Inspection/test method Visual inspection 4 points system
Sampling 10% fabric roll and according to 4 points system
Fabric Laboratory test
International or Buyers’ proposed procedure
According to buyers’ proposed test procedures
Checking issues Holes in fabric, Shade variation, Bare effect, Inappropriate length, width According to buyer such as shrinkage, wash fastness, color fastness etc.
Table 4. Operating procedure for in-process quality conformation. Process Pattern making Marker making
100% patterns 100% markers
Sampling
Spreading
100% plies and 100% lays
Lay cutting
100% lay
Sorting & bundling
100% cut panel
Sewing line
100% work stations of each sewing line
Sewing line-end
100% garments
Pressing quality
100% garments
Hang tagging, packing & cartoning
100% garments
Checking issues Grain line, check & stripe, design, size Parts missing, mixed parts, pattern not facing in the correct direction, patterns not aligned to fabric grain Ply alignment, ply tension/slackness, bowing, grain-line, selvedge alignment, fabric width, mismatch checks & strips, splicing, visible fabric defects Frayed edges, drill marks, fuzzy/serrated edge, improper cutting, notches, oil spots, improper knife sharpening, knife or scissor cut, ply to ply fusion Visible fabric defects, size variation, wrong shaped parts tied together, wrong order of tying. Needle damage, skipped stitch, seam grin, seam pucker, wrong stitch density, improperly formed stitch, oil spots/stains Sewing line quality issues, size measurement, irregular shape of sewing, insecure back stitching, mismatched checks & strips, mismatched seam, extraneous part caught in seam, asymmetrical garment, parts or accessories missing, incorrect interlining, mismatched trimmings, wrong parts joined Burnt garments, change in color, broken trims & appearance, uneven form garment, wrinkles, garments texture Pressing quality issues, assortments
THE JOURNAL OF THE TEXTILE INSTITUTE
Operating procedure for raw materials quality conformation There were two types of raw materials and categorized into two- fabric and accessories. The main raw material is fabric and the following Table 3 presents the OPQC for fabric quality conformation.
Table 5. Operating procedure for final product quality conformation. Process Final inspection
Sampling 100% garment &100% Packages
Checking issues Sewing line quality issues, sewing line-end quality issues, pressing quality issues, assortments
Operating procedure for quality conformation
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
5
Operating procedure for in-process quality conformation
This operating procedure directed the quality controllers to do their job with proper consistency providing a precise check list for each process in Checking issues column. The operating procedure was divided into three modules. These are Module 1: Operating procedure for raw materials (fabric) quality conformation, Module 2: Operating procedure for in-process quality conformation and Module 3: Operating procedure for final product quality conformation.
The following Table 4 presents the OPQC for In-processes (pattern making to cartoning) where quality conformation way was visual inspection. Operating procedure for final product quality conformation After producing a contract; final inspection of each contract was done by visually inspection for final product quality
Table 6. Training schedule for OPQC. Quality personnel Process Fabric inspection Pattern making Marker making Spreading (3 cutting tables) Cutting (3 cutting tables) Sewing line (11 sewing lines) Sewing line-end (11 sewing lines) Finishing (pressing) (11 lines) Finishing (hang tagging, packing & cartoning) (11 lines) Final inspection Total
No. of inspectors 4 2 2 3 × 2 = 6 3 × 2 = 6 11 × 1 = 11 11 × 2 = 22
Section in-charge 1 1
Module-1 √ √
Module-2 x √
Module-3 x x
1
√
√
x
11 × 1 = 11 11 × 2 = 22
1
5 91
1 5
√
√
√
Table 7. Process wise defects and their sources. Source of origin Process Cutting
Cutting Miss cut, ragged cutting, notches
Sewing
Fabric Neps, slubs, spot, shading, knot, dirty mark, hole, oil mark Shading, dirty mark
Finishing
Oil spot, dirt spot, holes
Mix shade
6.00 5.00
Dirty mark
Collar mistake, skip stitch, broken stitch, puckering, wavy, wrong assembly, irregular stitch, needle cut, raw edge, wrong barcode, high & low hem Size mistake, joint stitch, sewing alter, wrong button position, label misplace, raw edge, damage
–
5.42
1.49
2.16 1.01 0.75
0.89 0.00
0.00 Cutting loss
Sewing loss
Figure 4. Process wise fabric (in term of garment) loss percentage.
1.49
1.19 0.44
Finishing loss
Uncut thread, missing tag/sticker
Status before OPQC
4.65
3.03
3.00 1.00
Finishing –
Process wise fabric (in term of garment) loss %
4.00
2.00
Sewing –
Start to end Loss
First assessment after OPQC Second assessment after OPQC
6
C. J. MOIN ET AL.
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
(a)
(c)
(b)
(d)
Figure 5. (a–d) Distribution and probability plots of order quantity before OPQC and normalized final inspection after second assessment after OPQC.
conformation. The following Table 5 presents the OPQC for final inspection. Training of quality personnel The selected quality personnel were already concerned and a day-long training program was conducted like orientation only to align them with OPQC. The training schedule was as shown in the following Table 6. STEP–4: Quality improvement assessment was done after implementation of the above mentioned OPQC. The data were collected at 7th month from Cutting, Sewing and Final inspection of 67 orders with average quantity 4040 pieces and converted to normalized data and data were assessed. Second cycle The feedback of STEP–4 of first cycle was considered as STEP–1 for the second cycle. After two months of first assessment data were collected from Cutting, Sewing and Final inspection of 30 orders with average quantity 2715 pieces and assessed in similar manner.
Results and discussion The quality control procedure of the studied apparel manufacturer from cutting to final inspection was based on visual inspection without any methodical sampling before implementation
of the OPQC. But the process wise checking issues of OPQC made the visual inspection more effective. The orientation of OPQC made aware the quality controllers to their job responsibilities and motivated as well. Hence, the quality control procedure became systematic in each section. The assessment of quality inspectors became uniform with the use of checking issues of OPQC as check lists of their inspecting process.
Garment loss % The applied OPQC made the operating procedure of quality controllers’ more precise and consistence as a result fabric (in term of garment) loss % tends to reduce in subsequent process. At the first cycle it was reduced to 4.65 from 5.42 from start to end of the process. Identification of process wise defects and their source of origin (shown in Table 7) guided the management to eliminate those. All defects originated from sewing section was eliminated with some proactive actions of management; those were subjective training, motivating with incentive, timely changing of needles, proper monitoring and supervisions and few alter works in sewing. As a result after second cycle, sewing loss became 0% and in subsequent process i.e. finishing it was found only 0.44%. To minimize the defects originated from fabric, better quality input was focused and the accepted penalty points of fabric inspection was down to 20/100 yard2 and below from 25/100 yard2 and below. As a result cutting loss also reduced. The Figure 4 shows the process wise fabric (in term of garment) losses percentage.
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
THE JOURNAL OF THE TEXTILE INSTITUTE
(a)
(b)
(c)
(d)
7
Figure 6. (a–d) Process capability analysis before implementation OPQC (initial assessment).
Capability analysis Before and after implementation of OPQC, actual and normalized data of order quantity and the processes (cutting, sewing, finishing and final inspection) were checked for distribution and probability plot of using Minitab 17® and found normal. For example, distribution and probability plot of order quantity before OPQC and normalized final inspection quantity after second assessment were shown in Figure 5((a)–(d)). Process capability analysis of cutting, sewing & finishing input and final inspection initial (before implementation of OPQC), first and second assessment (after implementation of OPQC) were done using Minitab 17® against the target and specification limits and are shown in Figures 6–8, respectively. The graphs show that all processes are incapable before and after implementation of OPQC. This phenomenon is very usual because very few processes are completely satisfied various process capability indices in real world. The graphs also show that in case of cutting, sewing, finishing input and final inspection quantity, the tendency of data distribution percentages to go outside LSL and USL gradually decreases in subsequent processes. It also shows that mean of processes were not on target (100) for all assessment (initial, first and second). But with gradual improvement at final stage (final inspection) mean of the assessments (100.325, 100.513 and 101.313, respectively) became closer to
target. These arithmetic means of assessments is related to input allowance % at the start, loss % from start to end of process and extra shipment % over target (equivalent to output % after final inspection). The Figure 9 shows the continuous improvement of the process with earlier mentioned issues (Figures 10 and 11). The figure shows that process potential index, Cp were the lowest for initial assessment and it always stepped to standard (Cp = 1) at each subsequent process. In case of second assessment the Cp values of each process were higher than the other two assessments and maximum Cp value (0.78) was obtained in final inspection of second assessment. Improving phenomenon was also seen for process performance indexes, Cpk except the Cpk of final inspection of second assessment. The higher extra shipment % may be responsible for getting low process performance indexes, Cpk (0.44). This can be considered as one of the feedback for the next cycle of the continuous improvement. Quality loss analysis Taguchi quality losses (% of order value) process and section wise for each assessment were calculated and shown in Table 8. Taguchi quality losses percentage for Processing & Raw materials after implementation of OPQC is also shown in Figures 12 and 13.
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
8
C. J. MOIN ET AL.
(a)
(b)
(c)
(d)
Figure 7. (a–d) Process capability analysis after (first assessment) implementation of OPQC.
Table 8 shows that Taguchi quality losses (% of order value) were reduced in each assessment. It was found 10.88, 9.66 and 3.98% for initial, first and second assessment, respectively, from start to end of the process. Similar trend were also found in material and processing loss. Table 8 shows that Taguchi quality losses percentages for fabric were reduced in each assessment. It was found 75, 72, and 60% for initial, first and second assessment, respectively, from start to end of the process. Fabric loss % was reduced at subsequent processes in each assessment except sewing and finishing process of second assessment. It is mentionable that loss % (in terms of garment) for sewing and finishing were minimum, 0.00 and 0.44 respectively. So this exceptionality can be considered also as one of the feedbacks for the study of next cycle to reduce these losses. Table 8 shows that Taguchi quality loss percentages for processing were reduced in the starting process in each assessment and those were 4.1, 3.5 and 2.4% for initial, first and second assessment respectively. Taguchi loss % for processing trends to increase up to sewing and then decrease in finishing section. Initial and first assessments follow this trend in a rhythmic manner. But in case of sewing of first assessment and sewing & finishing process of second assessment it was found higher and 11.3, 17.5 and 16.6%, respectively. Earlier Figure 4 shows that fabric (in term of garment) loss percentages were 0 and 0.44% for sewing
and subsequent process i.e. finishing in second assessment due to proactive actions (identification and elimination of defects originated from sewing section) and better quality fabric input. These higher percentages of Taguchi loss function is also one of the feedbacks for the study of next cycle. Taguchi loss function is directly related to ‘A’ and ‘{𝜎 2 + (Y − T)2 }’ as per Equation (1). So, the higher deviation of mean from target in sewing and finishing process of second assessment may increase the Taguchi quality losses percentages. These losses should be minimized to increase the satisfaction of the customers (Buyers) and to avoid the risk of manufacturer for over production than target. Figure 14 shows guide line for the next cycle of quality improvement. It shows that as process mean become closer to target the Cp, Cpk and Taguchi loss function will be improved. If extra/less quantity of target production can be minimized at each process, Cp, Cpk and Taguchi loss function can be improved. To do so, further studies were required to investigate the relationship of fabric loss with its penalty points, found in fabric inspection. The selection of appropriate fabric penalty points as a regulator of input material quality control, consistency in operating procedure of quality controllers and continuous improvement attitudes are required to sustain the quality and its improvement. Though the study was conducted in apparel manufacturing quality improvement with a large scope it was confined its scope
THE JOURNAL OF THE TEXTILE INSTITUTE
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
(a)
(b)
Process Capability Report for Finishing input after OPQC (Second assessment)
(c)
Process Capability Report for Final inspection after OPQC (Second assessment
(d)
Figure 8. (a–d) Process capability analysis after (second assessment) implementation of OPQC.
7.00
5.75
6.00
5.42
5.16
Before OPQC
4.65
5.00
First assessment after OPQC
4.00 2.51
3.00 2.00
1.19
1.00
Second assessment after OPQC
1.31 0.33 0.51
0.00 Allowance % at Start
Loss % Start to end of process
Extra shipment % at End
Figure 9. Allowance % at the start, loss % from start to end of process and extra shipment % over target.
1.2
1.00
1 0.8 0.6 0.4
0.76
0.68 0.50 0.38
0.46 0.40
0.76 0.53 0.48
Status before OPQC
0.78 0.66 0.54
First assessment after OPQC
0.2 0 Cutting input
Figure 10. Cp of process vs. standard (at 3σ level).
Sewing input
Finishing input
3 Sigma level
Final inspection
Standard
Second assessment after OPQC
9
10
C. J. MOIN ET AL. 1.2
1.00
1 0.8 0.6 0.31
0.4
0.29
0.11
0.2
0.48
0.31 0.18
0.54
3 sigma level
0.44
Status before OPQC First assessment after OPQC
0 Cutting input
-0.2 -0.4
-0.35
-0.6
-0.36
Sewing input Finishing input Final inspection -0.17
Second assessment after OPQC
Standard
-0.18
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
Figure 11. Cpk of process vs. standard (at 3σ level). Table 8. Taguchi Loss function (% of order value). Initial assessment From raw materials processing up to cutting From cutting up to sewing From sewing up to finishing From finishing up to final inspection From start to end
First assessment after OPQC
Second assessment after OPQC
S/W/L 4.85
P/L 0.44
M/L 4.41
S/W/L 3.74
P/L 0.34
M/L 3.40
S/W/L 1.03
P/L 0.09
M/L 0.94
3.50 1.45 1.07
0.81 0.82 0.69
2.70 0.63 0.38
3.19 1.93 0.81
0.74 1.09 0.52
2.45 0.84 0.29
0.70 1.23 1.03
0.16 0.70 0.66
0.53 0.53 0.37
10.88
2.76
8.12
9.66
2.68
6.98
3.98
1.61
2.37
Notes: S/W/L = Section wise loss, P/L = Processing loss and M/L = Material loss.
80% 70% 60% 50% 40% 30% 20% 10% 0%
75% 72%
Taguchi quality losses % for Material loss 41%
Initial assessment
60%
First assessment after OPQC
35% 24%
25% 25% 13%
From Raw materials From Cutting up to processing up to sewing cutting
6% 9%
13% 4% 3%
9%
From Sewing up to From Finishing up to finishing final inspection
Second assessment after OPQC
From Start to end
Figure 12. Taguchi quality losses percentages for fabric (material) loss. 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%
40.4% Initial assessment
Taguchi quality losses % for Processing 27.8% 25.4% 17.5%
4.1% 3.5%
7.4% 7.6% 2.4%
From Raw materials processing up to cutting
4.0%
From Cutting up to sewing
11.3% 7.6%
First assessment after OPQC
16.6% 6.3% 5.4%
From Sewing up to finishing
From Finishing up to final inspection
Second assessment after OPQC From Start to end
Figure 13. Taguchi quality losses percentages for processing.
to only quality control activities of the industry. So, there is a scope to improve the raw material quality during sourcing and production procedures/process for quality improvement. In these circumstances, the study can be extended for modification of production procedures along with similar check points and checking issues of OPQC. The study also can be extended for Vertical-Manufacturer (who make the fabric and produce styles
garments in their own premises (Glock & Kunz, 2005)) and Vertical Manufacturer-Distributor (who make the fabric, produce styles in their own premises and sell garments directly to the consumers (Glock & Kunz, 2005)). The study methodology also can be improved by deploying more improved process capability indices like multivariate process capability indices, which are used for evaluation of processes with correlated quality characteristics.
THE JOURNAL OF THE TEXTILE INSTITUTE
(a)
(b)
Process Capability Report (If no extra quantity were produced at last process) (using 95.0% confidence) Target
LSL
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
Overall Within Overall Capability Pp 0.85 CI for Pp (0.63, 1.07) PPL 0.77 PPU 0.93 Ppk 0.77 CI for Ppk (0.54, 1.00) Cpm 0.83 LB for Cpm 0.65 Potential (Within) Capability Cp 0.84 CI for Cp (0.63, 1.06) CPL 0.76 CPU 0.92 Cpk 0.76 CI for Cpk (0.53, 0.99)
94 Observed 33333.33 0.00 33333.33
Performance Expected Overall 10408.91 2575.07 12983.98
96
98
100
0.25
USL
Process Data LSL 97 Target 100 USL 103 Sample Mean 99.7145 Sample N 30 StDev(Overall) 1.17445 StDev(Within) 1.18462
PPM < LSL PPM > USL PPM Total
11
0.23
Taguchi loss function (if no extra quantity were produced at last process)
0.20 0.15
0.15
0.10
0.08
0.05
102
Expected Within 10969.08 2772.99 13742.07
0.00 Section wise loss Processing loss
Material loss
Figure 14. (a and b) Process capability analysis and Taguchi loss function (If no extra quantity were produced at last process (From Finishing to final inspection)).
Conclusion The mentioned quality improvement project improved the performance of the apparel manufacturer. Within nine month the process loss % (in term of garments) was reduced to 78%. The check points and checking issues of applied OPQC and training of concerned quality persons were helped to improve the quality from the very beginning and faulty material are rejected in earlier process. As a result the performances were better in subsequent processes. The assessment by process capability analysis shows the directions and magnitude of corrections for each process. Figure 14 suggests Process potential index Cp and Process performance index Cpk can be improved to 0.84 and 0.76, respectively at 3 sigma level. It also shows that the Taguchi loss function can be minimized to 0.23 (% of order value). This quality improvement methodology always guides for the improvement and the concept of this study can be used in other similar industry. The OPQC of this methodology was produced subjectively based on practical data and current scenario of the studied apparel manufacturer. As result the success of the project became easy. But this methodology can be used successfully for similar industry with a review of OPQC based on the present status of the industry and loss statements according to company structure and procedures before implementation.
Disclosure statement No potential conflict of interest was reported by the authors.
References Antony, J. (2001). Simultaneous optimisation of multiple quality characteristics in manufacturing processes using Taguchi’s quality loss function. The International Journal of Advanced Manufacturing Technology, 17, 134–138. Bargelis, A. 2007. Design for process capability and capacity at the product conception stage. Proceedings of 19th international conference on production research “The Development of Collaborative Production and Service Systems in Emergent Economics”, Valparaiso, Chile.
Carr, H., & Tyler, D. J. (2000). Carr & Latham’s technology of clothing manufacture. Oxford: Blackwell Science. Chen, C. Y. (2008). A study on process capability index applied to banking service. Journal of American Academy of Business Cambridge, 14, 77–85. Chowdhury, M. R. (2013). Process capability analysis in pharmaceutical production. International Journal of Pharmaceutical and Life Sciences, 2, 85–89. Dadashian, F., Monfared, M., & Nasrabadi, H. (2009). Design of a new quality assessment system for textile products. The Journal of The Textile Institute, 100, 640–648. Das, S. (2009). Quality characterisation of apparel. New Delhi: Woodhead Publishing India. Dastoor, P., Radhakrishnaiah, P., Srinivasan, K., & Jayaraman, S. (1994). SDAS: A knowledge-based framework for analyzing defects in apparel manufacturing. Journal of the Textile Institute, 85, 542–560. Dean, J. W., & Evans, J. R. (1994). Total quality: Management, organization, and strategy. Minneapolis, MN: West Publishing Company. Eckes, G. (2003). Six sigma for everyone. New Jersey: John Wiley & Sons, Inc., USA.. Evans, J., & Lindsay, W. (2014). An introduction to six sigma and process improvement. Stamford: Cengage Learning. Feld, W. M. (2000). Lean manufacturing: Tools, techniques, and how to use them. Boca Raton: CRC Press. Geršak, J. (2013). Design of clothing manufacturing processes: A systematic approach to planning, scheduling and control. Philadelphia:Woodhead Publishing Limited. Glock, R. E., & Kunz, G. I. (2005). Apparel manufacturing: Sewn product analysis. Upper Saddle River, NJ: Prentice Hall. Gupta, N., & Bharti, P. (2013). Implementation of six sigma for minimizing the defects rate at a yarn manufacturing company. International Journal of Engineering Research and Applications, 3, 1000–1011. Handfield, R. B., & Nichols, E. L. (1999). Introduction to supply chain management. Upper Saddle River, NJ: Prentice Hall. Hasin, D. M. A. A. (2007). Quality control and management. Dhaka: Bangladesh Business Solutions. Heizer, J., & Render, B. (2006). Operations management. Upper Saddle River, NJ: Pearson Prentice-Hall. Kincade, D., Cassill, N., & Williamson, N. (1993). The quick response management system: Structure and components for the apparel industry. Journal of the Textile Institute, 84, 147–155. Kumar, C., & Naidu, N. (2012). Minimizing the eighth waste of leanabsenteeism through six sigma methodology. International Journal for Quality research, 6(2), 113–118. Lubben, R. T. (1988). Just-in-time manufacturing: An aggressive manufacturing strategy. New York, NY: McGraw-Hill Companies.
Downloaded by [Bangladesh University of Engineering and Technology ] at 01:38 21 September 2017
12
C. J. MOIN ET AL.
Mehta, P. V. (1985). An introduction to quality control for the apparel industry. Tokyo: JSN International. Moen, R., & Norman, C. (2006). Evolution of the PDCA cycle. http://cissp. tjscott.net/standards/moen.norman.pdca.origins.pdf. Montgomery, D. C. (2007). Introduction to statistical quality control. Hoboken, NJ: Wiley. Moon W., Suh (1992). Quality, Process, and Cost Controls—a ‘Random Walk’ in Textile Profitability. The Journal of The Textile Institute, 83(3), 348–360. doi:10.1080/00405009208631208. Nakajima, S. (1988). Introduction to TPM (Total Productive Maintenance), Productivity Press. Journal of Quality in Maintenance Engineering, 13, 338–352. Nalbant, M., Gökkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials & Design, 28, 1379–1385. Ordoobadi, S. (2009). Application of Taguchi loss functions for supplier selection. Supply Chain Management: An International Journal, 14, 22–30. Palanikumar, K. (2008). Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling. The International Journal of Advanced Manufacturing Technology, 36, 19–27. Purushothama, B. (2012). Training and development of technical staff in the textile industry. New Delhi: Woodhead Publishing India. Reeb, J. E., & Leavengood, S. A. (2010). Introduction to lean manufacturing. Corvallis: Oregon State University, Extension Service. Sivakumar, R., Kannan, D., & Murugesan, P. (2015). Green vendor evaluation and selection using AHP and Taguchi loss functions in production outsourcing in mining industry. Resources Policy, 46, 64–75. Statisti, U., & Tehnike, N. (2009). Improving the process capability of a turning operation by the application of statistical techniques. Materiali in tehnologije, 43, 55–59.
Su, J., Gargeya, V., & Richter, S. J. (2005). Global sourcing shifts in the US textile and apparel industry: A cluster analysis. Journal of the Textile Institute, 96, 261–276. Suh, Moon W. (1992). Quality, Process, and Cost Controls—a ‘Random Walk’ in Textile Profitability. The Journal of The Textile Institute, 83(3), 348–360. doi:10.1080/00405009208631208. Taguchi, G. (1986). Introduction to quality engineering: Designing quality into products and processes. Tokyo: Asian Productivity Organization. Tang, N., Pickering, J., & Freeman, J. (1993a). An Investigation into the control of brushed yarn properties: The application of machine vision and knowledge-based systems. Part I: The yarn brushing machine and experimental work. Journal of the Textile Institute, 84, 156–165. Tang, N., Pickering, J., & Freeman, J. (1993b). An Investigation into the control of brushed yarn properties: The application of machine vision and knowledge-based systems. Part II: The machine vision system. Journal of the Textile Institute, 84, 166–175. Tang, N., Pickering, J., & Freeman, J. (1993c). An Investigation into the control of brushed yarn properties: The application of machine vision and knowledge-based systems. Part III: The knowledge-based process control system. Journal of the Textile Institute, 84, 176–191. Uddin, S. M., & C. M. L. Rahman (2014). Minimization of defects in the sewing section of a garment factory through DMAIC methodology of six sigma. Research Journal of Engineering Sciences, 3, 21–26. Vijayakumar, G., & Robinson, Y. (2016). Impacts of lean tools and techniques for improving manufacturing performance in garment manufacturing scenario: A case study. International Journal of Advanced Engineering Technology, VII, 251–260. Yusuf, Y. Y., Sarhadi, M., & Gunasekaran, A. (1999). Agile manufacturing: The drivers, concepts and attributes. International Journal of Production Economics, 62, 33–43.
