Arvind Goodhill.docx

Graduation Project

2018

National Institute of Fashion Technology, Kolkata

Report on Graduation Project at

Arvind Goodhill Suit Manufacturing Pvt Ltd.

Mentored ByMr. Bikas Agrawal Asst. Prodessor NIFT, Kolkata

Submitted ByAbhilekh Thakur Gufran Ahmad B. F. Tech (2014-18)

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

ACKNOWLEDGEMENT This project would not have been possible to come into reality without the support of ALMIGHTY and the kind support and help of many individuals and organizations. We would like to extend our sincere thanks to all of them. We would also like to thanks our parents who gave us moral support during our research work on this project. First of all, we would like to thanks our Mentor Mr. Bikas Agrawal for providing us continuous guidance and helping us in the completion of this project. We would like to express my immense gratitude and sincere appreciation to Arvind Goodhill Suit Manufacturing Pvt. Ltd. for giving us an opportunity to pursue our Graduation Project. We would like to record our deepest sense of gratitude to Mr. Ravi Bhargava, CEO, Mr Arun Babu, General Manager(Operations), Mr Mahesh Rao, (Quality Head) ,Mr Sameer Sinha (Head-I.E.) from the Arvind Goodhill Suit Mfg. for their supervision, advice, valuable support and guidance from the very early stage of the course of our Graduation Project, which enabled us to proceed in the right direction and accomplish the mission of our work. Mr. Ratikanta Das(Manager Quality), Mr Vasava Kumar Swamy (Producton Manager), Mr. Naveen from the Quality and Production department for their cooperation and precious inputs on each and every step of our Graduation Project. We would also thank Mr. Sijeesh(I.E.) and Mr Shantanu(I.E.) for their constant support during the Project. We would also thank Mr. Dinesh Pandey and Mr. Devraj and (Quality Line in charge) of Jacket Line for supporting us in the Graduation Project. At the last we would like to thanks Mr. Saju KP (Head-Human Resource) for constant keeping up our spirit during the Project. It would not have been possible without the kind support and help of many individuals in the Organization. Thank You!

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Graduation Project Topic:- Implementing Quality Management System & Visual Control System in Production Floor by First Time Through (FTT)

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

ARVIND LIMITED INTRODUCTION Arvind Limited (formerly Arvind Mills) is a textile manufacturer and the flagship company of the Lalbhai Group. Its headquarters is in Naroda, Ahmedabad, and Gujarat, India. It has units at Santej (near Kalol). It manufactures cotton shirting, denim, knits and bottom weight (khaki) fabrics. It has also recently ventured into technical textiles when it started Advanced Materials Division in 2011. It is India's largest denim manufacturer apart from being the world’s fourth-largest producer and exporter of denim. HISTORY & OPERATIONS 

1897 – Arvind Mills starts business for sarees

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1931 – Arvind Mills Ltd was incorporated with share capital ₹165,000 ($55,000) in Ahmedabad. The products manufactured were dhotis, sarees, shirting, coats, printed lawns and voiles cambric’s, twills and gaberdine.

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1987 – The Company took up a modernization programmer to triple the production of denim cloth and to produce double yarn fabrics for exports. The new product groups identified were the indigo dyed blue denim, high quality two-ply fabrics for exports, and products such as sarees, full viols and dhotis.

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1991 – Arvind reached 100 million meters of denim per year, becoming the fourth largest producer of denim in the world.

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1992 – The company increased its production of denim cloth by 23,000 tons per day by modernizing the plant at Khatraj of Ankur Textiles.

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1994 – The company's operations were divided into textile, telecom and garments divisions.

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1995 – The garment division launched ready to stitch jeans pack under the brand Ruf & Tuf.

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1997 – The marketing and distribution network of the Newport brand was strengthened and the relaunched Flying Machine and Ruggers brand were strengthened.  Arvind Mills set up an anti-piracy cell for the first time in India to curb large scale counterfeiting of their brands Ruf &Tuf and Newport jeans.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

 Arvind Fashions doubled its capacity in the manufacturing facility in Bangalore to produce Lee jeans. 

1998 – Arvind Mills emerged as the world's third largest manufacturer of denim.  Arvind Mills went live with SAP R/3 ERP package in April 1997 in their new manufacturing units.

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2003 – For the fourth quarter, Arvind Mills saw a 280% growth in net profit.  Arvind Mills Ltd was assigned a "P1+" rating by CRISIL, which indicated a very strong rating for their commercial paper.

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2005 – For the fourth quarter in a row, Arvind Mills posted a profit growth in excess of 80%.  Arvind Mills bought entire stake in Arvind Brands from ICICI Ventures.

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2007 – Arvind expanded its presence in the brands and retail segment by establishing MegaMart, one of India's largest value retail chains.

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2010 – Arvind launched the Arvind Store, a concept putting the company's best fabrics, brands and bespoke styling and tailoring solutions under one roof.  Arvind launched its first major real estate projects.  Arvind became one of India's largest producers of fire protection fabrics.

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2012 – joint venture with PD Group, Germany, for manufacture of glass fabrics

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2014 – joint venture with PVH Corp for Calvin Klein Businesses in India  Launched formal suits with Goodhill Corporation Limited of Japan

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2014 – joint venture with OG Corp, Japan, for manufacture and sale of non-woven fabrics, the project being spearheaded by Dr. Kunal Shah

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2014 – forayed into the E-commerce segment with custom clothing brand Create

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2015 - The Company published its maiden Sustainability Report

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2016 – The Company completely entered online retailing and reached a revenue of 5,407.26INR crore (US$ 840 million)

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

BUSINESS 

Fabric Denim o Shirtings o Khakis o Knitwear o Voile Garment exports o Shirts o Jeans Arvind Brands (owned) o Flying Machine o Newport o Quads o Ruf &Tuf o Excalibur o Arvind RTW (Exclusively available at The Arvind Stores) Arvind Brands o Arrow o Gap Inc. o Lee o Wrangler o Gant U.S.A. o Tommy Hilfiger o Ed Hardy o Izod o Cherokee o Massimo o U.S. Polo Assn. o Billabong o Nautica o Aeropostale Advanced Materials Division EBO (exclusive brand outlet) / The Arvind Store Telecommunications service provider Engineering Real estate Mega Mart Retail o

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Arvind Goodhill Suit Manufacturing

Graduation Project

2018

ARVIND GOODHILL SUIT MANUFACTURING LTD Arvind Goodhill Suit Manufacturing Ltd, a venture of apparel maker Arvind Ltd and Japan’s Goodhill Corp. Ltd, it launched its formal suits business in 2014. “With Goodhill’s design and process technology, we will be able to target the premium market and offer our customers a product made in India and marked to perfection,” Arvind’s executive director Kulin Lalbhai said. “Suits is an under-serviced market in India.” Arvind, which gets a majority of its sales from supplying fabric, garments and denims to retailers, has been building its brands business and tying up with many international labels including Tommy Hilfiger, Arrow and Elle to sell their products in India. Arvind’s own brands include Colt and Excalibur as well as the retail chain Megamart. The joint venture firm has set up a suits manufacturing facility based out of the Bommansandra Industrial Area in Bangalore to produce high-end formal suits catering to the needs of both Indian and overseas customers. Started with two lines each for jackets and trousers, with a capacity to produce 350,000 pieces of jackets and 600,000 pairs of trousers annually, it expects to achieve a turnover of around Rs. 100 crores in the first year of operation. This unit manufactures suits and formal trousers catering largely to the export markets. This state-of-the-art manufacturing facility has an annual capacity of 960000 suits and trousers. With a total plant, built-up area of 90,000 square feet, the facility houses specially imported state-of-art manufacturing equipment. The major market for the firm is USA, and other markets are Canada and Mexico.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

COMPANY PROFILE NAME OF THE COMPANY

ARVIND GOODHILL SUITS MFG. LTD.

ADDRESS

63, Bommasandra Industrial Area, Bengaluru, Karnataka-560099

HEAD OFFICE YEAR OF ESTABLISHMENT

Naroda, Ahmedabad 2014

NATURE OF BUSINESS

Exporters

PRODUCT CATEGORY

Jackets and Trousers

MAIN EXPORT MARKET BUYER

US, MEXICO & CANADA Calvin Klein, J.S. Marketing, Arrow, Michael Kors, DKNY

PRODUCTION CAPACITY

AREA

80,000 pcs per month

90,000 square feet

Arvind Goodhill Suit Manufacturing

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2018

ORGANISATION STRUCTURE

Mr. Arun Babu (Operation Head)

Mr. Sameer Sinha (Technical Head)

Mr. Sanjay Joshi (Marketing Head) Mr. Ravi Bharghav (CEO)

Mr. Dharam Ranjan (Accounts Head)

Mr. Dhruv Singh (MTM)

Mr. Saju KP (HR Head)

Arvind Goodhill Suit Manufacturing

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Quality Assurance in Arvind Goodhill Suit Manufacturing The objective of the quality assurance and control department is to deliver quality product by thoroughly checking the quality of the product produced by the production department as per the requirement of the buyer. Quality Assurance department has its presence in every stages of the production. The Quality Assurance Department‘s function starts with receipt of the fabric that is, once the fabric and trims reaches the stores the quality of the fabric and trims are checked. The operation continues throughout cutting and sewing process. After each operation the quality checks are done and in the finishing department a detailed quality check is done and is sent for alteration in case of any defects, finally approve the trousers and jackets for packing. The objective of quality department is to impart quality in the product 

To ensure that the product has achieved the quality parameters of buyers.

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To restrict the defects entering into the final product

Objectives: 

To check the products for any defects

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To send back the pieces for alteration

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To recheck the altered pieces for any defects possible

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To clear the product for packing and shipment

QUALITY IN SEWING 

100% inspection of jacket & trouser is practiced in the company.

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Parts 1. Front & Back inspection report of Quality checking. 2. Front Measurement inspection random. 3. Lining inspection Quality report.

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End line 1. End line Inspection report for 100% inspection. 2. End line inspection report for random measurement Arvind Goodhill Suit Manufacturing

Graduation Project

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2018

Zone A-Top Quality Surface exposed to close customer scrutiny & affecting the finished appearance of garment. (Front & Back Section of Jacket)

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Zone B-Surface area where spots are less conspicuous to customer viewing (Under Sleeve & Upper Sleeve)

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Zone C-Internal Surface that are somewhat hidden from Customer view. (Inside of Jacket i.e. lining) -www.arvind.com

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Graduation Project Work: Introduction-For every industry or business, to get increased sales and better name amongst consumers and fellow companies it is important to maintain a level of quality. Especially businesses engaged in export have to sustain a high level of quality to ensure better business globally. The scenario today seen in global economic condition is rapid, where more focus is given on profit margin, customer demand for high quality product and improved productivity. The major challenges which are being faced by all countries apparel exporters are to bring effective solution to increase quality of product in the apparel manufacturing. Quality is a keyhole for global competition. For apparel industry, product quality and productivities are calculated right from the initial stage of raw material to the stage of finished garment. The quality control of garment industry normally depends on raw material, employee, machinery of the company. For a garment exporter there are many strategies and rules that are required to be followed to achieve good business. The fabric quality, product quality, delivery, price, packaging and presentation are some of the many aspects that need to be taken care in garment export business. In garment manufacturing, post-shipment rejection of few garments is a very usual occurrence. Reason being most of the manufacturers believe that garments are soft goods and non-repairable defect may occur due to low quality raw materials or faulty process or employee casual behaviour. However, factory must have check points to control over issue. There is no ready-made solution that can reduce rejection percentage overnight. Each order is unique. Most of the organization termed these garments as rejected because they can’t be repaired by any means. Rework in the garments industry is a common work that hampers the smooth production rate and poor products having an impact on overall factory economy. Minimization of rework is a must for quality and productivity improvement. Rework is a vital issue for poor quality product and low production rate. Reworks are the non-productive activities focusing on any activity that customer does not consider as adding value to his product. By reacting quicker in minimization of reworks to make a product as per customer demand with expected quality, the company can do some cost savings. In the recent time garments sector is becoming more volatile day by day. Garments styles are changing rapidly. However, buyers are more inimical with lead time, quality and cost. They are willing to get higher quality product with the shortest lead time offering lowest price for it. This is a common phenomenon, which is happening now in the garment sector all over the world. Being aware of the current state of the garment factories and knowing the buyers expectations and requirements, it would be very difficult to survive or to grow. Therefore, quality is becoming a vital key to successful growth of a company. The main objectives of the factories are to tide up to the lead time and the cost. Therefore, it is becoming a crying need for the factories to improve its quality level as well as productivity.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Defects signify lack of quality. The products dealt with in the garment industry are everchanging and so are the defects. Analysing finished products gives probable problematic areas for similar products in the future. Naturally, the bulk and array of data being dealt with requires a Knowledge Based System. But such systems today are not pro-active, they are rarely updated. If we know the product, then detecting defects is not hard. But defects are observed every day, in every product. The problem lies in monitoring. Human beings are susceptible to boredom and fatigue. Emotions hinder performance all the time. The garment industry is heading towards automation. Machines will soon start manufacturing clothes and the human contribution would be limited to supervision only (Drummond, 2012). Right First Time attempts to improve and stabilize the production, to avoid/minimize issues that led to the defects in the first place i.e. to get the right quality at the first time. Data can be captured for RFT at each process and it is expressed in percentage. Total audits passed in first time out of total audit conducted by auditors.

Though in many literatures on quality system, researchers have emphasized on mistake proofing system and a very few have addressed the traffic light system. Ratnayake et al. (2009) implemented cellular manufacturing with mistake proofing system in the industry for quality controlling where a team of operators focuses mainly on one part of the garment, helped when addressing most of the problems identified in the root cause analysis.

Traffic light system is a way of reducing the quality faults. It works in a way the traffic light works in the transportation system. In this paper, we have studied the traffic light system, a quality tool which was implemented in a garment industry named Arvind Goodhill Suit Manufacturing in India. The goal of this paper is to show how traffic light system can reduce the quality faults, improve the lead time and thus strengthen the supply chain. This paper will show how traffic light system can reduce the quality faults, improve the lead time and thus strengthen the supply chain. Data has been collected for one month before and after the system being implemented. The results are also shown here in this paper which tells us about the improvement of the productivity and quality level. This paper will show the reduction in re-work and frequency of defects in critical areas. The paper contains a detailed study of the defects along with Pareto analysis to identify the defects in the pre-implementation stage and also in the post-implementation stage to project the significant change.

The first section, it will deal with the study of the pre-existing re-work rate in the industry and the post-implementation re-work rate. It will comprise of all the data related to Traffic light system process. The second part will comprise of the detailed defect analysis, Pareto Arvind Goodhill Suit Manufacturing

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charts to portray the significant defects reduction. The third part will comprise of a conclusion showing the overall benefits of the company and the future aspects of this conducted project.

Need of the ProjectProblems faced in the Arvind Goodhill is that there were a lot of alterations and are not being filled by the checkers properly. The actual D.H.U were not being reported on the report, so they unable to solve the problems. Due to inline check alterations, there is lot of reworks between the lines which leads to problems in continuous material flow. Rework on 1 = Making 3 new garments so it is always a profitable decision to avoid reworks. Further, in most of the cases the industry fails to provide the correct rework percentage and provides a somewhat believable percentage by the end of the day just for the sake of it, control planning will give us the real image of the reworks which at the first place is necessary to improve. Presently, the DHU% in factory is 7.78%. The target is to reach less than 5%.

Objective To ensure the First Quality from each operation right at first time. Sub Objectives:  

Defect Reduction To implement a standardise system for Garment Production & Quality Control

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Literature ReviewCase Study-1 Right-First-Time - An Approach To Meeting The Demands Of Today’s Customer - David W Farrington-Meridian Fabrics, Courtaulds plc, PO Box 54. Haydn Road, Nottingham NG5 1DHPresented at the Societys International conference held at the University of Leeds. There are probably three areas that need examining if were to justify the approach of a ‘right-first-time’ objective within business. These are: 

The market place - its expectations and requirements

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The competition

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The ‘in-house’ benefits of doing it - opportunity and cost saving.

An analysis of the work done at Meridian to achieve first-quality production at the first time of asking shows that it is through a combined programme of four interdependent key ingredients: 

Employee involvement and participation

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Attention to detail across the business

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Closer relationships with both customers and suppliers

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Capital investment.

It has been tried to identify why a right-first-time approach is important, and then to pull together the elements to make it a reality. Earlier they had a customer survey carried out by an independent consultant. This highlighted the following main points: 

The general product quality is recognized as being good

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Our delivery on-time performance is reasonable, but it takes too long (i.e. our promised turn-round times exceed the customer’s need)

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When something goes wrong. Then it takes too long to put it right.

Similarly the consultant identified the need for a quick response in order to protect and to obtain business. Unless the quality of the product and of the business is founded firmly on a basis of a right-first-time philosophy and reliability, then there can be no achievement of QR because the accuracy of response will not be possible. Arvind Goodhill Suit Manufacturing

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The textile and apparel industry needs to respond by advertising itself as offering opportunity and challenge; it must set out to attract people of high calibre. Sewing percent defective reduced to approximately 40%. In finishing, stitching D.H.U. came down to approximately 8% from 16% as earlier, uncut thread D.H.U. came down to approximately 10% from 22% as earlier. Rework increased the cost of the different work categories between 2% to 30%. However, some best practices to control defect generation within the factory were suggested as- Make the workplace clean – from fabric store to cutting to sewing to washing and finishing. Place quality control system in proper place. Implies that sufficient no. of checkers, trained checkers, checkers making report while checking, analysis of reports and take action based on the quality check reports. Conduct training program for the checkers on how to check piece correctly to capture defective pieces. Train them to make garment checking reports. Run quality awareness program for your employees. Quality standard must be understood by each employee and everybody have to work to meet quality goal. No low standard work should be accepted by the following department. In sewing line don’t allow operators to keep bundles open and each bundle must be completed before forwarding to the next. It will help you track missing pieces. It is usual experience that operators throw pieces under tables when they make mistake or receive defective (incomplete) garments from previous operator. Nobody keeps track of these missing pieces until you found shortage of garments in finishing. Set standard operating procedures (SOP) for each task performed by your employees. SOP for quality control system for each department. Set audit team to audit your quality system in a regular interval. These recommendations were suggested to the individual department. The suggestive tools developed in this article cover a comprehensive series of aspects in minimizing reworks in the sewing section of apparel industries by ensuring quality production. The importance of the textile industry in the economy of Bangladesh is very high. The explosive growth of the RMG industry in the country, however, has not been enough supported by the growth of backward linkage facilities. So manufacturing the quality product is mandatory to sustain in this global competitive market. Quality is ultimately a question of customer satisfaction. Good Quality increases the value of a product or service, establishes brand name, and builds up good reputation for the garment exporter, which in turn results into consumer satisfaction, high sales and foreign exchange for the country. The Arvind Goodhill Suit Manufacturing

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perceived quality of a garment is the result of a number of aspects, which together help achieve the desired level of satisfaction for the customer. However, we should bear in mind that 1% defective product for an organization is 100% defective for the customer who buys that defective product. The study clearly indicates that by eliminating non-productive activities like reworks in the apparel industries time as well as cost are saved by ensuring quality production which have an important impact on overall factory economy. Case Study-2 This study was conducted by Md. Mazharul Islam and Md. Sadequr Rahman. They conducted this study at a composite knitwear manufacturing unit in Bangladesh. Their project was based on Traffic Light system implementation to reduce the alteration rate and enhance the productive capacity. Their paper comprises of the pre-implementation and post-implementation scenarios to show the significant difference.  The project shows drastic change in the alteration rate from 12% to 4% after implementing Traffic Light system.  About 0.38 day was saved from each line for rejection, which was 14 days for the whole factory.  About 0.19 day was saved from each line for alteration, which 7 days in terms of production. The pre-implementation stage showed 925 days production for one line for a month and after the implementation, the value got reduced to 878 days. This shows a reduction of 47 days of production for one line for a month. Total savings in terms of money for a particular month was 62,000 US Dollars. So, it could be concluded that Traffic Light system can be implemented in any garment manufacturing company to improve quality, reduce costs, reduce lead time thus resulting in stronger supply chain. Case Study-3 ‘An application of Pareto analysis and cause effect diagram for minimization of defects’ IJMER, Vol. 3, by Tanvir Ahmed, Raj Narayan Acharjee, Noman Sikdar. As Readymade Garments sector is a large industrial sector in Bangladesh, quality improvement can play a vital role for improving productivity as well as economic development for the country. This review published by Tanvir Ahmed, Raj Narayan Acharjee, Noman Sikder- 2013 represents a detail investigation on quality improvement of a garment factory by applying: 

Cause-Effect Diagram.

The aim of this study is to minimize defects that will reduce rework and rejection rate At present the success of the Readymade Garments sector highly depends on several factors such as manufacturing lead time, quality of product, production cost etc. These factors are Arvind Goodhill Suit Manufacturing

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hampered due to various defects in the products. These defects can be repairable that leads to rework or non-repairable that leads to rejection. Rework in the garments industry is a common work that hampers the smooth production rate and focus poor quality products having an impact on overall factory economy. Minimization of reworks is a must in quality and productivity improvement. Rework is a vital issue for poor quality product and low production rate. Reworks are the non-productive activities focusing on any activity that customer are not willing to pay for. Non-productive activities describe that the customer does not consider as adding value to his product. An application of Pareto analysis and cause-effect diagram for minimizing defect percentage in sewing section. By reacting quicker in minimization of reworks to make a product as per customer demand with expected quality, the company can invest less money and more costs savings. Whereas rejection causes waste and deceases resource efficiency. In this review four months defect data has been collected from the management and Pareto Analysis is performed on them. From the analysis top defect positions are identified where 78.56% defects occur. On those top positions further Pareto Analysis is performed to identify the top defect types. That resulted in total 115 concerning areas where 71.40% defects occur, which should be the major concerning areas to reduce defect percentage. So hierarchies of causes for individual defect types are organized and Cause-Effect Diagrams are constructed for those defect types. Then relative suggestions to those causes are also provided. Case Study-5 Dalgobind Mahto in this paper, it gives details of root cause analysis methods and techniques in identification quality of major key characteristics in manufacturing process. It is very risk in identifying problem in multistage operation. In this paper, root cause analysis was adopted to reduce the defect rate in cutting operation in CNC machines. This study dives detail structure to solve human related problem in manufacturing process. This study gives an idea for stakeholders to promote effective and better solution all time. This paper presents on use of Pareto chart and Cause and Effect diagram in analyzing the defect caused in garment industry. This papers aims at reducing the defect rate caused while stitching clothes. Using these methods it was identified about 80% defect rate in process of stitching. The top five defects was identified and analyzed. Using cause and effect diagram causes and effect are constructed. The study provided suggestion to reduce the defect rate. Thus this papers gives idea of how effectively minimizing the rework and defect rate. Terms (Quality Gurus)By Dr.Joseph M.Juran(1989): Quality means • “Fitness for use” – means if the product we bought has some deficiency, we can’t use it, so in that case we can say that the product is defective • He proposed that companies should judge fitness for use of a product from a customer’s view point and not from a manufacturer’s or seller’s viewpoint. Arvind Goodhill Suit Manufacturing

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Juran’s Trilogy(1999)• •

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Quality Planning: The structured process for designing products and services that meet new breakthrough goals and ensure that customer needs are met. Quality Control: can also be described as “a process for meeting the established goals by evaluating and comparing actual performance and planned performance, and taking action on the difference.” Quality Improvement: The process for creating breakthrough levels of performance by eliminating wastes and defects to reduce the cost of poor quality

By Philip Crosby(1979): • • •

“quality is free” Means that production costs are the same for items that do and those that do not meet specifications and standards Production and material costs are the same for first quality products (those that meet the standards) as for sub-standard products (those that do not meet standards)

Companies have same investment in a sub-standard product as in first-quality product Kaoru Ishikawa: 

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Ishikawa Diagram-identifies many possible causes for an effect or problem. It can be used to structure a brainstorming session. It immediately sorts ideas into useful categories. Used-When identifying possible causes for a problem

Research Methodology            

Secondary Data Collection Primary Data Collection & Analysis (JANUARY) Pareto Analysis of data Identification of Top 5 defects. (EACH SECTION) Brain Storming (Cause & Effect Diagram and Why Why Analysis) Devising of Standard Provide the solution for the defects (Traffic Light System) Training for the given solutions Implementing the given solutions Analyzing the improvement after implementation using the same procedure Comparing both the data Result Analysis & Documentation

Arvind Goodhill Suit Manufacturing

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This study contains use of quality tools to minimize defects and rework on garment industry. It includes the theoretical ideas about various defects, various quality tools specially Pareto Analysis and Cause-Effect diagram. Data Collection To find out about the various defects for 3 ongoing styles by collecting data with the help of end line checking formats and in line audit formats by checking the pieces thereby with the checker to evaluate the defective rates and DHU%. Pareto Analysis The data will be represented in Pareto charts where the lengths of the bars represent frequency of the defects which will be arranged with longest bars on the left and the shortest to the right. It is based on the 80-20 principle which means 80% problems are caused by 20% defects. Again further Pareto analysis is done to show the most dominant occurring defects out of the pool of top defects. Cause and Effect Analysis After finding out the major defects through the Pareto analysis, Cause and effect analysis was used to find out the possible causes or the root causes for the major defects. Why Why Analysis The primary goal of technique is to determine the root cause of a defect or problem by repeating the question “Why”. First Why, Second Why, Third Why, Fourth Why and Fifth Why: a Root Cause Implementing Effective Solutions and Data Collection The root causes were identified in the form of man, machine, material and method and corrective actions were proposed thereby. After that, solution were proposed including Traffic Light System, data was again collected for the last 30 days of the internship period in order to compare the initial and final scenario.

Analysis and Interpretation When number of defects in a garment is high it seemed as worse quality garment and when defects are less then it is a better quality garment. In checking, quality checker detects defects in garments and separate defective garments from good pieces. Where there is established quality system, quality checker records total number of defects found in the garments checked by her/him in a day and also she/he records the number of defective garments where those defects are found. The definition and formula for calculating DHU and Percentage Defectives are given below: Defective Pieces-Defective pieces are those pieces, which are separated for alteration during checking may be for any causes. For the quantitative measure there is two measuring unit as Defects per hundred units and percentage defective.

Arvind Goodhill Suit Manufacturing

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Defects per hundred units (DHU) – number of total defects in 100 checked garments. Formula for calculating DHU is = (Total no. of defects found/Total pieces checked) x 100 Percentage Defective (%) – total number of defective pieces in 100 checked garments. Percentage defective = (Total no. of defective pieces / Total pieces checked) x 100 For the purpose of analyzing the frequency of the defects in the sewing department, a style was chosen and 1 month defect data was collected in January 2017.The details of the styles are described and the end line inspection data, front section inspection data, lining section inspection data and sleeve section inspection data for each style is presented in a tabular form.

JACKET LINE Each jacket is comprised of 4 sections: 

Sleeve

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Lining

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Body

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Assembly

The SAM for a basic Jacket is approx. 70-85 min. The Jacket line follows the UPS system of production. The material moves on pneumatically controlled overhead hangers, which uses the principle of gravity beautifully.

Product Detail • • • • •

Product Description : Jacket (Sports) Brand : Calvin Klein Buyer: PVH Corp Style No: CK GP-3001 Colours: Grey and White

Arvind Goodhill Suit Manufacturing

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Data Analysis for this Style for January

Arvind Goodhill Suit Manufacturing

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PARETO ANALYSIS Pareto analysis was performed for the given style based on Jan month defect data from the sewing section. From this analysis “Vital few” areas where maximum defects occur were identified. Here horizontal axis represents defect types, vertical axis at left side represents no of defect amount. The amount of defects has been represented by vertical bars with their respective defect amounts have been represented by the blue coloured bars. Name of Defect No. of Defect % Defect Cumulative % Defects Fusing Mark Vent stitch open Fabric Damage Fit label missing Armhole lining lock open/miss Keyhole miss/open/uneven/broken Weaving Armhole open/jump/uneven stitch Wash care label open Sleeve Edge (kinari) open Main label miss/cross Pocket bag open Sleeve Elbow/Inseam open Sleeve cuff open/Button miss D/1 bar tack miss/open Vent up and down Armhole stitch visible Stain Lapel point open/jump stitch Collar zigzag open/broken/open/jump stitch Side seam open/loose stitch/puckering Lapel shape out Front to Front up and down OBW PKT ZIGZAG Flap uneven/open Pick up and down Shoulder seam open/loose/uneven Size label wrong/miss

51 42 36 31

11.33% 9.33% 8% 6.89%

11.33% 20.66% 28.66% 35.55%

29

6.44%

41.99%

28 27

6.22% 6%

48.21% 54.21%

27 24 22 22 19

6% 5.33% 4.89% 4.89% 4.22%

60.21% 65.54% 70.43% 75.32% 79.54%

17

3.78%

83.32%

16 14 11 8 6

3.56% 3.11% 2.11% 1.77% 1.33%

86.88% 89.99% 92.1% 93.87% 95.2%

4

0.89%

96.09%

3

0.66%

96.75%

3 3

0.66% 0.66%

97.41% 98.07%

2 1 1 1

0.44% 0.22% 0.22% 0.22%

98.51% 98.808% 99.106% 99.404%

1 1

0.22% 0.22%

99.702% 100%

Pareto Analysis data for Jacket Endline Section Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Endline Section Pareto Chart 50 40 30 20

51 42 36

31 29 28 27 27

24 22 22

19 17 16 14

10 0

11

8 6 4 3 3 3 2 1 1 1 1 1

Fusing Mark Vent stitch open Damage Fit label miss Armhole lining lock… Keyhole… Weaving Armhole… Wash care label open Sleeve kinari open Main label miss/cross Pocket bag open Sleeve Elbow/Inseam open Sleeve cuff open/Button miss D/1 bar tack miss/open Vent up and down Armhole stitch visible Stain Lapel point open/jump stitch Collar zigzag… Side seam open/loose… Lapel shape out Front to Front up and down OBW PKT ZIGZAG Flap uneven/open Pick up and down Shoulder seam… Size label wrong/miss

Frequency of Defcts

60

Type of Defect

Pareto Chart for Jacket Endline Section The data collected from Jacket Endline Section has been plotted in Pareto chart and it was found that 80% problems are caused due to 20% defects. The 20% defects are Fusing Marks, Vent Stitch Open, Damage, Fit Label Miss and Armhole Lining Lock open/miss. Observations from Pareto chart Analysis for Major Defects: • Fusing Marks are most common defect with as much as 11.33% of total •

Vent stitch open is second most common defect with as much as 9.33% of total.

•

Damage is third most common defect with as much as 8% of total.

•

Fit label miss is fourth most common defect with as much as 6.89% of total.

•

Armhole lining lock open/miss is fifth most common defect with as much as 6.44%.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Further Pareto Analysis of Endline Section Name of Defect

No. of Defects

Fusing Mark Vent stitch open Damage Fit label miss Armhole lining lock open/miss 60

% Defect

Cumulative % Defect

51 42 36 31

26.98413 22.22222 19.04762 16.40212

26.98 49.2 68.24 84.64

29

15.34392

100

Endline Top 5 Defects 51

50

Frequency of Defect

42 40

36 31

29

30

20

10

0 Fusing Mark

Vent stitch open

Damage

Fit label miss

Armhole lining lock open/miss

Endline Top 5 Defects

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Pareto Analysis data for Jacket Front SectionName of Defect

No. of Defects

Seam Open Damage flap bartack miss/ finishing not good marging uneven bottom pocket shape out bottom pocket flap shape out sticker no. wrong blind hem visible on shell SPI wrong OBW bag open/loose flap lining out flap width uneven chest pad tape alignment wrong Raw edge at OBW corner OBW basting open/miss Side vent up down chest pad alignment wrong OBW bag shape out Vent finishing/folding wrong

% Defect

Cumulative % Defect

28 18

23.33333 15

23.333 38.333

13 13 8

10.83 10.83 6.667

49.163 59.993 66.66

5 5 4 4 3 3 3

4.167 4.167 3.333 3.333 2.5 2.5 2.5

70.827 74.994 78.327 81.66 84.16 86.66 89.16

3 2 2 2 2 1

2.5 1.667 1.667 1.667 1.667 0.833

91.66 93.327 94.994 96.661 98.328 99.161

1

0.833

100

Front Section Pareto Chart Frequency of Defects

30

28

25 20 15 10 5

18

13

13 8 5

5

4

4

3

3

3

3

2

2

2

2

1

1

0

Pareto Chart for Jacket Front Section Arvind Goodhill Suit Manufacturing

Graduation Project

2018

The data collected from Jacket Front Section has been plotted in Pareto chart and it was found that 80% problems are caused due to 20% defects. The 20% defects are Seam Open, Damage, Flap Bartack miss/Finishing not good, Marging Uneven and Bottom Pocket Shape Out. Observations from Pareto chart Analysis for Major Defects: • Seam Open are most common defect with as much as 23.33% of total •

Damage open is second most common defect with as much as 15.33% of total.

•

Flap Bartack miss/Finishing not good is third most common defect with as much as 10.83% of total.

•

Marging Uneven is fourth most common defect with as much as 10.83% of total.

•

Bottom Pocket Shape Out is fifth most common defect with as much as 6.67%.

Further Pareto Analysis of Front Section Name of Defect

No. of Defects

Seam Open Damage flap bartack miss/ finishing not good marging uneven bottom pocket shape out

30

28

% Defect

Cumulative % Defect

28 18

35.00 22.50

35 57.5

13 13

16.25 16.25

73.75 90

8

10.00

100

Front Top 5 Defects

Frequency of Defect

25

20

18

15

13

13

10

8

5

0

Seam Open

Damage

flap bartack miss/ marging uneven finishing not good

bottom pocket shape out

Lining Section Top 5 Defects Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Pareto Analysis data for Jacket Lining SectionName of Defect

No. of Defects

Frequency of Defects

Collar point up & down Notch mismatch label miss/reverse/size wrong/po change Lining Edge (kinari) stitch uneven stain/fusing mark piping uneven main label/size label/washcare label missing pocket bag closing wrong/shape out spi seam open/loose/skip Gorg seam margin uneven Loose stitch/jump stitch/skip/open weaving/damage Collar stand width uneven s/h seam marging uneven/notch out

50 45 40 35 30 25 20 15 10 5 0

% Defect

Cumulative % Defect

43 29

23.11 15.59

23.11 38.7

25 23 17 15

13.44 12.36 9.139 8.06

52.14 64.5 73.639 81.699

11

5.914

87.613

6 4 4 3

3.22 2.15 2.15 1.613

90.833 92.983 95.133 96.746

2 2 1

1.075 1.075 0.537

97.821 98.896 99.433

1

0.537

100

Lining Section Pareto Chart

43

29

25

23 17

15

11 6

4

4

3

2

2

1

1

Type of Defects

Pareto Chart for Jacket Front Section

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

The data collected from Jacket Lining Section has been plotted in Pareto chart and it was found that 80% problems are caused due to 20% defects. The 20% defects are Collar point up & down, Notch miss-match, Label miss/reverse/size wrong/PO change, Lining Kinari stitch uneven and Stain/Fusing Mark. Observations from Pareto chart Analysis for Major Defects: • Collar point up & down are most common defect with as much as 23.11% of total •

Notch miss-match is second most common defect with as much as 15.59% of total.

•

Label miss/reverse/size wrong/PO change is third most common defect with as much as 13.44% of total.

•

Lining Kinari stitch uneven is fourth most common defect with as much as 12.36% of

total. •

Stain/Fusing Mark is fifth most common defect with as much as 9.13%.

Further Pareto Analysis of Lining SectionName of Defect

No. of Defects

Collar point up & down Notch miss-match label miss/reverse/size wrong/PO change Lining Kinari stitch uneven Stain/Fusing mark

50 45

43

% Defect

Cumulative % Defect

43 29

31.38686 21.16788

31.38 52.54

25

18.24818

70.78

23 17

16.78832 12.40876

87.56 100

Lining Top 5 Defects

Frequency of Defect

40 35

30

29

25

25 20

23 17

15 10 5 0

Collar point up & Notch missmatch label Lining kinari stitch stain/fusing mark down miss/reverse/size uneven wrong/po change

Lining Section Top 5 Defects Arvind Goodhill Suit Manufacturing

2018

Graduation Project

Pareto Analysis data for Jacket Lining SectionName of Defect

No. of Defects

% Defect

sleeve cuff open sleeve button wrong/open/loose elbow seam puckering Sleeve vent up and down Fusing mark Lining elbow open Inseam open fabric weaving sleeve keyhole placement wrong sleeve button missing Damage sleeve colour shade variation sleeve fullness/puckering

40

37

36

Cumulative % Defect

37

16.66

16.66

36 35 30 21 17 12 12

16.21 15.76 13.51 9.45 7.657 5.405 5.405

32.87 48.63 62.14 71.59 79.247 84.652 90.057

9 6 3

4.054 2.702 1.351

94.111 96.813 98.164

2 2

0.9 0.9

99.064 100

Sleeve Section Pareto Chart

35

35 30

Frequency of Defect

30 25 21 20 15

17 12

12

9

10

6

5

3

2

2

0

Type of Defects

Pareto Chart for Jacket Front Section

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

The data collected from Jacket Sleeve Section has been plotted in Pareto chart and it was found that 80% problems are caused due to 20% defects. The 20% defects are Collar point up & down, Notch miss-match, Label miss/reverse/size wrong/PO change, Lining Kinari stitch uneven and Stain/Fusing Mark. Observations from Pareto chart Analysis for Major Defects: • Collar point up & down are most common defect with as much as 23.11% of total •

Notch miss-match is second most common defect with as much as 15.59% of total.

•

Label miss/reverse/size wrong/PO change is third most common defect with as much as 13.44% of total.

•

Lining Kinari stitch uneven is fourth most common defect with as much as 12.36% of

total. •

Stain/Fusing Mark is fifth most common defect with as much as 9.13%.

Further Pareto Analysis of Sleeve SectionName of Defect

No. of Defects

Collar point up & down Notch miss-match label miss/reverse/size wrong/PO change Lining Kinari stitch uneven Stain/Fusing mark

% Defect

Cumulative % Defect

43 29

31.38686 21.16788

31.38 52.54

25

18.24818

70.78

23 17

16.78832 12.40876

87.56 100

Frequency of Defect

Sleeve Top 5 Defects 40 35 30 25 20 15 10 5 0

37

36

35 30 21

Sleeve Top 5 Defects Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram Cause & Effect Diagram-Through this analysis the root causes of major defects were identified and solutions were provided thereby. Cause & Effect Diagram for Fusing Marks-

Cause and Effect Diagram for Stain

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram for Seam Open

Cause and Effect diagram for Fit Label Miss

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram for Label Miss/Reverse/Size Wrong

Cause and Effect Diagram for Flap Bartack Miss/Finishing not Good

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram for Marging Uneven

Cause and Effect Diagram for Bottom Pocket Shape Out

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram for A/H Lining Lock Open/Miss

Cause and Effect diagram for Elbow Seam Puckering-

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram of Vent Stitch Open-

Cause and Effect Diagram for Damage-

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Cause and Effect Diagram for Lining Kinari Stitch Uneven-

Cause and Effect Diagram for Collar Point Up & Down-

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Why Why Analysis Why Why Analysis-The primary goal of technique is to determine the root cause of a defect or problem by repeating the question “Why”. First Why, Second Why, Third Why, Fourth Why and Fifth Why: a Root Cause  Pay attention to the logic of cause effect relationship  Make sure that root causes certainly lead to the mistake by reversing the sentences created as a result of the analysis with the use of expression “and therefore”  Look for cause step by step. Don’t jump to conclusions. Benefits of 5 Whys  

Help Identify the root cause of a problem. Determine the relationship between different root causes of a problem. One of the simplest tools; easy to complete without statistical analysis

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Solutions for Why Why Analysis

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Mock Development We have developed 3D mock of the critical operations for the operators to clearly what to do that particular operation. In case of any confusion they can refer to mock and not need to ask Q.C for the operation. They are hung on the machine in front of the operator so that operator always knows what is correct and what is wrong. They are also provided with important information and measurements for the operations.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Template for Sleeve Vent Up & Down Defect

Operator should place the sleeve end at the edge of tape to prevent the defect.

Use of White Gloves to prevent Stains

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

After the work cover the ready pieces to prevent stains.

Use of Mock for Main & Fit Label

Fusing Tape left carelessly over the workstation

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Proper Placement of Fusing Tape

Attachment of Metal Wire Guide to prevent Roping/Piping Uneven

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Training Regarding Collar Attachment

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Setting up of Correct Thread Tension

Daily Meeting of Line Quality Controller with the Inline & Endline Checkers

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Template development for Main Label Attachment

Template for Bottom Vent Up & Down

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Teflon Coated Pressure Foots and Throat Plate should be used to prevent oil marks leaking from Machine.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Standard Operating Procedure (SOP) Development •

To provide standard operating procedure to the department.

•

Helps to distinguish right or wrong methods of operations.

•

To provide step-by-step written procedure about how to do a job

•

SOP gives desired result and maintains consistency in results.

•

SOP helps (operator) who is going to do a particular job by ensuring success method of doing a job.

Existing Sheet

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Implemented Sheet

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

The above Standard Operating Procedure (SOP) was made for   

All Critical Operations of Jacket Sleeve Section Body Assembly Section Sleeve Assembly Section

We have also made the SOP for the Critical Operations of Trousers and in Kannada also.

Implemented Sheet (Trouser)

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Implemented Sheet (Trouser) in Kannada

SOP Implementation

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Quality Posters We have also made Quality Posters for the Operators in English, Kannada and Oriya. Quality Posters are made to give information about the policies of Quality Department of Company and about the Traffic Light System.

QUALITY

We Aim For Zero Defect

Quality Poster (English/Kannada)

Cleanliness is everyone's duty , To enhance the line's beauty

Cleanliness Quality Poster (English/Kannada)

Take A Trial Before You Start Stitching (Morning ,After Lunch, After Maintenance)

(

,

,

)

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Trail Taking Quality Poster (English/Kannada)

Make Machine Oil/Dust Free After Maintenance, Avoid Stain

Avoiding Stain Quality Poster (English/Kannada)

G R Y

NO DEFECT

1 DEFECT NEW/CHANGE IN OPERATOR

Follow Traffic Light, Avoid Defect

Traffic Light Quality Poster (English/Kannada)

QUALITY

We Aim For Zero Defect

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Quality Poster (English/Odiya)

G R Y

NO DEFECT

1 DEFECT NEW/CHANGE IN OPERATOR

Follow Traffic Light, Avoid Defect

Traffic Light Quality Poster (English/Odiya)

Poster implementation near Punching Machine

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Poster Implementation Quality Checking MethodsGARMENT OUTSIDE FRONT 1. Top and under collar A. Top collar Put the garment on hanger (Front side towards you). To check the top collar uses both hands. Checks the following Uneven collar edge, collar shape (pic-1)

B. Hanger loop Check the following Tacking tight, Secured tacking, tacking even

C. Under collar Turn the garment (Back side towards you). To check the under collar lift the top collar up. Check the following Under collar zigzag secured, up and down stitch, Under collar should be inside 2 mm from top collar edge (pic-1) Arvind Goodhill Suit Manufacturing

Graduation Project

2018

2. Lapel point Turn the garment (Front side towards you). Check the following Check both side lapel together for evenness. (Pic-2) Lapel peak width on both sides Up and down between both lapels Lapel peak point should be sharp Piping reverses at lapel peak and notch

(Pic-2)

Lapel peak shape.

3. Shoulder seam To check the seam uses both hands to stretch the seam. Check for seam shape, fullness distribution equal (Pic-3) 4. Lapel and Front edge A. Lapel Keep garment front side towards you Check the following Lapel should be straight upto 10 cm Lapel width should be even on both sides Lapel should be rolled properly lapel break point distance should be 1/2” above the Button and button hole. Lapel point to lapel break point facing panel cover front panel and after break point to bottom front panel cover facing panel (2mm )(Pic-4) (Pic-4)

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

B. Button and button hole at front Hold the button with both hands Check the following Button and button hole position Button hole should be straight Button wrapping and fraying thread on button hole

D. Front edge Hold the front edge on your hand and start moving your hand from top to bottom and Check the following Piping visible on both sides Tipping and piping width based on style Front edge up and down. Front edge shape (Pic-5) (Pic-5) E. Bottom shape Use both hands to check the bottom shape and check the following

Bottom shape Bottom up and down on both sides Lining should be inside 3 mm at bottom shape. 5. Breast pocket Check the following Breast pocket should be neat and clean. Check the zigzag stitch secured on the edges. Inserts the finger on both corner of breast pocket to check finger gaps are even on both the side. Check the long stitch secured in the breast pocket Breast pocket width Pocket corner pleat, dimple mark. Arvind Goodhill Suit Manufacturing

Graduation Project

2018

6. Waist pocket and Dart seam A. Waist pocket To check waist pocket, lift the flap to you. Check pocket long stitch inserts the finger on both corner Of the pocket. Check the following Waist pocket corner pleat, open, dimp. Flap lining should be inside . Waist pocket flap shape.

B. Dart seam Check the following Dart shape No dimple at end of the dart Dart length should be equal on both sides. 7. Sleeve Check the following:

A. Even Crown shape Use both hands to check the crown shape. Crown shape should be even . Sleeve fall should be even on both sides

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

B. Armhole seam To check the seam use the both hands to stretch the seam Check the following Notch matching , seam margin (1 cm) Thread visible. 8. Elbow seam, in seam and button, button hole A. Elbow seam and in seam Use both hands to check elbow and in seam by stretching the seam

B. Sleeve button and button hole To check sleeve button, keep the sleeve facing towards to you Check the following Sleeve button match on both sides Button to button gap Button position from sleeve bottom and vent edge. Button to button up and down between both sleeves Sleeve bottom shape should be even. Sleeve vent length should be even on both side.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

C. Sleeve bottom shape and cuff Turnover the bottom edge and check the following Bottom edge shape Double crease at cuff (Pic-14)

9.

Scan front and sleeve Scan front and sleeve for fabric flows and defects (if any).

1

3

1

2

3 2

7

7 4

4

5

9

6

6 8

8

4

4

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

GARMENT OUTSIDE BACK

10. Center back seam Turn the garments back panel facing side towards you. Check the seam use both hands to stretch the seam. Check the following Center back seam should be straight. Waviness, puckering

11.

Side seam and vent edge (kinari)

A. Side seam To check the side use both hands to stretch the seam Check the following Side seam puckering

B. Vent Edge (kinari) To check the vent kinari lift the center back panel Check the following Kinari piping Thread colour matching Seam should be straight Hold the vent one above another to check vent up and down.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

12. Bottom shape Hold the bottom edge in both hands and check the following Bottom shape is correct Lining should be inside 1.5 cm at back side bottom.

13. Scan back Scan back for fabric flows and defects (if any).

13

10

11

11

12

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

GARMENT INSIDE FRONT 1.Gorge seam Turn the garment lining front towards to you. Use both hands to check the gorge seam by streching the seam. Gorge seam should be straight.

2.Shoulder seam Use both hands to check the shoulder seam by streching the seam. Check the following Margin should be even . Fullness distribution equal.

3. Lining front to facing seam Use both hands to check the linin front to facing seam by streching the seam Check the following Even fullness distribution Looseness,Puckering 4.Welting pocket and label A.Welting pocket Check the following Pocket corner pleat and open. Smiling of welting pocket . D-bartack both side , puckering.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

B. Main label Check the following Brand name and spelling . Label colour . Zigzag stitch open and uneven. C. Wash care label Check for brand name, size and spelling, content, model and style code, work order . 5.Side seam and Armhole seam A. Side seam Use both hands to chek the side seam by streching the seam. Check the following Side seam even and flat.

B. Lining armhole seam Check the following Armhole shape even Thread visible.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

6. Lining bottom seam Use both hands to check the lining bottom seam by streching the seam. Check the following Lining should be inside.

7.Scan front lining Scan front lining for fabric defects (if any).

1

2

3

1

2

3

4 4

7 4

5

5

6

6

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

GARMENT INSIDE BACK 8.Lining center back seam and Edge (Kinnari) A.Lining center back Check for lining center back seam use both hands to stretch the seam Check the following Pleat folding even. Seam should be straight. Puckering Lining tight at bottom. (Pic-30)

B.Vent Edge (kinari) Check the following Seam should be straight . Lining tight at vent .

9. Side panel to lining center back seam Checking for side panel to lining center back seam use both hands to stretch the seam Seam should be flat.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

10. Lining bottom seam Use both hands to check the lining bottom seam by streching the seam. Lining should be inside 1cm from bottom. 11. Scan front lining Scan back lining for fabric defects.

11

8 9

9

10

10

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Implementation

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Quality Checking Videos We have also made the Quality Checking Videos to help in training of checkers and enable them to check the garments properly and identify the defects easily. The videos are made for all four sections of Quality Checking Points   

Front Lining Sleeve Body Assembly

At last we have also made the video of Endline Checking. The videos list the important focus points and areas for defect checking. Some of the screenshots of videos are as follows-

Endline Checking

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Front Section Checking

Lining Section Checking Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Sleeve Section Checking

Body Assembly Section Checking

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Traffic Light System •

Traffic light system is the most effective inspection tool to reduce defect generation at source.

•

It is a random inspection system.

•

Traffic light system is more effective in controlling shop floor quality than other quality tools because of its visual communication.

•

At the same time it measures operator’s performance level in quality.

•

No operators like to be presented themselves as lower quality makers. They concentrate on quality aspect during stitching garments. Inline checking system will alert operators in concentrating their job. If less number of defective seam/stitches is made, less the time will be lost in repairing it. It also helps in other way.

•

Traffic Light System is designed to flag the problem at source and allow immediate corrective action rather than all potentially defective products to continue to be manufactured.

BackgroundThe Traffic Light System is specifically aimed:•

To improve first time quality of the product by bringing precision and consistency in execution of operations, by making operators cognizant with their performance level and accuracy at specified intervals.

•

This will lead to rise in efficiency of operators, resulting in growth in output of the factory; as well as reduction in number of defected pieces at the end of sewing lines, subsequently saving the time and the cost incurred by the company on their reworking and alteration.

Need for the Traffic Light System•

REDUCING ALTERATION RATE - Higher alteration rate stands as a severe bottleneck in achieving an uninterrupted production flow, and adding up unnecessary production cost and time.

•

INCREASING OPERATOR EFFICIENCY - Operator efficiency is a very crucial factor in determining the attainment of specified target in production lines. Higher efficiency is directly proportional to better productivity.

Arvind Goodhill Suit Manufacturing

Graduation Project

•

2018

QUALITY IMPROVEMENT- Quality is the one of the most important aspects which influences the sale-ability of the product. Consumer satisfaction is highly based on the quality being offered. Thus, it is very essential to have an avant-garde approach towards quality.

Definition of Traffic Light Quality System •

In the production line each worker is characterized by a card and the card is being hung over the machine.

•

Green is for good quality, Red stands for quality alert and Yellow is for new operator/change in operator. Five piece quality checking system, is to be implemented and the quality controllers are to be instructed to check the quality status.

•

Every one hour, collect data of critical operators on a regular basis taking five samples of each critical operation.

Procedure •

When a worker produces quality product with zero defect, he or she is characterized by a Green card.

•

But when any worker who does a single fault out of the checked five pieces, a Red card is hanged above his or her head that indicates that this worker is producing faults that should be corrected and an extra care should be taken to this worker.

•

Yellow card is hanged above the head of new operator/ if the operator is changed due to any reason indicating that line Q.C. and Line Supervisor must be extra careful about him or her.

Pre-implementation process• • • • • •

Step 1: Studying of the productivity of the line with respect to the running style on a single day. Step 2: Separation of defect-free and pieces with defects during end line inspection. Step 3: Classification and recognition of defects on the basis of intensity of occurrence. Step 4: Calculation of total production and no. of pieces send back for rework\ alteration. Step 5: Continuation of the process for a week (6 working days). Step 6: Determination of average production and average percentage alteration.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Implementation process • • • • •

Step 1: Putting of formats of Traffic Light System on critical operations Step 2: Checking of pieces and filling of formats with prescribed colors by In-line QC on hourly basis for the whole day. Step 3: Calculation of total production and no. of finished pieces send for rework\ alteration in terms of percentage at the end of the day. Step 4: Continuation of the process for 3 weeks Step 5: Determination of average production and average percentage alteration per week. Step 6: Comparison of the difference of average productivity and percentage alteration pre and post implantation of Traffic Light System.

Defect, Colour Code & Frequency of Q.C Visit- Below table is showing the stages of Sewing Quality AssuranceDefect Found None

Colour code

One

New Operator/ Change in Operator

Yellow

Signature

Action Required

Q.C.

None

Q.C.

Inform Operator Supervisor, Line Chief Production Manager to make necessary correction of the process: Show the correction method to the operator.  Involve the mechanic to solve sewing machine problem.  If Operator cannot reach the quality level of the process, sewing operator change is required.  If sewing machine problem can’t be solved, need to replace the machine.  If machine cannot be replaced stop the machine operation. Line Q.C. Should be extra careful while dealing with New Operator. He or She should know the correct method for the operation and have necessary skills required for the operation.

Q.C.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Data Collection FormatsTraffic Light Audit Sheet: For individual operators. Inspection is to be done hourly in Eight shifts.

Analysis Format-

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Implementation

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Implementation

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Data Analysis Pre-Implementation StageFirst Week• Product Description : Jacket (Sports) • Brand : Calvin Klein • Buyer: PVH Corp • Style No: CK GP- 8016 • Colours: Grey and White DATE 02 Jan 03 Jan 04 Jan 05 Jan 06 Jan Total

OUTPUT 170 520 340 370 330 1730

DEEFCTIVE PIECES 18 36 25 28 24 131

ALTERATION% 10.58 6.92 7.35 7.56 7.27 7.94

Average production= 346 pieces. Average no. of defective pieces= 26 pieces Average re-work rate= 7.94 % The bar chart shown below depicts the output per day and the defective pieces for the first week. 600 520 500 400

370

340

330 OUTPUT

300

DEEFCTIVE PIECES 200

170

100 18

36

25

28

24

03/Jan

04/Jan

05/Jan

06/Jan

0 02/Jan

Second Week • Product Description : Jacket • Brand : Calvin Klein • Buyer: PVH Corp • Style No: CK GP-8016 • Colours: Grey and White

Arvind Goodhill Suit Manufacturing

Graduation Project

DATE 08 Jan 09 Jan 10 Jan 11 Jan 12 Jan 13 Jan Total

OUTPUT 370 260 210 710 428 210 2188

DEEFCTIVE PIECES 30 22 18 42 27 15 154

2018

ALTERATION% 8.10 8.46 8.57 5.91 6.30 7.14 7.41

Average production= 365 pieces. Average no. of defective pieces= 26 pieces Average re-work rate= 7.41 % The bar chart shown below depicts the output per day and the defective pieces for the first week. 800

710 700 600 500

400

428 370

OUTPUT DEEFCTIVE PIECES

260

300

210

210

200 100

30

22

18

42

27

15

0

08/Jan 09/Jan 10/Jan 11/Jan 12/Jan 13/Jan

Third Week• Product Description : Jacket (Sports) • Brand : Calvin Klein • Buyer: PVH Corp • Style No: CK GP-3001 • Colours: Grey and White DATE 16 Jan 17 Jan 18 Jan 19 Jan 20 Jan Total

OUTPUT 240 200 0 80 180 700

DEEFCTIVE PIECES 21 12 0 2 6 41

ALTERATION% 8.75 6 0 2.5 3.33 5.15

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Average production= 140 pieces. Average no. of defective pieces= 8.2 pieces Average re-work rate= 5.15 % The bar chart shown below depicts the output per day and the defective pieces for the first week. 300 250

240 200

200

180 OUTPUT

150

DEEFCTIVE PIECES 100 50

80

21

12

0 0

2

6

0 16/Jan

17/Jan

18/Jan

19/Jan

20/Jan

Fourth Week• Product Description : Jacket (Sports) • Brand : Calvin Klein • Buyer: PVH Corp • Style No: CK GP-3001 • Colours: Grey and White DATE 22 Jan 23 Jan 24 Jan 29 Jan 30 Jan 31 Jan Total

OUTPUT 240 275 330 191 230 200 1466

DEEFCTIVE PIECES 23 23 23 19 15 20 123

ALTERATION% 9.58 8.36 6.96 9.94 6.52 10 8.56

Average production= 244 pieces. Average no. of defective pieces= 21 pieces Average re-work rate= 8.56 %

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

The bar chart shown below depicts the output per day and the defective pieces for the fourth week 330

350 300 250

275 240

230

200

191

200

OUTPUT

150

DEEFCTIVE PIECES

100 50

23

23

23

19

15

20

0

Problems faced due to the existing inspection:    

There was high rate of alteration. The type and cause of defects could not be identified correctly. Time taken for a style to move out of the line increased. Before traffic light system was implemented there was a high percentage of alteration.

IMPLEMENTATION PHASE This is the most difficult phase in any projects because all the ideas do not follow the theory strictly and hence multiple problems were faced while implementing Traffic Light system. Some of the problems are discussed below:  Negligence of the workers and adapting to the new process took time. Explanations were not easily understood and there was a lot of confusion amongst them.  The existing inspection system had to be transformed and individual demonstration had to be provided to the quality checkers which resulted in errors in the first week.  The marking system was quite laborious and hectic. Hence, we tried to maintain a new format for keeping a record of the regular data in bulk.  Inspecting all the critical operations individually every hour was a tough task.  A two weeks trial implementation was also done to find whether it’s effective or not.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Analysis during trial/training period First WeekDATE 19 Feb 20 Feb 21 Feb 22 Feb 23 Feb 24 Feb Total

OUTPUT 80 90 130 190 270 280 1040

DEEFCTIVE PIECES 6 13 16 15 9 6 65

ALTERATION% 7.5 14.44 12.03 7.89 3.33 2.14 7.88

DEEFCTIVE PIECES 21 17 17 16 20 18 109

ALTERATION% 10 5.86 5.28 5.92 5.55 5.27 6.31

Average Production- 174 Alteration Rate-7.88% Second WeekDATE 26 Feb 27 Feb 28 Feb 1 Mar 2 Mar 3 Mar Total

OUTPUT 210 290 322 270 360 341 1793

Average Production-299 Alteration Rate-6.31% Problems:  Due to the implementation of a new inspection system workers felt confused.  Adjustment to the new process took time. This period is referred to as the “learning” period.  However, the alteration rate was reduced by 0.26% despite the confusion.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Post-Implementation Stage Style- Blackberrys Crew-4 Colour-Peach, Mint, Sky Blue, Beige First WeekDATE 05 Mar 06 Mar 07 Mar 08 Mar 09 Mar 10 Mar Total

OUTPUT 290 460 431 0 40 40 1261

DEEFCTIVE PIECES 16 13 16 0 2 9 56

ALTERATION% 5.51 2.82 3.71 0 5 22.5 7.91

DEEFCTIVE PIECES 8 5 16 19 9 57

ALTERATION% 2.96 6.25 6.4 7.6 2.72 5.18

DEEFCTIVE PIECES 12 11 16 13 11 9 72

ALTERATION% 4.13 4.21 6.4 2.82 3.37 2.43 3.89

Average Production-252 Alteration Rate-7.91%

Second WeekDATE 12 Mar 13 Mar 14 Mar 15 Mar 17 Mar Total

OUTPUT 270 80 250 250 330 1180

Average Production- 236 Alteration Rate- 5.18% Third WeekDATE 20 Mar 21 Mar 22 Mar 23 Mar 24 Mar Total

OUTPUT 19 Mar 290 261 250 460 250 326 1837

Average Production-306 Alteration Rate-3.89% Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Fourth WeekDATE 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Mar Total

OUTPUT 280 322 360 350 431 460 2203

DEEFCTIVE PIECES 15 15 20 17 13 13 93

ALTERATION% 5.35 4.65 5.5 4.85 3.71 2.82 4.48

Average Production-367 Alteration Rate-4.48% Graphical Representation-

Average Production Post Implementation 400

367

350 300

306 252

250

236

200 150 100 50 0 1st

2nd

3rd

4th

Average Alteration Post Implementation 9.00% 8.00% 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% 0.00%

7.91%

5.18% 3.89%

1st

2nd

3rd

4.48%

4th

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Average Alteration per Week Preimplementation vs Postimplementation 7.27%

8.00%

5.36%

6.00% 4.00% 2.00% 0.00% Pre Implementation

Post Implementation

ResultsPre-implementation: Average production per week= 1521 pieces Average defects per week= 113 Alteration rate= 7.27% Post-implementation Average production per week= 1621 pieces Average defects per week= 69 Alteration rate= 5.36% This analysis clearly show that,  Implementation of traffic light system for four weeks has resulted in a sharp decrease of 1.91% in terms of alteration rate.  A total time of 219 minutes has been saved on an average due to reduced re-work. Stage

Alter%

Pre7.27 Implementation Post5.36 Implementation 

Alter Pieces 113

Time/alter (min) 3.3

No. of Alter/piece 1.5

Total Time (min) 560

69

3.3

1.5

341

At least 3% of the altered garments are saved from rejections after the implementation of Traffic Light system showing the improvement in quality.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Stage

Alter%

Alter Pieces

Rejection %

PreImplementation PostImplementation

7.27

113

10

Rejected Pieces 11

5.65

69

10

7

Cost Benefit Analysis Worker’s Wage Rate- Rs12,000/month No of Working Days in a Month- 26 days No of Working Hours/day- 8 hrs Average per Minute Rate= 12000/(26x8x60) = (12000/12480) = Rs 0.96 Cost of Poor QualityDoing Rework/Alteration in 1 = Making 3 new garments in same time Normal SAM of a Jacket=75.46min Operational Cost/Jacket=75.46 x 0.96 =Rs72.44 Alteration SAM of a Jacket=226.38min Operational Cost/Jacket=226.38 x 0.96 =Rs 217.32 Indirect Cost (Overhead Cost)- Overtime Over Alteration Add Maintenance Overhead Cost (O/H) = 20% of Labour Cost =20% of Rs 217.32 =Rs 43.46 Total Operational Cost of Jacket (Alteration) =217.32+43.46 =Rs 260.78

Pre-implementation: Alteration cost= Average alteration per week x Average alteration cost = 113 x 260.78 = ₹ 29468.14

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Post- implementation: Alteration benefit:

1st Week 2nd Week 3rd Week 4th Week

No of Alter Pieces/ week 56 57 72 93

Total Alteration Cost

Alteration Benefit

14603.68 14864.46 18776.16 24252.54 Total Profit

14864.46 14603.68 10691.98 5215.6 45375.72

Rise in Production

Operational Cost per Piece 72.44 72.44 72.44 72.44 Total Benefit

Production Benefit

Production Benefit:

1st Week 2nd Week 3rd Week 4th Week

-22 -38 32 93

-1593.68 -2752.72 2311.04 6736.92 4701.56

Cost of Employing in-line Q.C. = Rs 10,000 Gross profit= (Alteration benefit + Production benefit) – Cost of Employing Q.C. Gross profit= Rs 40077.28 per month i.e. Rs.1541.43 per day. In other words, the post implementation production is 6481 pieces. So, Rs. 6.18 was saved while manufacturing each garment.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Results & DiscussionAfter all the analysis, certain results were obtained. From all the above given tables, these figures are clear in the report:  Implementation of traffic light system for four weeks has resulted in an increase in the production capacity by 6%. An average of 100 garments has been manufactured more post-implementation each week.  A sharp decrease of 1.91% has also been noticed in terms of alteration rate.  A total time of 219 minutes has been saved on an average due to reduced re-work.  At least 2% of the altered garments are saved from rejections after the implementation of Traffic Light system showing the improvement in quality.  78.97% of the defects are caused in 7.11% areas. So by concentrating on these areas, most of the defects can be reduced.  DHU was reduced to 5.3% from 6.88% in a span of 4 weeks.  A monthly amount of rupees 40077.28 was saved on alteration. Limitations:  Not easy for workers to understand.  Requires a systematic and regular attention for updating the format.  May create extra pressure on operators which could create adverse impact in the long run.  Improper operator training caused rework.  Certain critical operations were more prone to defects than others. Further Scope:  The formats can be put on each and every operation for complete minimization of alteration.  There can be a separate format for line supervisor as well; so that they also become careful about the defected operations.  P.A.S (Point Allocation System) could be introduced to keep the workers motivated.  The maintenance department should be improved so as to improve the machine conditions.  Decrease in level of rejections and reworks and reduced fabric usage.  Increased productivity and reduction in lead time and cost reduction and increased profit.  Decrease in re-work ultimate lead to profit of industry. Profits can be utilized in bringing more advanced machines which can lead to minimum re-work.

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

References M. Islam, A. Khan, & M. Khan. (2013). Minimization of reworks in quality and productivity improvement in the apparel industry. International Journal of Engineering and Applied Sciences, 1, 4th ser. Retrieved February, 2016 from http://eaas-journal.org/survey/userfiles/files/Minimization of Reworks in the apparel industry.pdf. 

‘An application of Pareto analysis and cause effect diagram for minimization of defects’ IJMER, Vol. 3, by Tanvir Ahmed, Raj Narayan Acharjee, Noman Sikdar.



Prasanta Sarkar, “Garment Manufacturing, processes, practices and technology” Online Clothing Study,Gurgaon,India, Published July 2015.



Minimization of Defects in Garment during Stitching-Ms.N.S. Patil, Mr.S.S.Rajkumar, Ms.P.W.Chandurkar, Mr.P.P.Kolte- International Journal on Textile Engineering and Processes Vol. 3, Issue 1 January 2017

Annexure First off Format-Finishing

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

First off Format-Sewing

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

First off Format-Cutting

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

RAM Report-

Shipment Tracking Sheet-

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Quality Performance Tracking Board-

Key Performance Indicator

Arvind Goodhill Suit Manufacturing

Graduation Project

2018

Roaming Q.C. Format Jacket Page-1

Page -2

Arvind Goodhill Suit Manufacturing