Indian Textile and Clothing

Training module for the middle level managers based on the skill gap identification for Garment Industry in Tiruppur

By D.Malmarugan Associate Professor

SARDAR VALLABHBHAI PATEL

INTERNATIONAL SCHOOL OF TEXTILES & MANAGEMENT
Autonomous Institute, Ministry of Textiles, Govt. of India.

1483, Avanashi Road, Peelamedu,Coimbatore 641 004.Tamilnadu.

Training module for the middle level managers based on the skill gap identification for Garment Industry in Tiruppur

Executive Summary The apparel exporters have an ambitious target of USD15 billion in exports by year 2011-12, even though 2009-2010 exports at USD 10.64 billion were slightly down by 2.64% over the previous year. Each USD 1 billion in exports requires an input of 36 million man-hours of work and the attendant demand for raw materials, accessories and logistics creates vibrancy in the entire ecosystem. The value chain comprises of spinning, weaving, knitting and garmenting. Also, it uses different materials such as cotton, jute, and wool, silk, man-made and synthetic fibers. The government should implement the Indo-EU Free Trade Agreement (FTA) soon.FTA has the potential to boost India's textiles and clothing exports to the European Union by over $3 billion. It will also create an additional 2.5 million jobs in our economy. Currently, the apparel sector employs 6 million people directly and 3 million indirectly. And 50 per cent of the work force is women. With right policies, this sector can absorb another 5 million workers directly within the next 3 years. The need of the hour is Skill development for employees in the Tiruppur Garment cluster, to be competitive in the Global level. A study to identify Skill gaps among middle level managers of organizations in The Tiruppur Garment cluster. Based on the findings training modules were developed for a period of Twenty days in functional areas like Production, Merchandising, Material Sourcing, Human resources and Finance and Costing.

Table of Contents Sl.No 1. 1. 1.1 Introduction 2. 1.2 Problems faced by the Indian Garment Industry 3. 1.3 Government Intervention and need for the study 4. 2.1 Production processes involved in Garmenting 5. 6. 7. 8. 9. 10. 11. 2.2. Skill requirements and skill gaps 2.2.1Nature of Skill Gap 2.2.2Labour Laws and Skill Gap 2.3 Existing Institutions 2.3.1Garments 2.3.2Measures to Improve the Institutions 2.4 Current Training/Education Infrastructure 12. 13. 14. 2.5 Emerging trends in skill requirements 2.5.1 Research & Development 2.5.2 Labour laws 2.5.3. Regions which will drive human resource requirements 16. 2.6 Projected Human Resource Requirements in the Textile & Clothing Sector 17. 2.6.1 Projected Size of the Textile and Clothing Industry 24 24 20 22 22 23 8 9 9 10 11 11 19 6 4 Topic Page Number 3 4

15.

18.

2.6.2 Projected human resource requirement 2.6.3 . Skill Pyramid for the T&C industry

24 25 27 28 28 28 30 30 72

19. 20. 21. 22. 23. 24. 25. 26.

3 Methodology
3.1 Instrument 3.2 Sample 3.3 Statistical tools of Analysis.

4. Analysis and Discussion 4.1 Descriptive Analysis
4.2 Inferential Analysis using Test of Significance.

27. 28. 29. 30.

5. Findings and Conclusion 5.1 Production Functional area. 5.2 Merchandising functional area 5.3 Material Sourcing Functional Area 5.4 Human resources Functional area 5.5 Finance Functional area 6. The Training Modules. 6.1 .Merchandising Functional area 6.2 Production Functional area 6.3 Material Sourcing Functional area

139 140 140 141

31. 32. 33. 34. 35. 36.

142 142 143 143 145 151

37. 38. 39.
‘

6.4 Human Resource Functional area. ANNEXURE: Questionnaire References

157 167 176

Chapter 1 Introduction 1.1 Introduction: Indian Textile and Clothing (T&C) industry is currently one of the largest and most important industries in the Indian economy in terms of output, foreign exchange earnings and employment. The industry contributes 4% to the country’s GDP and 14% to the country’s industrial production. The textiles industry accounts for around 14% of total exports from India. The apparel exporters have an ambitious target of USD15 billion in exports by year 201112, even though 2009-2010 exports at USD 10.64 billion were slightly down by 2.64% over the previous year. Each USD 1 billion in exports requires an input of 36 million man-hours of work and the attendant demand for raw materials, accessories and logistics creates vibrancy in the entire ecosystem. The value chain comprises of spinning, weaving, knitting and garmenting. Also, it uses different materials such as cotton, jute, and wool, silk, man-made and synthetic fibers. The clothing sector is the final stage of the textile value chain and the maximum value addition takes place at this stage. Apparel and clothing industry is fragmented and pre-dominantly in the small-scale sector excluding tailoring units, there are around 13,000 units of which 12,000 are SSI units. Most apparel manufacturers (80%) have small operations (with <20 sewing machines) while 99% of them are proprietorship/partnership concerns. The clothing industry is fragmented and pre-dominantly in the small-scale sector. The reason for this could be attributed to the SSI reservation policy which was in vogue till 2001 for woven apparels and up to March 2005 for knitwear. The quota policy which prevailed during the quota regime also did not encourage consolidation of the units. The apparel industry is concentrated primarily in 8 clusters, i.e., Tirupur, Ludhiana, Bangalore, National Capital Region or NCR

(Delhi/Noida/Gurgaon), Mumbai, Kolkata, Jaipur, and Indore. While Tirupur, Ludhiana and Kolkata are major centres for knitwear; Bangalore, NCR, Mumbai, Jaipur, and Indore are major centers for woven garments. 1.2 Problems faced by the Indian Garment Industry The unprecedented rise in price of raw materials (cotton & yarn) over the past few months and also general increase in all other costs due to hike in duty of petroleum products has made Indian garments uncompetitive in the world market. While our exports from India are falling, exports from low-cost countries such as Bangladesh, Vietnam and Cambodia continue to rise. The slowdown in the global economy has hit Indian garment exports. Exports to Europe which was facing a debt crisis have fallen. The US market is still fragile. 1.3 Government Intervention and need for the study What’s needed now is the government's support to compete with other countries. The government should support the sector in terms of higher duty draw back rates to offset cost disadvantages. The government should implement the Indo-EU Free Trade Agreement (FTA) soon.FTA has the potential to boost India's textiles and clothing exports to the European Union by over $3 billion. It will also create an additional 2.5 million jobs in our economy. Currently, the apparel sector employs 6 million people directly and 3 million indirectly. And 50 per cent of the work force is women. With right policies, this sector can absorb another 5 million workers directly within the next 3 years. The need of the hour is Skill development for employees in the Tiruppur Garment cluster, to be competitive in the Global level. For the above said reasons a study on skill gap identification and Training Requirements was necessary. Apex Cluster development Services , an

Organization involved in developmental activities in the Tiruppur Garment Cluster handed over the assignment of identifying the Skill gaps among supervisory level employees in the Tiruppur Garment cluster, to Sardar Vallabhbhai Patel International School of Textiles and Management.(An Autonomous institute under the ministry of Textiles, government of India).The following report is the outcome of the efforts taken in Data collection, analysis and Suggestions as Training modules in various functional areas for the Skill gaps in Tiruppur garment Cluster. The report is arranges as chapters in Literature Review, Methodology adopted, Data analysis, Findings and Training modules in various functional areas.

Chapter 2 Literature review 2.1 Production processes involved in Garmenting The various activities involved in garment manufacturing are . Cutting The fabric is cut as per the defined pattern for different parts of the garment. Markings are made on the spread fabric which is then cut/chopped in the cutting machine. Wastage reduction is a key consideration during this step. Stitching A number of stitch and seam types, and sewing machines are used for stitching the garment. Name of operations 1 Hem pocket 2 Crease pocket 3 Sew front placket 4 Folding right front edge 5 Sew pocket 6 Attach yoke to back 7 Join shoulder 8 Attach sleeve 9 Top stitch on sleeve 10 Side seam & in seam 11 Fuse collar &band interlining 12 Run stitch 13 Trim collar & band 14 Pressing 15 Top stitch & join

16 Trim upper collar 17 Top stitch collar band 18 Trim band & notch 19 Attach collar &label 20 Close collar 21 Hem cuff interlining 22 Run stitch cuff 23 Turn & press cuff 24 Top stitch cuff 25 Attach cuff 26 Close cuff 27 Bottom hem 28 Sew button hole 29 Sew button Source: ATDC Stitch classification is based on the structure of the stitch and method of interlacing. Machine in each class may have the capability of producing several different types of stitches depending on the machine structure and how it is set and threaded. A group of stitches with specific purpose is called seam, or in other words a line of stitches. Seams are categorized into 8 classes are designated according to the types and minimum number of components within the seam. Assembling Assembling will be required for a unit which has a line system of manufacturing where different components of the fabric are stitched separately and have to be assembled to make the complete garment. Various accessories like button are also added to the garment. Finishing

Finishing involves the following operations: Removal of excess thread, Washing Pressing/ Ironing and Folding.

2.2. Skill requirements and skill gaps In the age of cut throat competition among continuous upgradation of machinery is must to remain competitive in a sector like textiles and clothing, where export potentials are high. Along with modernization there occurs need for skilled workers who can run the machinery efficiently and understand the modern production processes. Thus skill requirement increases with the technological upgradation. In the Indian scenario for want of availability of skilled laborers in adequate quantity many firms in the industry are hesitant to expand their scale of operations or enter into high end segments with cutting edge technology. Low level of skills of the workers has a bearing on income of both workers as well as the firm. This works like a vicious circle. Low skilled employees in an organization means an organization with low productivity, and low quality and low value of output. It results in low competitiveness in the market leading to low returns for the firm. Such situation not only leads to low investment in HR and technology (obstruction in expansion and/or up-gradation of the existing system), but also results in low wages and low morale of employees. Lack of investment in HR and technology again means low skills/knowledge, which completes one side of the loop of low-skill poverty vicious circle. Lack of investment in HR and technology also results in creation of no or few additional jobs. It means supply and demand of labour gets imbalanced in favour of supply. Less demand and more supply puts pressure on wages. Eventually, organizations remain in the vicious circle of low productivity, low quality output and low value output .(Rehman and Ali, 2008)

2.2.1Nature of Skill Gap Skill gap can be defined as the gap between required level of knowledge and skill to do a particular activity and the existing level of knowledge and skill to accomplish the work. Alternatively, it can also be identified by the gap in the demand and supply of skilled workers at the existing wage rates in a unit. Skill gap may be at varying levels in different sort of activities in a textiles unit. Further, skill gap can be found at different hierarchical levels of an organization, e.g. at operative level, supervisory level, middle management level or senior management level. So remove the skill gap at various levels different strategies should be adopted. In some sort of activities, skill gap can be easily removed by a few days of training or on job training but in some other tasks a formal and intensive training is required. In addition, literate and educated workers are quicker to learn as compared to illiterate and uneducated workers. So the former are easier to train as compared to the latter. 2.2.2Labour Laws and Skill Gap Persistent skill gap in the textiles and clothing sector is very closely linked with the prevalent labor laws in the country. They can create a conductive environment for skill enhancement or they may hinder the growth of labour skills by hindering expansions during seasonal industries. It is therefore important that labour laws should be framed in such a manner that it should not hinder the growth and instead be used for the overall development of both workers and industry. 2.3 Existing Institutions Industrial Training Institutes (ITIs) established during the 1950s was the major effort on the part of Government to impart skills in various vocational trades to

meet the skilled manpower requirements of the various industries of the country. But they hardly provided core-competency training in textiles at operator level unlike other engineering disciplines. Vocational training for workers in the pre-or post-employment stages did not develop significantly in a structured and regular fashion. The Indian textiles workforce was generally developed within the industry where newly inducted unskilled workers acquired their skills from skilled colleagues already engaged in the industry, who passed on their expertise to such unskilled workers. As a result, they inherited the basic expertise along with any flaws and faulty skills. Some of the progressive composite mills did have special training programmes for unskilled, semiskilled and skilled workers apart from on job training (Ministry of Textiles, 2006). Currently, out of the total 4971 ITIs 1243 ITIs offer training in textiles with a yearly intake of 33372. They impart training in following trades-Bleaching, Dying; Block printing; Cutting and tailoring; Dress making; Embroidery; Hand weaving of niwar tape; Durries, Carpet, Knitting with hand operated machine; Weaving of silk and woollen fabrics, etc. 2.3.1Garments In the apparels segment most of the training imparted to workers is informal in nature. An unskilled worker first works as a helper in different activities of a garment making unit e.g. cutting, labeling, ironing, packaging, etc. Over a period of time he becomes a skilled worker. A few units recruit worker trained through ITI or other institutions. In Ludhiana knitting cluster, several apparel units recruit teen aged boys and provide them on the job training in stitching. It was found during the NCAER survey, 2008-09 that in certain clusters, a few skilled workers impart training in stitching to new labourers on payment during their leisure time at home. This is also an informal arrangement of training. In select clusters, Government established a few Apparel Training & Design

Centres (currently total thirteen in number) to train and upgrade the skills of workers in the garment sector. Recently, Infrastructure Leasing and Finance Services (IL&FS) has launched a project called Skills for Employment in Apparel Manufacturing (SEAM), a pilot effort to train and place rural belowpoverty-line youth in the apparel industry. But considering the massive skill gap in the sector, the efforts are little to have major impact. Generally, workers gain full expertise within 2-3 years. Scarcity of skilled workers is felt more during peak season.

2.3.2Measures to Improve the Institutions Currently, there is a massive gap between the availability of skilled manpower and the requirements of the industry, particularly in the weaving, dying, processing and garment segments. To bridge this gap requires massive expansion and modernization of training institutes/polytechnics across the country. They can be opened on a public -private partnership basis with maximum industry-institute interface. · The number of ITIs targeted specifically to the requirements of the textiles sector need to be increased significantly to meet the shortage of operatives. They may be persuaded to relate their courses and curriculum in textiles with the inputs from the textiles industry to make them more relevant to modern machineries and processes used in textiles industry. · Post graduate courses are required to develop a specialized skilled labour pool for the industry. These are to be offered as part of engineering degree programmes in various engineering colleges, IITs and NITs. · The Textile Research Associations (TRAs) may be strengthened with one time grant from the government to design and offer more short term structured training programmes.

· The existing network of Apparel Training and Design Centres (ATDCs) promoted by the Apparel Export Promotion Council may be expanded and strengthened to meet the needs of the rapidly growing RMG sector. · Knitting & knitwear service centers may be set up in the major knitting centers of Tiurupur, Ludhiana, Delhi and Kolkata to cater to the support service needs of the decentralized knitting and knitwear industry · Emphasis should be laid on not only educating and skilling the workers but also on a continuous process of skilling, re-skilling, multi-skillin g and skill modulation. · Capacities of powerloom service centres to conduct training programmes can be expanded. Simultaneously, new training centres may be established in smaller clusters where presently there are no training centres for skill development of workers. · The reorient and modernize of the industry may require major adjustments in human resource development policies so that skilled workers displaced during the adjustment process may be reabsorbed into productive employment. For this purpose, there is need to develop and install a meaningful mechanism that can utilize skilled weavers displaced from the hand-loom sector to productive employment in the power-loom and mill sectors. These skilled hand-loom weavers are major assets to the industry, but only if they can be utilized in the production of the sophisticated products that are in demand for domestic and export markets in hand looms or even in power looms and mills sector. · Need to reforms the rigid labour laws. · Industry associations like CITI (Confederation of Indian Textiles Industry) and other smaller associations should play a pivotal role in coordinating with training institutions and industry for the fulfilment of the training needs of various sectors of textiles industry and help in laying foundation for development of such institutes.

The following table contains the Functional area wise Skills Required and Skill gaps in various levels.
Function Level Skills Required Skill Gaps

Knowledge of various types of fabrics (type of material, count/picks, Dye requirements, etc). Knowledge of various types Purchase Manager of fabric defects such as breakage of threads, missing threads, stains, patches and shade variation, etc. Awareness of the latest price trends in the fabric market. Negotiation and communication skills for negotiating with the fabric manufacturers. In-depth knowledge of the various types of fabric and quality parameters. Negotiation and communication skills.

Ability to calculate the Procurement amount of requisite quality fabric required based on the order size and likely Purchase associate/ executive wastage. Knowledge of various types of fabric defects and other quality parameters. Liaison with the fabric manufacturers and fabric Insufficient knowledge of various types of fabric defects and other quality parameters.

Function

Level

Skills Required processors.

Skill Gaps

Understanding of various production activities as the merchandiser is interface between the buyer and the Senior Merchandiser company Soft skills like negotiation and communication skills. These skills assume more significance for export oriented units. Knowledge of foreign languages such as French Merchandising for better co-ordination with the buyer. Ability to handle multiple accounts/customers. Thorough understanding of costing. Understanding of buyer requirements of design and quality.

Lack of soft skills for interacting with buyers in the international market. Knowledge of foreign languages is limited to English – this might prove to be an issue with India becoming a sourcing hub for garments and knitwear Understanding of various factors affecting costing.

Junior Merchandiser/ Merchandising executive

Reviewing materials used for garment manufacturing Understanding of various production activities as the person is responsible for

Inadequate understanding of various production activities. The person employed picks up the requisite skills with

Function

Level

Skills Required execution of the order. Ability to work closely with other functions like design, production etc. Time management skills to handle multiple orders at the same time. Basic computer skills.

Skill Gaps experience. Inadequate understanding of quality requirements.

Design and develop garments according to buyer requirements. Ability to modify existing Design Designer designs to suit the current trends in the market. Keep abreast with the latest fashion trends in the key markets - the designer should be aware of the colours, contours which are in vogue. Knowledge of Styling, Elements of Design, Basics of Costing, Fabric Study, Pattern Making and Draping.

Inadequate understanding of buyer requirements which leads to number of iterations before the sample is accepted. Insufficient knowledge of latest fashion trends in the international markets – changes in design between ‘seasons’. It is required that the designer be able to forecast trends by being networked with foreign designers in major markets. The same is applicable to Indian markets as well.

Production Manager

Knowledge of pattern making Ability to undertake inspection, production planning and control

Inadequate knowledge of speciality fabrics Lack of adequate scientific knowledge of line balancing, work study, and

Function

Level

Skills Required Man-management skills.

Skill Gaps Quality Control (this is because a large number of managers have been elevated by experience rather than by formal

Production

training). Insufficient knowledge of various types of sewing machines (refer table listed earlier) – ability work in a cross-functional manner across sewing machines Inadequate soft skills to manage the shop floor personnel.

In-depth knowledge of production process and inspection methods Line Supervisor/ Floor supervisor Knowledge of different type of fabrics as well as understanding of stitching processes. Ability to guide the sewing machine operators. Man-management skills to manage the shop floor. The Supervisor should be able to motivate the workers in the challenging work atmosphere as the demand is seasonal and order driven.

Good machine control knowledge of threading of sewing machine, stitching on different shapes, seaming garment components together in various fabrics to Operator specified quality and quality

Lack of proper knowledge of sewing machine operations, and different types of seams and stitches Ability to work across different machines is missing

Function

Level

Skills Required standard Knowledge of machine maintenance procedures Knowledge of Pattern Making, Grading and Draping. Knowledge of CAD for Pattern Development Ability to sew complete garment. Quality requirements are all the more important for companies focussing on international markets. Even

Skill Gaps Ability to stitch the complete garment is missing ( In case of units which do not follow line system of production)

Knowledge of international quality standards is a significant gap.

Quality control Quality executive

small quality issues can lead to cancellation of order. Understanding of the customer requirements by interacting with the merchandiser. Knowledge of international standards is desirable. Knowledge of in line and final quality testing procedures - ability to understand and prevent defects like size variations, loose threads, stains etc.

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2.4 Current Training/Education Infrastructure Human Resource and Skill Requirements in the Textile Industry The current training infrastructure is inadequate on both number of people trained and also the quality of training being imparted. Also, very few of the training initiatives are targeted at the shop floor level. The newly inducted workers learn through informal training and learning from the experience of the existing work force. Training Infrastructure of Textile Sector Training Institute Number of centres/units Textiles Research Associations (TRAs) 8 Powerloom Service Centres (PSCs) 44 Indian Institutes of Handloom Technology (IIHT) 4 Weaver’s Service Centres (WSC) 24 Industrial Training Institutes (ITI) offering courses related to Textiles 1,243 Home Science Colleges offering Textiles & Clothing Courses 24 Apparel Training & Design Centres (ATDCs) 52 Institute of Apparel Management 1 National Institute of Fashion Technology 12* Sardar Vallabhbai Patel Institute of Textiles Management

Source: Report of the Committee to assess the requirement of human resource in the Textile sector, Ministry of Textiles, ATDC, NIFT *Does not include one international centre

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Training in these Industrial Training Institutes (ITIs) is mainly imparted in the following trades: (1) Bleaching (2) Dyeing (3) Block printing (4) Cutting and tailoring (5) Dress making (6) Embroidery (7) Hand weaving of niwar tape (8) Durries (9) Carpet (10) Knitting with hand operated machine (11) Weaving of silk and woollen fabrics, etc. The availability of trained manpower is a key issue for the garmenting sector. The ATDC, ITIs and NIFT annually train up to 50,000 workers. A few private sector players also provide training specific to the garmenting sector. A large portion of the requirement of human resource at the operator level is met by on the job training. Hence training at the operator level is a key gap. Acute shortage of skilled man power leads to poaching and acts as a detriment to spending on in house training initiatives. 2.5 Emerging trends in skill requirements Emerging trends in human resource requirements Technology
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The changes in technology would significantly affect the profile of people involved. As mentioned earlier, the share of shuttle-less looms in the Indian textiles industry is only 2-3% as against a world average of 16.9%, thereby indicating a low degree of modernization in the Indian weaving industry. Although the Indian spinning sector is relatively more modernised, around60% of installed spindles are more than 10 years old and open-end (OE) rotors account for only 1% of total installed spindles. In the apparel sector, India has much lower investment in special purpose machines, which perform specific functions and add value to the product. Very few export establishments have invested in cutting machines or finishing machines. The low level of technology and government incentives like TUFS would drive modernization in the industry where as the high power costs would be a detriment. The technological upgradation would necessitate the human resource to be trained in modern machinery and also greater in house spending on training. The shortage of labour and increasing wage rate would further induce greater automation which will lead to higher productivity. For instance, the operating hours per quintal of yarn have decreased from 77 to 25 on account of modernization and would continue to fall. Also, the numbers of people involved in post spinning operations have come down on account of automatic cone winding machines. The modern machinery would require skilled maintenance people who have the requisite knowledge of the same. Proper maintenance would be crucial as machine down time and costly spare parts would significantly affect the performance of the industry. Quality Processes There would be increasing focus and adoption of quality and environment related processes, such as: ISO 9001:2008
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ISO 14001. 2.5.1 Research & Development The textile industry does not have R&D as a focus area. The industry would have to invest more in both process and product R&D to maintain product and cost competitiveness. This requires industry-academia collaborations as well as individual R&D efforts by the companies. 2.5.2 Labour laws More flexible labour regulations will positively affect the industry. Currently, T&C industry comes under the purview of Contract Labour Act, 1970 which prohibits contract labour for the work that is perennial in nature. The exporters find it difficult to manage the seasonal and order based volatility in demand on account of this. Change in the current regulations can lead to opening up of more employment opportunities. Also, the current regulations prohibit women from being employed in night shifts. Relaxation of the same with adequate safeguards can lead to more participation of women and also help in addressing the skill shortage in the industry. Human resource related Modernisation of technology would necessitate more technical skills for operators in the production and maintenance functions across the value chain of the textile industry. The sector also needs multi-tasking/multi skilling at the operator level. The human resource at the higher levels as well as in other functions like procurement would need to possess the knowledge of various types of machines and also keep abreast with the changes in technology. The garmenting sector would be the key driver of the employment in the textile sector. Majority large portion of the human resource requirement will be for operators who have the adequate knowledge of sewing machine operations
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and different types of seams and stitches. Although, the industry will continue to have predominantly line system of operations, designer and high end fashion exports would necessitate “make through” system of operations which would require the operators to have the ability to stitch the complete garment. The availability of merchandising and designing skills would be crucial for increasing share in export markets and tapping the potential in new markets.

2.5.3. Regions which will drive human resource requirements The major centres in India where this employment generation would take place are Tamil Nadu, West Bengal, Karnataka, Maharashtra, and Gujarat. The state of Tamil Nadu will account for around 30% of the employment in the textile sector. The poor performance of the industry in the recent past has resulted in the sector not attracting new investments. The cluster development activities of various organisations have not found takers and hence new clusters do not appear likely at this point of time. However, Andhra Pradesh is a likely future destination for new investments, especially in the garmenting sector with the establishment of Apparel Parks. The government initiatives of providing power at a cost of 2 Rs per unit will be a key factor in attracting investments in spinning sector. Also, the state has surplus cotton and would result in lower logistics cost. Availability of raw materials and low power costs will also attract investments in the downstream activities like fabric manufacturing, processing and garmenting. The scheme of integrated textile parks and various SEZs would also affect the regions availability of labour. States like Uttranchal necessitate that most of the labour force in the units operating in SEZ should be local.

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The states of UP, Bihar and Orissa etc would be key catchment areas to meet the labour requirements. Already the spinning sector in Tamil Nadu is seeing more and more influx of labour from these states as the current wage rates in the states are very high. Environmental concerns would affect the processing sector. The effluent treatment requirements might see units shifting to coastal areas as marine discharge requirements are less stringent. 2.6 Projected Human Resource Requirements in the Textile & Clothing Sector In this section, we shall review the projected human resource requirement in the Textile and Clothing sector based on the projection of industry size.

2.6.1 Projected Size of the Textile and Clothing Industry It is estimated that the PFCE on clothing will grow at a CAGR of 7.5% between 2008 and 2024. Based on projected growth of GDP and exports, we expect that the exports of textiles will grow at a rate of 11% to 11.5%. Thus, the overall T&C sector will grow at a CAGR of 9.5% to a size of Rs. 6,730 billion. Out of this, the share of exports is expected to increase from just under 50% currently to about 60% in 2022. 4 Our overall approach to macro-economic modeling and forecasting is explained in a separate annexure 2.6.2 Projected human resource requirement While analysing the human resource requirement, we have categorised the overall T&C sector as follows: 1. The Mainstream T&C sector – comprising of Spinning, Fabric Manufacturing, Fabric Processing, and Garmenting.
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2. Other related industries such as: a. Handloom b. Woolen c. Sericulture d. Handicrafts e. Jute. While we expect the human resource requirement in the Mainstream T&C sector to be closely related to market driven T&C industry growth, the human resource requirement in areas such as handloom and handicrafts would have to be supplemented by initiatives from the Government and Industry. The addition of human resource into these other sectors would be at a much lower rate as compared to the Mainstream sectors due to need for significant support for earnings, scope for enhanced technology intervention and automation as compared to current levels, the need to add value, and attractiveness of the sector among the human resource supply. Keeping in mind the above factors and the growth of the industry, we have projected the human resource requirement for the T&C sector. It is expected that the overall employment in the sector would increase from about 33 to 35 million currently to about 60 to 62 million by 2022. This would translate to an incremental human resource requirement of about 25 million persons. Of this the Mainstream T&C sector has the potential to employ about 17 million persons incrementally till 2022. 2.6.3 . Skill Pyramid for the T&C industry Given that the industry would required a varied profile of skill sets, the following figure presents an overview of the profile of skill requirements as

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derived from human resource requirements across different sectors of the T&C industry. The skill pyramid, in summary, captures where the T&C industry stands relatively in terms of skills (a function of activity, educational requirements, and amount of ‘preparatory’ time required to inculcate aspecific skill) as compared to all other industries. As can be observed, the lower portion of the pyramid, ‘Skill Level 1’, has the highest incremental requirement of human resources. It requires persons who are minimally educated, yet can handle simple and/or repetitive tasks (persons employed in activities such as basic machine operations, knitting, cutting, and stitching/sewing, etc.). Such skills can also be obtained in lesser time duration as compared to engineering or ITI courses. As many as over 15 million persons are required across skill levels 1 and 2 outlined above.

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Chapter 3

Methodology
The methodology to be adopted is as provided by the funding agency Apex Cluster Development Services Pvt. Ltd and fine tuned by frequent interactions with the team at Tiruppur led by the Cluster Development Manager

Sl. No.

TITLE OF SERVICES

DESCRIPTION

No. of Man-Days

1.

Preparatory Study

For undertaking study in the cluster about its functions and to understand the skill requirements and to identify 6 the existing gap in the Middle Management Level.

2. 3.

4.

Drafting of To be designed for interviewing 200 2 Questionnaire Middle Level Managers working in the cluster for Understanding the Sample gap in the knowledge level of 20 Survey Middle Managers in their relevant Revising and functional areas. finalizing the Also this survey to be used to Questionnaire understand the most convenient 2 based on time, etc., so as to make the program Sample more participative. Survey Survey At-least 200 Middle Level Managers have to be covered for making the 200 study through MSMEs in the Cluster. The whole process is to be closely monitored and documented by right resource persons so as to attain the desired result in developing the 200

5.

6.

Monitoring

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cluster and addressing the gap.

7.

Compilation and analysis Preparation of training modules and course materials. Coordinating activities

The data acquired to be compiled properly and analysed to identify the 10 skill gaps in the cluster. Developing Training modules in relevant functional areas. All the administration and coordination of survey to be covered under this.

8.

20

9.

30

3.1 Instrument Questionnaire was prepared based on Literature review and discussion with experts in this field and was finalized by the taem at Apex Cluster Tiruppur office.The questions are relevant and important to measure the skill gaps in the Tiruppur Garment Clsuter 3.2 Sample Sample of 200 middle level Managers working in the cluster in various functional areas for Understanding the gap in the knowledge level of Middle Managers in their relevant functional areas was chosen. The sample is a large sample so generalization of findings is possible. 3.3 Statistical tools of Analysis. Data was analyzed using statistical techniques like percentage and Chi-square Analysis

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Chapter 4 Analysis and Discussion The data was analyzed using Percentage analysis and chi-square analysis. 4.1 Descriptive Analysis: Percentage Analysis was used for Descriptive analysis

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Table: 1 Departments of the respondents
Department

Frequency 1.Merchandising 2.Production 3. Human Resources 4.Finance &costing 5. Fabric sourcing Total 48 44 36 33 39 200

Percent Valid Percent 24.0 22.0 18.0 16.5 19.5 100.0 24.0 22.0 18.0 16.5 19.5 100.0

Cumulative Percent 24.0 46.0 64.0 80.5 100.0

The above table provides the department wise breakup of the respondents.24% of the respondents belong to Merchandising Department. Production personnel were 22% while Human resource executives made up 18% of the respondents. About a fifth were from Fabric sourcing and 16.5 % belong to finance and Costing.

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Table: 2 Qualification of Respondents. Qualification Frequency
Valid

Percent Valid Percent 1.5 9.5 4.0 10.5 10.5 1.5 59.5 3.0 100.0 1.5 9.5 4.0 10.5 10.5 1.5 59.5 3.0 100.0

Cumulative Percent 1.5 11.0 15.0 25.5 36.0 37.5 97.0 100.0

1.Matriculation (sslc) 2.higher secondary(plus two) 3.Diploma in textiles tech 4.other diploma 5.Graduate in fashion design 6.P.G. in textile 7.other graduates 8.Textile engg. graduate Total

3 19 8 21 21 3 119 6 200

Graduate degree holders from streams other than Textiles make up about 60% of the respondents. A tenth are Higher secondary passed and Diploma in streams other than Textile are another one tenth and Graduates degree holders in Fashion Design make up one tenth of the respondents. Engineers in Textiles are just 3%while Postgraduates in Textiles are a mere one and a half percent. Diploma holders in Textiles are 4% and Matriculation passed are just 1.5%.

34

Table:3 Experience of respondents. experience Frequency Valid 1. less than 5 years 2. 5-10 years 3. 10-15 years 4. 15-20 years 5. >20 years Total 87 81 22 9 1 200 Percent Valid Percent 43.5 40.5 11.0 4.5 .5 100.0 43.5 40.5 11.0 4.5 .5 100.0 Cumulative Percent 43.5 84.0 95.0 99.5 100.0

35

A vast Majority of the respondents are having Experience of less than 10 years and half of them are having experience less than 5 years. A tenth are having experience between 10-15 years and only about 5% are having experience between 15-20 years.

Table 4: Human Relation skills hrskills Frequency Valid 1.yes 2. no Total 186 14 200 Percent Valid Percent 93.0 7.0 100.0 93.0 7.0 100.0 Cumulative Percent 93.0 100.0

A vast Majority are confident of possessing Human relationship skills.

36

Table 5 Sufficient knowledge to perform Tasks sufficientknowledge Frequency Valid 1. yes 2. no Total 195 5 200 Percent Valid Percent 97.5 2.5 100.0 97.5 2.5 100.0 Cumulative Percent 97.5 100.0

Almost everybody are confident of possessing sufficient Knowledge to perform Their Tasks

37

Table 6: Updated Technical Knowledge uptodatetechknowledge Frequency Valid 1. yes 2. no Total 177 23 200 Percent Valid Percent 88.5 11.5 100.0 88.5 11.5 100.0 Cumulative Percent 88.5 100.0

Except for a tenth of the respondents , others are confident of having Updated Technical Knowledge in their respective Domains.

38

Table:7 Convenient Timings for Training Convenient timings Frequency Valid 1. Sunday 2. Saturday &Sunday 3. weekdays 4. no time Total 161 6 7 26 200 Percent Valid Percent 80.5 3.0 3.5 13.0 100.0 80.5 3.0 3.5 13.0 100.0 Cumulative Percent 80.5 83.5 87.0 100.0

A vast Majority prefer the weekends especially Sundays for the Training Programs, as they are occupied with their work on weekdays. A bit more than a tenth are unable to find time for Training.

39

Table 8Production: Production Planning Production planning Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2. no
Total Missing Total System

52 12
64 136 200

26.0 6.0
32.0 68.0 100.0

81.2 18.8
100.0

81.2 100.0

About a fifth of the respondents are not conversant with Production Planning techniques.

40

Ta ble 9: Production: Budgeting and costing Budgeting and costing Cumulative Frequency Percent Valid Percent Percent Valid 1.yes 2. no Total Missing System Total 23 41 64 136 200 11.5 20.5 32.0 68.0 100.0 35.9 64.1 100.0 35.9 100.0

Two thirds are not conversant with Budgetting and Costing methods in Production

41

Table 10 :Production:Machinery Planning mcplanning Cumulative Frequency Percent Valid Percent Percent Valid 1.yes 2.no Total Missing System Total 19 45 64 136 200 9.5 22.5 32.0 68.0 100.0 29.7 70.3 100.0 29.7 100.0

Two thirds are not conversant with Machinery Planning methods in Production

42

Table 11: Production: Layout playout Frequency Valid 1.yes 2.no Total Missing System Total 18 46 64 136 200 Percent Valid Percent 9.0 23.0 32.0 68.0 100.0 28.1 71.9 100.0 Cumulative Percent 28.1 100.0

Two thirds are not conversant with Prodcution Layout Planning methods .

43

Table 12 Production: Standard Alerted minute psam Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2. no Total Missing System Total 42 22 64 136 200 21.0 11.0 32.0 68.0 100.0 65.6 34.4 100.0 65.6 100.0

One third of the respondents are not conversant with Stadard Alert minute.

44

Table 13: Production:Quality control and newly developed Fabrics. pqcnewdevfabrics Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 45 19 64 136 200 22.5 9.5 32.0 68.0 100.0 70.3 29.7 100.0 70.3 100.0

About 30% are not aware of Quality control techniques and Newly Developed Fabrics.

45

Table 14: Production: Statistical Quality control and Operations Research psqcandor Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 39 25 64 136 200 19.5 12.5 32.0 68.0 100.0 60.9 39.1 100.0 60.9 100.0

About 40% of the respondents are not familiar with Statistical Quality control and Operations Research techniques.

46

Table 15:Production:Lighting, ergonoimics and Industrial engineering plightingergoie Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 38 26 64 136 200 19.0 13.0 32.0 68.0 100.0 59.4 40.6 100.0 59.4 100.0

Four tenths of the respondents are not familiar in Lighting impact, ergonomics and other industrial engineering aspects.

47

Table 16:Production:Lean Maufacturing pleanmfrg Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 26 38 64 136 200 13.0 19.0 32.0 68.0 100.0 40.6 59.4 100.0 40.6 100.0

Sixtenths of the respondents are not familiar with Lean Manufacturing techniques.

48

Table 17: Merchandising: Prospecting and Vendor evaluation mvendor Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 32 15 47 153 200 16.0 7.5 23.5 76.5 100.0 68.1 31.9 100.0 68.1 100.0

49

About 32% of the respondents are not familiar with Prospecting and Vendor Evaluation

Table 18: Merchandising: Sample Development msampledev Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 45 2 47 153 200 22.5 1.0 23.5 76.5 100.0 95.7 4.3 100.0 95.7 100.0

Most of the respondents are aware of Sample and Product Development techniques.

50

Table 19:Merchandising:Printing , Dyeing and Washing mprintdyewash Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 42 5 47 153 200 21.0 2.5 23.5 76.5 100.0 89.4 10.6 100.0 89.4 100.0

51

Printing Dyeing and Washing methods are known to nine tenths of the respondents.

Table20 : Merchandising: Sketch studying and Garment Construction methods msketchgarmentconst Cumulative Frequency Percent Valid Percent Percent Valid 1.yes 2.no Total Missing System Total 40 7 47 153 200 20.0 3.5 23.5 76.5 100.0 85.1 14.9 100.0 85.1 100.0

52

Sketch studying and Garment Construction methods are known to 85% of the respondents.

Table 21: Merchandising: Department wise costing details mdeptcosttech Frequency Valid 1.yes 2.no Total Missing System Total 37 10 47 153 200 Percent Valid Percent 18.5 5.0 23.5 76.5 100.0 78.7 21.3 100.0 Cumulative Percent 78.7 100.0

53

Department wise costing details are known to about eight tenths of the respondents.

54

Table 22: Merchandising: Communication, Interpersonal skills mcommun Frequency Valid 1.yes 2.no Total Missing System Total 43 4 47 153 200 Percent Valid Percent 21.5 2.0 23.5 76.5 100.0 91.5 8.5 100.0 Cumulative Percent 91.5 100.0

Nine tenths of the respondents are familiar with Communication, Interpersonal skills

55

Table 23:Merchandisng: Fabric consumption details mfabricconsump Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2. no
Total Missing Total System

43 4
47 153 200

21.5 2.0
23.5 76.5 100.0

91.5 8.5
100.0

91.5 100.0

Fabric consumption details are known to nineteenths of the respondents.

56

Table 24: Material Sourcing: Fabrics, Geographical availability and Price msfabavasilprice Frequency Valid 1.yes 2.no Total Missing System Total 38 3 41 159 200 Percent Valid Percent 19.0 1.5 20.5 79.5 100.0 92.7 7.3 100.0 Cumulative Percent 92.7 100.0

Most of the respondents know specification of Fabrics, Geographical availability and Price

57

Table 25: Material Sourcing: Trims and Accessories-quality parameters mstrims Frequency Valid 1.yes 2.no Total Missing System Total 37 4 41 159 200 Percent Valid Percent 18.5 2.0 20.5 79.5 100.0 90.2 9.8 100.0 Cumulative Percent 90.2 100.0

Nine tenths of the respondents are aware of Trims and Accessories-quality parameters

58

Table 26: Material Sourcing: Interacting with merchandiser for requisition msinteractionmerch Frequency Valid 1.yes 2.no Total Missing System Total 35 6 41 159 200 Percent Valid Percent 17.5 3.0 20.5 79.5 100.0 85.4 14.6 100.0 Cumulative Percent 85.4 100.0

85 % of the respondents are good in interacting with merchandiser for requisition.

59

Table 27: Materials Sourcing: Negotiating and communication skills msnegotiatecomm Frequency Valid 1.yes 2.no Total Missing System Total 38 3 41 159 200 Percent Valid Percent 19.0 1.5 20.5 79.5 100.0 92.7 7.3 100.0 Cumulative Percent 92.7 100.0

60

Most of the respondents are familiar with Negotiating and communication skills.

Table 28: Materials Sourcing -Incoming quality inspection, Lot to lot variation of incoming materials msincomqc Cumulative Percent Frequency Percent Valid Percent Valid 1.yes Missing System Total 41 159 200 20.5 79.5 100.0 100.0 100.0

Everybody are familiar with incoming quality inspection, Lot to lot variation of incoming materials

61

Table 29: Human Resources: Prospecting and selecting employees hrprospectnselection Cumulative Percent Frequency Percent Valid Percent Valid 1.yes Missing System Total 41 159 200 20.5 79.5 100.0 100.0 100.0

All of the respondents were familiar with Prospecting and selecting employees for various positions

62

Table 30: Human Resources: Various Laws of Industrial Relations hrlawsnir Cumulative Frequency Percent Valid Percent Percent Valid 1.yes 2.no Total Missing System Total 40 1 41 159 200 20.0 .5 20.5 79.5 100.0 97.6 2.4 100.0 97.6 100.0

Almost everybody are aware of Various Laws of Industrial Relations.

63

Table31: Human Resources: various Welfare measures hrwelfare Cumulative Frequency Percent Valid Percent Percent Valid 1.yes 2.no Total Missing System Total 39 2 41 159 200 19.5 1.0 20.5 79.5 100.0 95.1 4.9 100.0 95.1 100.0

Most of the respondents are aware of the various welfare measure of employees.

‘
64

Table 32: procedures of Rewarding employees for Better performance hrrewardemp Cumulative Frequency Percent Valid Percent Percent Valid 1.yes Missing System Total 41 159 200 20.5 79.5 100.0 100.0 100.0

Everybody are aware of the procedures of Rewarding employees for Better performance

65

Table 33: Human Resources: measuring performance of Employees hrperfmeasure Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 40 1 41 159 200 20.0 .5 20.5 79.5 100.0 97.6 2.4 100.0 97.6 100.0

Almost ever respondent was conversant with methods of measuring performance of Employees

66

Table 34: Human Resources: Training and Development of Employees hrtraingndevp Cumulative Percent Frequency Percent Valid Percent Valid 1.yes 2.no Total Missing System Total 40 1 41 159 200 20.0 .5 20.5 79.5 100.0 97.6 2.4 100.0 97.6 100.0

Almost ever respondent was conversant with methods of measuring performance of Employees

67

Table 35: Finance: Book Keeping Practice finbookkeep Cumulative Frequency Percent Valid Percent Percent Valid yes no Total Missing System Total 34 4 38 162 200 17.0 2.0 19.0 81.0 100.0 89.5 10.5 100.0 89.5 100.0

Book Keeping Practice are known to nine tenths of the respondents.

68

Table 36: Finance: Computerized accounting method fincomputer Cumulative Frequency Percent Valid Percent Percent Valid yes no Total Missing System Total 36 2 38 162 200 18.0 1.0 19.0 81.0 100.0 94.7 5.3 100.0 94.7 100.0

Computerized accounting methods are familiar to almost every respondent.

69

Table 37: Finanace: Working capital Management Practices finwc Cumulative Frequency Percent Valid Percent Percent Valid yes no Total Missing System Total 34 4 38 162 200 17.0 2.0 19.0 81.0 100.0 89.5 10.5 100.0 89.5 100.0

Working capital Management Practices are known to nine tenths of respondents.

70

.

Table 38: cash Management fincashmanage Frequency Valid yes no Total Missing System Total 35 3 38 162 200 Percent Valid Percent 17.5 1.5 19.0 81.0 100.0 92.1 7.9 100.0 Cumulative Percent 92.1 100.0

Most of the respondents are aware of cash Management Practices

71

Table 39: Banking Procedures finbanking Frequency Valid yes no Total Missing System Total 32 6 38 162 200 Percent Valid Percent 16.0 3.0 19.0 81.0 100.0 84.2 15.8 100.0 Cumulative Percent 84.2 100.0

More than eight tenths of the respondents are aware of banking Procedures

72

Table 40: Taxation Procedures fintax Frequency Valid yes no Total Missing System Total 36 2 38 162 200 Percent Valid Percent 18.0 1.0 19.0 81.0 100.0 94.7 5.3 100.0 Cumulative Percent 94.7 100.0

Almost everybody are familiar with Taxation Procedures.

73

4.2 Inferential Analysis using Test of Significance. Chisquare analysis was adopted to test the significant relationship between Dependent variable and Independent variable such as qualification, experience etc. Table 41:Significance of relationship between sufficient knowledge and experience Crosstab Count experience less than 5 years 5-10 years sufficientknowle yes dge no Total 86 1 87 79 2 81 10-15 years 20 2 22 15-20 years 9 0 9 >20 years 1 0 1 Total 195 5 200

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.829a 4 .305 Likelihood Ratio 3.683 4 .451 Linear-by-Linear 1.247 1 .264 Association N of Valid Cases 200 a. 6 cells (60.0%) have expected count less than 5. The minimum expected count is .03. There is no significant relationship between the Experience and knowledge sufficient to discharge their responsibilities in the area of function and the Experience level of the respondents.
74

Table 42: Significance of relationship between sufficient knowledge and Qualification

Qualification higher dip oth gra oth secon tex er fashion pg in er textileengg sslc dary tech dip design textile gra graduate Total sufficientkno yes wledge no Total 2 1 3 17 2 19 8 20 0 1 20 1 21 3 0 11 9 6 0 6 195 5

8 21

0 11 3 9

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 21.091a 7 .004 Likelihood Ratio 14.076 7 .050 Linear-by-Linear 13.041 1 .000 Association N of Valid Cases 200 a. 10 cells (62.5%) have expected count less than 5. The minimum expected count is .08. There is significant relationship between the Experience and knowledge sufficient to discharge their responsibilities in the area of function and the Qualification level of the respondents

75

Table 43: Significance of relationship between up to date technical knowledge and Experience Crosstab Count experience less than 5 years 5-10 years uptodatetechknowle yes dge no Total 79 8 87 73 8 81 10-15 years 18 4 22 15-20 years 7 2 9 >20 years 0 1 1 Total 177 23 200

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 10.341a 4 .035 Likelihood Ratio 6.694 4 .153 Linear-by-Linear 4.401 1 .036 Association N of Valid Cases 200 4 cells (40.0%) have expected count less than 5. The minimum expected count is .12. There is significant relationship between the up to date technical knowledge and the Experience level of the respondents

76

Table 44: Significance of relationship between up to date technical knowledge and Qualification Crosstab Count Qualification high er seco dip ndar tex sslc y tech uptodatetechknowle ye dge s no Total 2 1 3 15 4 19 7 1 8 gra fashi textil on pg in eengg other desig textil other gradu Tota l dip n e gra ate 17 4 21 16 5 21 3 0 3 111 8 119 6 177 0 23 6 200

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 11.257a 7 .128 Likelihood Ratio 11.185 7 .131 Linear-by-Linear 7.912 1 .005 Association N of Valid Cases 200 a. 9 cells (56.3%) have expected count less than 5. The minimum expected count is .35. There is no significant relationship between the up to date technical knowledge and the Qualification level of the respondents

77

Table 45: Significance of relationship between Familiarity with Production Planning Techniques and Experience Crosstab Count experience less than 5 years 5-10 years pproductionplanni yes ng no Total 28 7 35 16 5 21 10-15 years 6 0 6 15-20 years 2 0 2 Total 52 12 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 2.235a 3 .525 Likelihood Ratio 3.689 3 .297 Linear-by-Linear .881 1 .348 Association N of Valid Cases 64 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .38. There is no Significant relationship between Familiarity with Production Planning Techniques and Experience of the respondent

78

Table 46: Significance of relationship between Familiarity with Production Planning Techniques and Qualification Crosstab Count Qualification higher dip gra ssl second tex othe fashion c ary tech r dip design pproductionplanni yes ng no Total 1 0 1 8 2 10 6 0 6 11 2 13 9 3 12 Textile engg other graduat gra e 15 4 19 2 1 3

Total 52 12 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 2.515a 6 .867 Likelihood Ratio 3.728 6 .713 Linear-by-Linear .731 1 .393 Association N of Valid Cases 64 a. 10 cells (71.4%) have expected count less than 5. The minimum expected count is .19. There is no Significant relationship between Familiarity with Production Planning Techniques and Experience of the respondent

79

Table 47: Significance of relationship between Familiarity with budgeting and costing Techniques and Experience Crosstab Count experience less than 5 years 5-10 years pbudgettingandcosti yes ng no Total 13 22 35 7 14 21 10-15 years 1 5 6 15-20 years 2 0 2 Total 23 41 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.617a 3 .202 Likelihood Ratio 5.271 3 .153 Linear-by-Linear .106 1 .745 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .72. There is no Significant relationship between Familiarity with budgeting and costing Techniques and Experience of the respondent

80

Table 48: Significance of relationship between Familiarity with budgeting and costing Techniques and Qualification Crosstab Count Qualification high er seco dip ssl ndar tex c y tech pbudgettingandcosti ye ng s no Total 0 1 1 3 7 10 1 5 6 gra fashi on

other dip 1 12 13

Textile engg desig other gradua Tot al n te gra 6 6 12 10 9 19 2 1 3 23 41 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 10.748a 6 .096 Likelihood Ratio 12.175 6 .058 Linear-by-Linear 5.882 1 .015 Association N of Valid Cases 64 a. 9 cells (64.3%) have expected count less than 5. The minimum expected count is .36. There is Significant relationship between Familiarity with budgeting and costing Techniques and Qualification of the respondent( at 0.1 significance level)

81

Table 49: Significance of relationship between Familiarity with machinery planning Techniques and experience

Crosstab Count experience less than 5 years 5-10 years pmcplannin yes g no Total 10 25 35 5 16 21 10-15 years 3 3 6 15-20 years 1 1 2 Total 19 45 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.950a 3 .583 Likelihood Ratio 1.827 3 .609 Linear-by-Linear .705 1 .401 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .59. There is no Significant relationship between Familiarity with machinery planning Techniques and experience of the respondent.

82

Table 50: Significance of relationship between Familiarity with machinery planning Techniques and Qualification

Crosstab Count Qualification high er seco ssl ndar other c y dip tex tech dip pmcplannin ye g s no Total 0 0 1 3 10 13 Asymp. Sig. (2-sided) Textile engg gra fashion othe graduat Tot al design r gra e 5 7 12 9 10 19 1 2 3 19 45 64

1 10 5 1 10 6 Chi-Square Tests Value
a

df

Pearson Chi-Square 9.093 6 .168 Likelihood Ratio 11.990 6 .062 Linear-by-Linear 7.404 1 .007 Association N of Valid Cases 64 a. 9 cells (64.3%) have expected count less than 5. The minimum expected count is .30. There is no Significant relationship between Familiarity with machinery planning Techniques and Qualification of the respondent

83

Table 51: Significance of relationship between Familiarity with Layout and Experience Crosstab Count experience less than 5 years 5-10 years playout yes no Total 8 27 35 7 14 21 10-15 years 2 4 6 15-20 years 1 1 2 Total 18 46 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.316a 3 .725 Likelihood Ratio 1.276 3 .735 Linear-by-Linear 1.140 1 .286 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .56. There is no Significant relationship between Familiarity with Layout and Experience of the respondents.

84

Table 52: Significance of relationship between Familiarity with Layout and Qualification

Crosstab Count Qualification highe r ssl secon dip tex c dary tech playout yes no Total 0 1 1 2 1 gra Textile fashi Engg on other desig graduat dip n e other gra 3 4 8 12 Asymp. Sig. (2-sided) 7 12 19 1 2 3

Total 18 46 64

8 5 10 10 6 13 Chi-Square Tests Value
a

df

Pearson Chi-Square 2.187 6 .902 Likelihood Ratio 2.485 6 .870 Linear-by-Linear 1.756 1 .185 Association N of Valid Cases 64 a. 9 cells (64.3%) have expected count less than 5. The minimum expected count is .28. There is no Significant relationship between Familiarity with Layout and Qualification of the respondent.

85

Table 53: Significance of relationship between Familiarity with SAM techniques and Experience

Crosstab Count experience less than 5 years 5-10 years psam yes no Total 24 11 35 13 8 21 10-15 years 4 2 6 15-20 years 1 1 2 Total 42 22 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .483a 3 .923 Likelihood Ratio .472 3 .925 Linear-by-Linear .283 1 .595 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .69. There is no Significant relationship between Familiarity with SAM techniques and Experience of the respondent

86

Table 54: Significance of relationship between Familiarity with SAM techniques and Experience

Qualification higher dip second tex sslc ary tech psam yes no Total 1 0 1 5 5 10 6 0 6 other dip 10 3 13 gra Textile fashion Engg design other gra graduate 9 3 12 10 9 19 1 2 3 Total 42 22 64

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 8.761 6 .187 Likelihood Ratio 10.857 6 .093 Linear-by-Linear 1.835 1 .176 Association N of Valid Cases 64 a. 9 cells (64.3%) have expected count less than 5. The minimum expected count is .34. There is no Significant relationship between Familiarity with SAM techniques and Qualification of the respondent.

87

Table 55: Significanof of relationship between Familiarity with newly developed fabrics and Experience Crosstab Count experience less than 5 years 5-10 years pqcnewdevfabri yes cs no Total 22 13 35 19 2 21 10-15 years 3 3 6 15-20 years 1 1 2 Total 45 19 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 6.604a 3 .086 Likelihood Ratio 7.370 3 .061 Linear-by-Linear .040 1 .841 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .59. There is Significant relationship between Familiarity with newly developed fabrics and Experience of the respondent (at 0.1 significance level)

88

Table 56: Significance of relationship between Familiarity with newly developed fabrics and Qualification

Qualification highe r ssl secon dip tex c dary tech pqcnewdevfabri yes cs no Total 0 1 1 6 4 10 4 2 6 Textile engg gra fashion othe graduat e design r gra 9 3 12 14 5 19 2 1 3

Other dip 10 3 13

Total 45 19 64

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 3.437 6 .752 Likelihood Ratio 3.489 6 .745 Linear-by-Linear .802 1 .371 Association N of Valid Cases 64 a. 9 cells (64.3%) have expected count less than 5. The minimum expected count is .30. There is no Significant relationship between Familiarity with newly developed fabrics and Qualification of the respondent

89

Table 57: Significance of relationship between Familiarity with Statistical Quality Control and Operations Research and Experience Crosstab Count experience less than 5 years 5-10 years psqcandor yes no Total 18 17 35 17 4 21 10-15 years 3 3 6 15-20 years 1 1 2 Total 39 25 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.266a 3 .153 Likelihood Ratio 5.603 3 .133 Linear-by-Linear .526 1 .468 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .78. There is no Significant relationship between Familiarity with Statistical Quality Control and Operations Research and Experience of the respondent

90

Table 58: Significance of relationship between Familiarity with Statistical Quality Control and Operations Research and Qualification

Qualification textile engg higher gra second dip tex other fashion other gradu sslc ary tech dip design gra ate Total psqcand ye or s no Total 0 1 1 6 4 10 5 1 6 11 2 13 6 6 12 9 10 19 2 1 3 39 25 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 8.004a 6 .238 Likelihood Ratio 8.865 6 .181 Linear-by-Linear 1.003 1 .317 Association N of Valid Cases 64 a. 8 cells (57.1%) have expected count less than 5. The minimum expected count is .39. There is no Significant relationship between Familiarity with Statistical Quality Control and Operations Research and Qualification of the respondents.

91

Table 59: Significance of relationship between Familiarity with Lighting impact, ergonomics and other industrial engineering aspects and Experience

Crosstab Count experience less than 5 years 5-10 years plightingergo yes ie no Total 19 16 35 15 6 21 10-15 years 3 3 6 15-20 years 1 1 2 Total 38 26 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.932a 3 .587 Likelihood Ratio 1.979 3 .577 Linear-by-Linear .074 1 .786 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .81. There is no Significant relationship between Familiarity with Lighting impact, ergonomics and other industrial engineering aspects and Experience of the respondent.

92

Table 60: Significance of t relationship between Familiarity with Lighting impact, ergonomics and other industrial engineering aspects and Qualification

Qualification higher second dip tex other ary tech dip 0 1 1 4 6 10 4 2 6 7 6 13 gra fashion design 7 5 12 textileen gg other gra graduate Total 13 6 19 3 0 3 38 26 64

sslc plightingergoie ye s no Total

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 6.017 6 .421 Likelihood Ratio 7.417 6 .284 Linear-by-Linear 3.830 1 .050 Association N of Valid Cases 64 a. 8 cells (57.1%) have expected count less than 5. The minimum expected count is .41. There is no Significant relationship between Familiarity with Lighting impact, ergonomics and other industrial engineering aspects and Qualification of the respondent.

93

Table 61: Significance of relationship between Familiarity with Lean Manufacturing techniques and Experience Crosstab Count experience less than 5 years 5-10 years pleanmfrg yes no Total 14 21 35 7 14 21 10-15 years 4 2 6 15-20 years 1 1 2 Total 26 38 64

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 2.228a 3 .526 Likelihood Ratio 2.204 3 .531 Linear-by-Linear .484 1 .487 Association N of Valid Cases 64 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .81. There is no Significant relationship between Familiarity with Lean Manufacturing techniques and Experience of the respondent.

94

Table 62: Significance of relationship between Familiarity with Lean Manufacturing techniques and Qualification

Qualification high er seco ndar dip tex tech sslc y pleanmfrg yes no Total 0 1 1 4 6 10 3 3 6 textilee ngg other graduat Total gra e 4 15 19 1 2 3 26 38 64

other dip 8 5 13

gra fashion design 6 6 12

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 6.783a 6 .341 Likelihood Ratio 7.347 6 .290 Linear-by-Linear 1.716 1 .190 Association N of Valid Cases 64 a. 8 cells (57.1%) have expected count less than 5. The minimum expected count is .41. There is no Significant relationship between Familiarity with Lean Manufacturing techniques and Qualification of the respondent

95

Table 63 Significance of relationship between Familiarity with Prospecting, Vendor Evaluation techniques and Experience Crosstab Count experience less than 5 years 5-10 years mvendor yes no Total 18 10 28 6 1 7 10-15 years 5 3 8 15-20 years 3 1 4 Total 32 15 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.390a 3 .708 Likelihood Ratio 1.542 3 .673 Linear-by-Linear .126 1 .723 Association N of Valid Cases 47 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is 1.28. There is no Significant relationship between Familiarity with Prospecting, Vendor Evaluation techniques and Experience of the respondent.

96

Table 64 Significance of relationship between Familiarity with Prospecting, Vendor Evaluation techniques and Qualification

Qualification high er gra textilee seco fashio ngg ndar pg in other graduat n Total y other dip design textile gra e mvendor yes yes Total 0 1 1 5 3 8 6 3 9 1 1 2 17 7 24 3 0 3 32 15 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.047a 5 .543 Likelihood Ratio 5.076 5 .407 Linear-by-Linear 1.934 1 .164 Association N of Valid Cases 47 a. 8 cells (66.7%) have expected count less than 5. The minimum expected count is .32. There is no Significant relationship between Familiarity with Prospecting, Vendor Evaluation techniques and Qualification of the respondent.

97

Table 65 Significance of relationship between Familiarity with Sample and Product Development techniques and experience Crosstab Count experience less than 5 years 5-10 years msampledev yes no Total 27 1 28 6 1 7 10-15 years 8 0 8 15-20 years 4 0 4 Total 45 2 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 2.294a 3 .514 Likelihood Ratio 2.172 3 .538 Linear-by-Linear .118 1 .732 Association N of Valid Cases 47 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .17. There is no Significant relationship between Familiarity with Sample and Product Development techniques and experience of the respondent.

98

Table 66 Significance of relationship between Familiarity with Sample and Product Development techniques and experience

Qualification Textile engg higher gra secon other fashion pg in other graduat Total dip design textile gra e dary msampledev y es n o Total 1 0 1 7 1 8 8 1 9 2 0 2 24 0 24 3 0 3 45 2 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 3.706a 5 .592 Likelihood Ratio 4.234 5 .516 Linear-by-Linear 2.392 1 .122 Association N of Valid Cases 47 a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .04. There is no Significant relationship between Familiarity with Sample and Product Development techniques and Qualification of the respondent.

99

Table 67 Significance of relationship between Familiarity with Printing Dyeing and Washing methods and experience Crosstab Count experience less than 5 years 5-10 years mprintdyewas yes h no Total 25 3 28 6 1 7 10-15 years 7 1 8 15-20 years 4 0 4 Total 42 5 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .603a 3 .896 Likelihood Ratio 1.017 3 .797 Linear-by-Linear .110 1 .740 Association N of Valid Cases 47 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .43. There is no Significant relationship between Familiarity with Printing Dyeing and Washing methods and experience of the respondent.

100

Table 68 Significance of relationship between Familiarity with Printing Dyeing and Washing methods and Qualification

Qualification higher gra second other fashion pg in textileengg ary dip design textile other gra graduate Total mprintdyewas ye h s no Total 1 0 1 7 1 8 8 1 9 2 0 2 21 3 24 3 0 3 42 5 47

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square .833 5 .975 Likelihood Ratio 1.463 5 .917 Linear-by-Linear .001 1 .972 Association N of Valid Cases 47 a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .11. There is no Significant relationship between Familiarity with Printing Dyeing and Washing methods and Qualification of the respondent

101

Table 69 Significance of relationship between Familiarity with sketch studying and Garment Construction methods and Experience Crosstab Count experience less than 5 years 5-10 years msketchgarmentco yes nst no Total 25 3 28 6 1 7 10-15 years 7 1 8 15-20 years 2 2 4 Total 40 7 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.313a 3 .230 Likelihood Ratio 3.178 3 .365 Linear-by-Linear 2.263 1 .132 Association N of Valid Cases 47 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .60. There is no Significant relationship between Familiarity with sketch studying and Garment Construction methods and Experience of the respondent.

102

Table 70 Significance of relationship between Familiarity with sketch studying and Garment Construction methods and Qualification

Qualification textilee ngg higher gra second other fashion pg in graduat dip design textile other gra e ary msketchgarmentcon ye st s no Total 0 1 1 5 3 8 8 1 9 1 1 2 23 1 24 3 0 3

Total 40 7 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 13.690a 5 .018 Likelihood Ratio 11.610 5 .041 Linear-by-Linear 8.554 1 .003 Association N of Valid Cases 47 a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .15. There is Significant relationship between Familiarity with sketch studying and Garment Construction methods and Qualification of the respondent.

103

Table 71 Significance of relationship between Familiarity with department wise costing details and Experience Crosstab Count experience less than 5 years 5-10 years mdeptcosttec yes h no Total 20 8 28 6 1 7 10-15 years 8 0 8 15-20 years 3 1 4 Total 37 10 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 3.289a 3 .349 Likelihood Ratio 4.911 3 .178 Linear-by-Linear 1.419 1 .234 Association N of Valid Cases 47 a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .85. There is no Significant relationship between Familiarity with department wise costing details and Experience of the respondent.

104

Table 72 Significance of relationship between Familiarity with department wise costing details and Qualification

Qualification textilee ngg higher gra pg in secon other fashion textil othe graduat Total e dary dip design e r gra mdeptcosttec yes h no Total 1 0 1 6 2 8 5 4 9 2 0 2 21 3 24 2 1 3 37 10 47

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 5.125 5 .401 Likelihood Ratio 5.388 5 .370 Linear-by-Linear .649 1 .421 Association N of Valid Cases 47 a. 8 cells (66.7%) have expected count less than 5. The minimum expected count is .21. There is no Significant relationship between Familiarity with department wise costing details and Qualification of the respondent.

105

Table 73 Significance of relationship between Familiarity with Communication, Interpersonal skills and Experience

Crosstab Count experience less than 5 years 5-10 years mcommun yes no Total 25 3 28 7 0 7 10-15 years 7 1 8 15-20 years 4 0 4 Total 43 4 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.361a 3 .715 Likelihood Ratio 2.264 3 .519 Linear-by-Linear .246 1 .620 Association N of Valid Cases 47 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .34. There is no Significant relationship between Familiarity with Communication, Interpersonal skills and Experince.of the respondent.

106

Table 74 Significance of relationship between Familiarity with Communication, Interpersonal skills and Qualification

Qualification text ilee gra ngg higher fashio pg in gra second other n textil other dua ary dip design e gra te mcommun ye s no Total 1 0 1 8 0 8 6 3 9 1 1 2 24 0 24 3 0 3

Total 43 4 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 14.892a 5 .011 Likelihood Ratio 13.130 5 .022 Linear-by-Linear 1.287 1 .257 Association N of Valid Cases 47 a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .09. There is Significant relationship between Familiarity with Communication, Interpersonal skills and Qualification of the respondent.

107

Table 75 Significance of relationship between Familiarity with Fabrics, Consumption Details and Experience Crosstab Count experience less than 5 years 5-10 years mfabricconsum yes p no Total 26 2 28 6 1 7 10-15 years 8 0 8 15-20 years 3 1 4 Total 43 4 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 2.508a 3 .474 Likelihood Ratio 2.710 3 .439 Linear-by-Linear .268 1 .605 Association N of Valid Cases 47 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .34. There is no Significant relationship between Familiarity with Fabrics, Consumtion details and Experience of the respondent.

108

Table 76 Significance of relationship between Familiarity with Fabric Consumption Details and Qualification Qualification highe r gra secon other fashion pg in dary dip design textile mfabricconsum yes p no Total 1 0 1 7 1 8 8 1 9 2 0 2 textil eeng g grad Tot other gra uate al 22 2 24 3 43 0 4 3 47

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .801a 5 .977 Likelihood Ratio 1.285 5 .936 Linear-by-Linear .159 1 .690 Association N of Valid Cases 47 a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .09. There is no Significant relationship between Familiarity with Fabric Consumption Details and Qualification of the respondent.

109

Table 77 Significance of relationship between Familiarity with Fabrics, Geographical availability and Price and Experience Crosstab Count experience less than 5 years 5-10 years msfabavasilpric yes e no Total 18 0 18 14 3 17 10-15 years 2 0 2 15-20 years 3 0 3 >20 years 1 0 1 Total 38 3 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.570a 4 .334 Likelihood Ratio 5.621 4 .229 Linear-by-Linear .095 1 .758 Association N of Valid Cases 41 a. 8 cells (80.0%) have expected count less than 5. The minimum expected count is .07. There is no Significant relationship between Familiarity with Fabrics, Geographical availability and Price and Experience of the respondent.

110

Table 78 Significance of relationship between Familiarity with Fabrics, Geographical availability and Price and Qualification

Qualification higher dip gra seconda tex fashion sslc ry tech other dip design msfabavasilpric yes e no Total 2 0 2 5 2 7 1 1 2 3 0 3 6 0 6 textilee ngg other graduat Total gra e 1 0 1 19 0 19 1 0 1 38 3 41

pg in textile

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 12.562 7 .084 Likelihood Ratio 10.316 7 .171 Linear-by-Linear 5.361 1 .021 Association N of Valid Cases 41 a. 13 cells (81.3%) have expected count less than 5. The minimum expected count is .07. There is Significant relationship between Familiarity with Fabrics, Geographical availability and Price and Qualification of the respondent.(at 0,1 significance level_

111

Table 79 Significance of relationship between Familiarity with Trims and Accessories-quality parameters and Experience

Crosstab Count experience less than 5 years 5-10 years mstrims yes no Total 17 1 18 14 3 17 10-15 years 2 0 2 15-20 years 3 0 3 >20 years 1 0 1 Total 37 4 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 2.212a 4 .697 Likelihood Ratio 2.647 4 .619 Linear-by-Linear .028 1 .867 Association N of Valid Cases 41 a. 8 cells (80.0%) have expected count less than 5. The minimum expected count is .10. There is no Significant relationship between Familiarity with Trims and Accessories-quality parameters and Experience of the respondent.

112

Table 80 Significance of relationship between Familiarity with Trims and Accessories-quality parameters and Qualification

Qualification higher gra secon dip tex other fashion sslc dary tech dip design mstrims yes no Total 2 0 2 6 1 7 2 0 2 2 1 3 5 1 6 textileeng Tota g pg in textile other gra graduate l 1 0 1 18 1 19 1 0 1 37 4 41

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 3.467 7 .839 Likelihood Ratio 3.412 7 .844 Linear-by-Linear .388 1 .534 Association N of Valid Cases 41 a. 13 cells (81.3%) have expected count less than 5. The minimum expected count is .10. There is no Significant relationship between Familiarity with Trims and Accessories-quality parameters and Qualification of th erespondnet.

113

Table 81 Significance of relationship between Familiarity with interacting with merchandiser and Experience Crosstab Count experience less than 5 years 5-10 years msinteractionmer yes ch no Total 17 1 18 12 5 17 10-15 years 2 0 2 15-20 years 3 0 3 >20 years 1 0 1 Total 35 6 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.188a 4 .269 Likelihood Ratio 5.816 4 .213 Linear-by-Linear .000 1 .991 Association N of Valid Cases 41 a. 8 cells (80.0%) have expected count less than 5. The minimum expected count is .15. Ther is no Significant relationship between Familiarity with interacting with merchandiser and Experience of the respondent.

114

Table 82 Significance of relationship between Familiarity with interacting with merchandiser and Qualification

Qualification high er seco dip ndar tex sslc y tech msinteractionmer ye ch s no Total 2 0 2 4 3 7 1 1 2 gra fash ion pg in textileen other desi texti other gg dip gn le gra graduate Total 3 0 3 6 0 6 1 0 1 17 2 19 1 0 1 35 6 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 8.951a 7 .256 Likelihood Ratio 9.017 7 .251 Linear-by-Linear 2.536 1 .111 Association N of Valid Cases 41 a. 13 cells (81.3%) have expected count less than 5. The minimum expected count is .15. There is no Significant relationship between Familiarity with interacting with merchandiser and Qualification of the respondent.

115

Table 83 Significance of relationship between Familiarity with Negotiating and communication skills and Experience Crosstab Count experience less than 5 years 5-10 years msnegotiatecom yes m no Total 17 1 18 15 2 17 10-15 years 2 0 2 15-20 years 3 0 3 >20 years 1 0 1 Total 38 3 41

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 1.052 4 .902 Likelihood Ratio 1.425 4 .840 Linear-by-Linear .086 1 .769 Association N of Valid Cases 41 a. 8 cells (80.0%) have expected count less than 5. The minimum expected count is .07. There si no Significant relationship between Familiarity with Negotiating and communication skills and Experience of the respondent

116

Table 84 Significance of relationship between Familiarity with Negotiating and communication skills and Qualification

Qualification texti leen gra fashio pg in gg other n textil other grad Tot dip design e gra uate al 3 0 3 5 1 6 1 0 1 19 0 19 1 38 0 3 1 41

higher dip secon tex sslc dary tech msnegotiatecom ye m s no Total 2 0 2 5 2 7 2 0 2

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 7.647 7 .365 Likelihood Ratio 7.682 7 .361 Linear-by-Linear 3.121 1 .077 Association N of Valid Cases 41 a. 13 cells (81.3%) have expected count less than 5. The minimum expected count is .07. There is no Significant relationship between Familiarity with Negotiating and communication skills and Qualification of the respondent.

117

Table 85 Significance of relationship between Familiarity with incoming quality inspection, Lot to lot variation of incoming materials and Experience Crosstab Count experience less than 5 years 5-10 years msincomqc yes Total 18 18 17 17 10-15 years 2 2 15-20 years 3 3 >20 years 1 1 Total 41 41

. No statistics are computed because this dependent variable is a constant Table 86 Significance of relationship between Familiarity with incoming quality inspection, Lot to lot variation of incoming materials and Qualification Crosstab Count Qualification higher second dip tex othe sslc ary tech r dip msincomqc yes Total 2 2 7 7 2 2 3 3 gra fashion design 6 6 pg Textile in engg texti gradua le other gra te Total 1 1 19 19 1 1 41 41

No statistics are computed because this dependent variable is a constant

118

Table 87 Significance of relationship between Familiarity with Prospecting and selecting employees and Experience Crosstab Count experience less than 5 years 5-10 years hrprospectnselecti yes on Total 14 14 21 21 10-15 years 4 4 15-20 years 2 2 Total 41 41

No statistics are computed because this dependent variable is a constant

Table 88 Significance of relationship between Familiarity with Prospecting and selecting employees and Qualification Qualification Texti le engg other gradu gra ate 2 2 35 35 2 2

higher gra second fashion ary other dip design hrprospectnselecti yes on Total 1 1 1 1

Total 41 41

No statistics are computed because this dependent variable is a constant

Table 89 Significance of relationship between Familiarity with various Laws of Industrial Relations and Experience

119

Crosstab Count experience less than 5 years 5-10 years hrlawsnir yes no Total 13 1 14 Chi-Square Tests Value df Asymp. Sig. (2-sided) 21 0 21 10-15 years 4 0 4 15-20 years 2 0 2 Total 40 1 41

Pearson Chi-Square 1.977a 3 .577 Likelihood Ratio 2.198 3 .532 Linear-by-Linear 1.189 1 .275 Association N of Valid Cases 41 a. 6 cells (75.0%) have expected count less than 5. The minimum expected count is .05. There is no Significant relationship between Familiarity with various Laws of Industrial Relations and Experience of the Respondent.

120

Table 90 Significance of relationship between Familiarity with various Laws of Industrial Relations and Qualification

Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate hrlawsnir yes no Total 1 0 1 1 0 1 2 0 2 34 1 35 2 0 2 Total 40 1 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .176a 4 .996 Likelihood Ratio .321 4 .988 Linear-by-Linear .059 1 .809 Association N of Valid Cases 41 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .02. There is no Significant relationship between Familiarity with various Laws of Industrial Relations and Qualification of the respondent.

121

Table 91 Significance of relationship between Familiarity with various Welfare measures and Experience

Crosstab Count experience less than 5 years 5-10 years hrwelfare yes no Total 13 1 14 20 1 21 10-15 years 4 0 4 15-20 years 2 0 2 Total 39 2 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .463a 3 .927 Likelihood Ratio .737 3 .864 Linear-by-Linear .419 1 .518 Association N of Valid Cases 41 a. 6 cells (75.0%) have expected count less than 5. The minimum expected count is .10. There is no Significant relationship between Familiarity with various Welfare measures and Experience of the respondent.

122

Table 92 Significance of relationship between Familiarity with various Welfare measures and Qualification

Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate hrwelfare yes no Total 1 0 1 1 0 1 2 0 2 33 2 35 2 0 2 Total 39 2 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .360a 4 .986 Likelihood Ratio .650 4 .957 Linear-by-Linear .120 1 .729 Association N of Valid Cases 41 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .05. There is no Significant relationship between Familiarity with various Welfare measures and Qualification of the respondent

123

Table 93 Significance of relationship between Familiarity with procedures of Rewarding employees for Better performance and Experince Crosstab Count experience less than 5 years 5-10 years hrrewardem yes p Total 14 14 21 21 10-15 years 4 4 15-20 years 2 2 Total 41 41

No statistics are computed because this dependent variable is a constant Table 94 Significance of relationship between Familiarity with procedures of Rewarding employees for Better performance and Qualification

Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate hrrewardem yes p Total 1 1 1 1 2 2 35 35 2 2 Total 41 41

No statistics are computed because this dependent variable is a constant

124

Table 95 Significance of relationship between Familiarity with measuring performance of Employees and Experience Crosstab Count experience less than 5 years 5-10 years hrperfmeasur yes e no Total 13 1 14 21 0 21 10-15 years 4 0 4 15-20 years 2 0 2 Total 40 1 41

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 1.977 3 .577 Likelihood Ratio 2.198 3 .532 Linear-by-Linear 1.189 1 .275 Association N of Valid Cases 41 a. 6 cells (75.0%) have expected count less than 5. The minimum expected count is .05. There is no Significant relationship between Familiarity with measuring performance of Employees and Experience of the respondent.

125

Table 96 Significance of relationship between Familiarity with measuring performance of Employees and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate hrperfmeasur yes e no Total 1 0 1 1 0 1 2 0 2 34 1 35 2 0 2 Total 40 1 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .176a 4 .996 Likelihood Ratio .321 4 .988 Linear-by-Linear .059 1 .809 Association N of Valid Cases 41 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .02. There is no Significant relationship between Familiarity with measuring performance of Employees and Qualification of the respondent.

126

Table 97 Significance of relationship between Familiarity with Training and Development of Employees and Qualification Crosstab Count experience less than 5 years 5-10 years hrtraingndev yes p no Total 14 0 14 20 1 21 10-15 years 4 0 4 15-20 years 2 0 2 Total 40 1 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .976a 3 .807 Likelihood Ratio 1.362 3 .714 Linear-by-Linear .035 1 .852 Association N of Valid Cases 41 a. 6 cells (75.0%) have expected count less than 5. The minimum expected count is .05. There is no Significant relationship between Familiarity with Training and Development of Employees and experience of the respondent.

127

Table 98 Significance of relationship between Familiarity with Training and Development of Employees and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate hrtraingndev yes p no Total 1 0 1 1 0 1 2 0 2 34 1 35 2 0 2 Total 40 1 41

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .176a 4 .996 Likelihood Ratio .321 4 .988 Linear-by-Linear .059 1 .809 Association N of Valid Cases 41 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .02. There sis no Significant relationship between Familiarity with Training and Development of Employees and Qualification of the respondent.

128

Table 99 Significance of relationship between Familiarity with Book Keeping Practice and Experience

Crosstab Count experience less than 5 years 5-10 years finbookkee yes p no Total 9 1 10 19 1 20 10-15 years 5 1 6 15-20 years 1 1 2 Total 34 4 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.200a 3 .241 Likelihood Ratio 2.952 3 .399 Linear-by-Linear 1.723 1 .189 Association N of Valid Cases 38 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .21. There is no Significant relationship between Familiarity with Book Keeping Practice and Experience of the respondent.

129

Table 100 Significance of relationship between Familiarity with Book Keeping Practice and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate finbookkee yes p no Total 1 0 1 1 0 1 1 0 1 30 4 34 1 0 1 Total 34 4 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .526a 4 .971 Likelihood Ratio .943 4 .918 Linear-by-Linear .252 1 .616 Association N of Valid Cases 38 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .11. There is no Significant relationship between Familiarity with Book Keeping Practice and Qualification of the respondent.

130

Table 101 Significance of relationship between Familiarity with Computerised accounting method and experience Crosstab Count experience less than 5 years 5-10 years fincompute yes r no Total 8 2 10 20 0 20 10-15 years 6 0 6 15-20 years 2 0 2 Total 36 2 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.911a 3 .116 Likelihood Ratio 5.663 3 .129 Linear-by-Linear 3.255 1 .071 Association N of Valid Cases 38 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .11. There is no Significant relationship between Familiarity with Computerised accounting method and experience of the respondent.

131

Table 102 Significance of relationship between Familiarity with Computerized accounting method and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate fincompute yes r no Total 1 0 1 1 0 1 1 0 1 32 2 34 1 0 1 Total 36 2 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .248a 4 .993 Likelihood Ratio .458 4 .977 Linear-by-Linear .119 1 .730 Association N of Valid Cases 38 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .05. There is no Significant relationship between Familiarity with Computerized accounting method and Qualification of the respondent.

132

Table 103 Significance of relationship between Familiarity with working capital Management Practices and Experience Crosstab Count experience less than 5 years 5-10 years finwc yes no Total 9 1 10 18 2 20 10-15 years 6 0 6 15-20 years 1 1 2 Total 34 4 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.024a 3 .259 Likelihood Ratio 3.296 3 .348 Linear-by-Linear .431 1 .512 Association N of Valid Cases 38 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .21. There is no Significant relationship between Familiarity with working capital Management Practices and Experience of the respondent.

133

Table 104 Significance of relationship between Familiarity with working capital Management Practices and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate finwc yes no Total 1 0 1 1 0 1 1 0 1 30 4 34 1 0 1 Total 34 4 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .526a 4 .971 Likelihood Ratio .943 4 .918 Linear-by-Linear .252 1 .616 Association N of Valid Cases 38 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .11. There is no Significant relationship between Familiarity with working capital Management Practices and Qualification of the respondent.

134

Table 105 Significant relationship between Familiarity with cash Management Practices and Experience

Crosstab Count experience less than 5 years 5-10 years fincashmanag yes e no Total 9 1 10 19 1 20 10-15 years 6 0 6 15-20 years 1 1 2 Total 35 3 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.682a 3 .128 Likelihood Ratio 3.776 3 .287 Linear-by-Linear .558 1 .455 Association N of Valid Cases 38 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .16. There is no Significant relationship between Familiarity with cash Management Practices and Experience of the respondent.

135

Table 106 Significance of relationship between Familiarity with cash Management Practices and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate fincashmanag yes e no Total 1 0 1 1 0 1 1 0 1 31 3 34 1 0 1 Total 35 3 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .383a 4 .984 Likelihood Ratio .697 4 .952 Linear-by-Linear .183 1 .669 Association N of Valid Cases 38 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .08. There is no Significant relationship between Familiarity with cash Management Practices and Qualification of the respondent.

136

Table 107 Significance of relationship between Familiarity with banking Procedures and Experience Crosstab Count experience less than 5 years 5-10 years finbankin yes g no Total 9 1 10 15 5 20 10-15 years 6 0 6 15-20 years 2 0 2 Total 32 6 38

Chi-Square Tests Value
a

df

Asymp. Sig. (2-sided)

Pearson Chi-Square 3.028 3 .387 Likelihood Ratio 4.153 3 .245 Linear-by-Linear .305 1 .581 Association N of Valid Cases 38 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .32. There is no Significant relationship between Familiarity with banking Procedures and Experience of the respondent.

137

Table 108 Significant relationship between Familiarity with banking Procedures and Experience Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate finbankin yes g no Total 1 0 1 1 0 1 1 0 1 28 6 34 1 0 1 Total 32 6 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .838a 4 .933 Likelihood Ratio 1.460 4 .834 Linear-by-Linear .401 1 .527 Association N of Valid Cases 38 a. 8 cells (80.0%) have expected count less than 5. The minimum expected count is .16. There is no Significant relationship between Familiarity with banking Procedures and Qualification

138

Table 109 Significant relationship between Familiarity with various taxation Procedures and Experience Crosstab Count experience less than 5 years 5-10 years fintax yes no Total 9 1 10 20 0 20 10-15 years 5 1 6 15-20 years 2 0 2 Total 36 2 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 3.237a 3 .357 Likelihood Ratio 3.762 3 .288 Linear-by-Linear .000 1 1.000 Association N of Valid Cases 38 a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .11. There is no Significant relationship between Familiarity with various taxation Procedures and Experience of the respondent.

139

Table 110 Significant relationship between Familiarity with various taxation Procedures and Qualification Crosstab Count Qualification higher gra fashion textileengg secondary other dip design other gra graduate fintax yes no Total 1 0 1 1 0 1 1 0 1 32 2 34 1 0 1 Total 36 2 38

Chi-Square Tests Value df Asymp. Sig. (2-sided)

Pearson Chi-Square .248a 4 .993 Likelihood Ratio .458 4 .977 Linear-by-Linear .119 1 .730 Association N of Valid Cases 38 a. 9 cells (90.0%) have expected count less than 5. The minimum expected count is .05. There is no Significant relationship between Familiarity with various taxation Procedures and Qualification of the respondent.

140

Chapter 5 Findings and Conclusion Based the Analysis of data, the following conclusions are arrived at.
24% of the respondents belong to Merchandising Department. Production personnel were 22% while Human resource executives made up 18% of the respondents. About a fifth were from Fabric sourcing and 16.5 % belong to finance and Costing Graduate degree holders from streams other than Textiles make up about 60% of the respondents. A tenth are Higher secondary passed and Diploma in streams other than Textile are another one tenth and Graduates degree holders in Fashion Design make up one tenth of the respondents. Engineers in Textiles are just 3%while Postgraduates in Textiles are a mere one and a half percent. Diploma holders in Textiles are 4% and Matriculation passed are just 1.5%. A vast Majority of the respondents are having Experience of less than 10 years and half of them are having experience less than 5 years. A tenth are having experience between 10-15 years and only about 5% are having experience between 15-20 years. A vast Majority are confident of possessing Human relationship skills Almost everybody are confident of possessing sufficient Knowledge to perform their Tasks.

141

Based on Chisquare analysis, there is significant relationship between The perception that their experience and knowledge is sufficient to discharge their responsibilities of their area of function and the experience of the respondent at 0.05 statistical significance level. Except for a tenth of the respondents , others are confident of having Updated Technical Knowledge in their respective Domains Based on Chisquare analysis, there is significant relationship between their perception of their technical knowledge being uptpdate and the Experience of the respondent. This statistically significant at the 0.05 significance level. A vast Majority prefer the weekends especially Sundays for the Training Programs, as they are occupied with their work on weekdays. A bit more than a tenth are unable to find time for Training.

5.1 Production Functional area.
Based on Percentage analysis, the respondents are familiar in varying degrees with Standard Alerted Minute (SAM), quality controlling techniques as well as newly developed fabrics, Lighting impact, ergonomics and other industrial engineering aspects, Statistical Quality Control and Operations Research and production planning The respondents are not so familiar with budgeting and costing,machinery planning and layout and Lean manufacturing.

142

Based on Chisquare analysis, there is significant relationship between quality controlling techniques as well as newly developed fabrics and experience of the respondent.And there is significant relationship between budgeting and costing methods in Production and Qualification of the respondent. Both are statistically significant at the 0.1 significance level.

5.2 Merchandising functional area
Based on Percentage analysis, the respondents are familiar in varying degrees with Prospecting, Vendor Evaluation, Sample and Product Development techniques, Printing Dyeing and Washing methods sketch studying and Garment Construction methods, department wise costing details, Communication, Interpersonal skills and fabric consumption details Based on Chisquare analysis, there is significant relationship between sketch studying and Garment Construction methods in merchandising and the qualification of the respondent. And there is significant relationship between Communication, Interpersonal skills and the Qualification of the respondent. Both are statistically significant at the 0.05 significance level.

5.3 Material Sourcing Functional Area
Based on Percentage analysis, the respondents are familiar in varying degrees with specification of Fabrics, Geographical availability and Price, Trims and Accessories-quality parameters, interacting with merchandiser for requisition Negotiating and communication skills incoming quality inspection and Lot to lot variation of incoming materials Based on Chisquare analysis, there is significant relationship between

143

specification of Fabrics, Geographical availability and Price and Qualification of the respondent. This is statistically significant at the 0.05 significance level.

5.4 Human resources Functional area
Based on Percentage analysis, the respondents are familiar in varying degrees with Prospecting and selecting employees for various positions ,various Laws of Industrial Relation,s various Welfare measures ,the procedures of Rewarding employees for Better performance, measuring performance of Employees and Training and Development of Employees.

5.5 Finance Functional area
Based on Percentage analysis, the respondents are familiar in varying degrees with Book Keeping Practice, Computerised accounting method, working capital Management Practices,cash Management ,banking Procedures and various taxation Procedures Since the Qualification of most of the respondents are not commensurate with the Jobs, Training in various functional areas are required,. The Training modules for 20 days in various functional areas was developed based on the analysis of data.

144

Chapter 6 The Training Modules. The Training modules were developed based on the analysis of data and the Conclusions drawn. 6.1 .Merchandising Functional area
MODULE 1 – Apparel Industry Structure – an Introduction DAY 1 – An overview of industry structure including all the key stake holders. DAY 2 – An Understanding of product life cycle and seasons in apparel industry. DAY 3 – An understanding of various target segments – designer label to discount stores. MODULE 2 – Decoding the Process and Role of Merchandiser DAY 4 – An understanding of trends forecasts, research and development, competitive shopping, international fairs. DAY 5 – Design and Prototype development and initial costing. DAY 6 – – Role and interface of Merchandiser at production - Pre-production – Production – Post Production – interface with different stake holders

MODULE 3 – Planning and Execution in a multi style environment within limited time and resources. DAY7 – understanding of various lead times – fabric, processing, transit, production. DAY 8 – Critical Path Management – application of fundamentals in applied apparel merchandising. DAY 9 – Tools for order tracking, control and monitoring. DAY 10 – Risk management and risk response planning. DAY 11 – Change management and control in apparel merchandising MODULE 4 – Basic technical knowledge and Retail merchandising DAY 12 – understanding of design basics – styles, silhouettes, basic sketches. DAY 13 –Technical knowledge for merchandisers -understanding of basic stitch types

145

– basics of fit evaluation and pattern correction – identification of patterns, methods of measurements etc . DAY14 – Basics of Quality – AQL, Just in time, – TQM, Process reengineering, Kaizen, Benchmarking, fishbone, Pareto charts etc. – Basic defects – fabric and garment – Fabric testing & evaluation MODULE 5 – Sourcing skills DAY 15 – evaluation of supplier sources and negotiation. - using micro and macro perspective – Negotiation strategy and tactics. DAY16 – Sourcing fundamentals – - Key factors in sourcing decisions. - Comparative analysis of various sourcing destinations. DAY 17&18 – Costing / Pricing – micro and macro perspective DAY 19 – View from the Buyer’s side – landed costs, retail margins, customer returns, claims etc. MODULE 6 – Smart Merchandising skills DAY 20 – Basics of filing, record keeping, paperwork, approvals and samples, and professional templates and SOPs for effective merchandising.

146

6.2 Production Functional area
Time Title Registration Inauguration Textiles & Apparel – Introduction to current scenario, International and national perspective. – Market dynamics. Apparel Production Technologies – Introduction to tech. used across the globe and advantages and disadvantages. – Technology Management Lunch Resource Person

DAY I
9.00-10.00 AM 10.00-11.00 AM 11.15-12.15 AM 12.15 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM Chief Guest:

3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Product development: Steps from prototype to production model – Importance of pre-production activities – Product data management: Understanding and interpretation of specification sheet
Tea

Determination of machine requirements Basic Pattern Making: Measurement taking – Size chart and meaning of sizes – Definition of various garment parts and positions – Drafting: Basic principles used to draft standard size block patterns Drafting of sleeve and collar & Computer grading
Tea Computerized production pattern making – Hardware, software and system programming to produce a sample production pattern – Computer aided manipulation of pattern pieces to create individual styles spreading and cutting – Types and functions – Spreading and cutting machines – Developments in spreading and cutting including computer aided machines Lunch Sewing machinery Classification - Concept of sewing machinery functions Stitch and Seam Classification Tea Sewing needle and sewing thread specification, thread consumption

DAY II
9.30-10.30 AM

10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM

12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

DAY III

Industrial Visit – Most Modern Apparel Industry
147

DAY IV
9.30-10.30 AM 10.30-11.30 AM Planning a logical garment construction sequence Construction techniques of garment closures: Application of zippers – fly, kissing, lap; Button and buttonholes, hooks and eye snaps, Velcro Tea Sewing problems and their remedies Classification and tabulation of data, construction of frequency diagram and its applications- Quality– Measure of dispersion, Lunch Mean and standard deviation, co-efficient of variation- Quality control charts for variables and attributes – Acceptance sampling – AQL – Test of Significance Tea Quality Assurance – ISO 9000 Quality System Concept and application of fibre quality parameters of natural (Length, strength, fineness, maturity, moisture and trash) and man-made fibres ( Length, strength, fineness and crimp) – Fibre quality index and its relation with yarn strength and evenness Quality parameters of spun(Count and Strength and its CV %, , Evenness, imperfection, hairiness, Classimat faults) and filament yarns (Count and Strength and its CV % , evenness) – Yarn testing concept application Tea Quality parameters of woven and knitted fabrics – Principle and concept of Physical testing of fabrics – Fabric handle – Fabric Inspection – Fabric defects – Fabric grading system

11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

DAY V
9.30-10.30 AM

10.30-11.30 AM

11.30 – 11.45 AM 11.45-12.45 PM

12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Lunch Garment quality parameters – Quality control in pattern making, cutting and stitching – Quality of trims and accessories Tea Quality control in garment finishing – Defects in garments

DAY VI DAY VII
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM

Industrial Visit – Exposure to modern Testing
Job order Costing and its application in Garment industry. Marginal Costing technique for decision making Costing in Knitting and Garments– Elements of cost Tea Calculation of garment weight of different sizes, Dia determination, Setting the knitting program, Dyeing program Consumption of fabric per garment- Estimating of cost of process loss in Compacting, Bleaching, Raising, Shearing ,

148

Printing and Dyeing 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM Lunch Estimating the Knitting rates- Calculation of CMT charges. Cost sheet with Profit margins and foreign quotes. Tea New concepts in costing – Activity based costing – Target costing – Cost restructuring issues and Cost Reduction Measures in the textile industry Preparatory processes of woven fabrics – Singeing – Desizing – Scouring – Bleaching – Mercerizing – Heat setting – Other preparatory processes – Process flow charts – Machineries. Classification of dyes – Theory of dyeing – Banned dyes and chemicals – Water quality – Water analysis – Waste water treatment. Tea Dyeing of cotton – Dyeing of polyester – Dyeing of blends – Wool and Silk dyeing Yarn dyeing Lunch Woven fabric dyeing Knit fabric dyeing Tea Garment dyeing – Washing – Stone washing, acid washing, enzyme washing, bio polishing, bleaching, laser fading and ozone fading - laundering equipment and procedures – garment processing machinery.

DAY VIII
9.30-10.30 AM

10.30-11.30 AM

11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

DAY IX DAY X
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Industrial Visit – Exposure to Dyeing and Finishing
Finishing of woven fabrics – Finishing of knitted fabrics – Tubular and open-width finishing. Softener finish – Anti-shrink finish – Resin finish – Water proof finish – Fire retardant finish – Anti-bacterial finish. Tea Modern developments in chemical processing State and modernization of textile chemical processing industry Lunch Finishing: Optical brightening, stiffening, softening, crease resistant and crease retentive finish, anti-static finish, antibacterial finish, water proofing, flame proofing, soil release finish, mildew and moth proofing – Stain removal, care labels. Tea Mechanical finishing : raising, sueding, other surface effects.

149

DAY XI
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM Product evaluation and profiling. Production System - Products and Services – POM functions – Operation Strategies Tea Competitive priorities of textile industry Productivity – Productivity Improvement Lunch Demand Forecasting – Delphi method – Moving Averages – Exponential Smoothing –Simple Regression and Correlation analysis Production Planning and Control in textile industry – Aggregate planning – Master production schedule – Tea Material requirement planning – Bill of material – Capacity requirement planning – Introduction to ERP

DAY XII DAY XIII
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Practical Training in Garment CAD
Inventory Management – Types of Inventory – Cost of Inventory – Fixed Order Quantity Systems – Fixed Order Period Systems Economic Order Quantity – Other Inventory models – ABC in Inventory classification – JIT in manufacturing – Kanban. Tea Manufacturing operations scheduling – Work centers – Work centre scheduling – Sequencing – Priority Rules and Techniques – Shop floor Control – Lunch Facility layout – Process layout – Product layout Line Balancing – Cellular layout Tea Job Design – Considerations in Job design – Work method analysis – Work Measurement – Time study – Work sampling – Work loads in textile manufacturing

DAY XIV DAY XV
9.30-10.30 AM

Practical Training in ERP Software
Determination and Description of Material QualityReceiving and Incoming Quality Inspection , Acceptance Sampling Plans, Vendor process capability; Cost reduction Techniques-Standardisation, Simplification and Variety Reduction; Value Analysis and Engineering Make or Buy Decision, Purchasing Research , Sources of Supply, Price Determination and Negotiation, Vendor Rating, Selection and Development,
Tea

10.30-11.30 AM

11.30 – 11.45 AM

150

11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM

Legal aspects of Purchasing ;Public purchasing and Tendering ; International Purchasing- Procedures and Documentation;.
Lunch

Purchasing of Capital equipment-Appraisal Methods, evaluating Supplier’s Efficiency Stores Layout, Classification and Codification; Material Logistics- Warehousing Management, Material Handling : Cases from Textile and Apparel Industry
Tea

4.15-4.30 PM 4.30-5.30 PM

Traffic and Transportation, Disposal of Scrap, Surplus and Obsolete materials; Inventory control of spare parts, Materials Information System.

DAY XVI DAY XVII
9.30-10.30 AM

Out Door activity based learning - Ooty Soft Skills and Management games
Introduction to energy management – need for energy conservation – Demand side management – Energy Consumption of textile machinery – Specific Energy Consumption (UKG) Cost of energy vs. sales value of textile products Tea Energy Conservation in textile industry – Energy conservation in lighting, compressors and boilers – Energy Audit in a textile mill Captive generation and different types of fuels – Non conventional energy Sources – Co-generation Lunch Types of effluents produced by textile industry – Effluent treatment processes Recent developments like Reverse Osmosis – Concept of zero discharge Tea Water quality and test methods – Quality requirement of process water and drinking water – Water Pollution – Effluent standards of pollution control boards – Solid water management Environment pollution and Industrialization – Environment impact assessment and environment management systems – Tea Air Pollution – Air pollution control and equipments in industry – Air quality monitoring Noise pollution Lunch

10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

DAY XVIII
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM

Introduction to Business Communication – Meaning and 151

3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

significance – Types – Barriers – Principles of effective communication Style of business writing
Tea

Business letters, routine, bad news, sales, collection and application – Memorandum Individual; Presentation on Business topics relevant to Textiles and Apparel-Video Feedback. Group Discussions. Seminars aimed at improving presentation skills.
Tea

DAY XIX
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Individual feedback on Scope for improvement to be provided by Faculty and internal assessment components awarded on presentation skills
Lunch Principles of non-verbal communication and their application to clothing styles and body language Speeches, introduction, thanks, occasional and thematic Dialoged communication - Interviews, selection, appraisal, discipline Tea Group communication - Structured and unstructured. Internal and External Communication of an organization Components of organizational communication. Report writing - Structure of reports - Presentation skills Effective use of audio-visual media .Cases from Textiles and Apparel Tea

DAY XX
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Conducting Meetings – Procedure – Preparing agenda Minutes of meetings – resolutions Conducting seminars and conferences – Procedures of regulating group discussions.
Lunch

Small test / Feedback / Other Discussions
Tea Closing Ceremony Chief Guest

152

6.3 Material Sourcing Functional area
Time Title Registration Inauguration Textiles & Apparel – Introduction to current scenario, International and national perspective. – Market dynamics. Textile Material Uniqueness and its properties Lunch Resource Person

DAY I
9.00-10.00 AM 10.00-11.00 AM 11.15-12.15 AM 12.15 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM Chief Guest:

Understanding and interpretation of specification sheet
Availability in the International Arena Tea

Niche products and accessories Cotton material – Fibre to End product – Availability and Value addition.
Tea

DAY II
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Silk – Fibre to End product – Availability and Value addition. Wool – Fibre to End product – Availability and Value addition.
Lunch Other Natural fibres (Coir, Pineapple, bamboo etc) Tea Interaction on commodity trading

DAY III DAY IV
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Industrial Visit – Fibre markets
Manmade fibres - Fibre to End product – Availability and

Value addition
Tea Other manmade Fibres (mineral etc) Fibre to End product –

Availability and Value addition
Lunch Mean and standard deviation, co-efficient of variation- Quality control charts for variables and attributes – Acceptance sampling – AQL – Test of Significance Tea Quality Assurance – ISO 9000 Quality System

DAY V
153

9.30-10.30 AM

10.30-11.30 AM

11.30 – 11.45 AM 11.45-12.45 PM

Concept and application of fibre quality parameters of natural (Length, strength, fineness, maturity, moisture and trash) and man-made fibres ( Length, strength, fineness and crimp) – Fibre quality index and its relation with yarn strength and evenness Quality parameters of spun(Count and Strength and its CV %, , Evenness, imperfection, hairiness, Classimat faults) and filament yarns (Count and Strength and its CV % , evenness) – Yarn testing concept application Tea Quality parameters of woven and knitted fabrics – Principle and concept of Physical testing of fabrics – Fabric handle – Fabric Inspection – Fabric defects – Fabric grading system

12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Lunch Garment quality parameters – Quality control in pattern making, cutting and stitching – Quality of trims and accessories Tea Quality control in garment finishing – Defects in garments

DAY VI DAY VII
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Industrial Visit – Exposure to modern Testing
Job order Costing and its application in Garment industry. Marginal Costing technique for decision making Costing in Knitting and Garments– Elements of cost Tea Calculation of garment weight of different sizes, Dia determination, Setting the knitting program, Dyeing program Consumption of fabric per garment- Estimating of cost of process loss in Compacting, Bleaching, Raising, Shearing , Printing and Dyeing Lunch Estimating the Knitting rates- Calculation of CMT charges. Cost sheet with Profit margins and foreign quotes. Tea New concepts in costing – Activity based costing – Target costing – Cost restructuring issues and Cost Reduction Measures in the textile industry Preparatory processes of woven fabrics – Singeing – Desizing – Scouring – Bleaching – Mercerizing – Heat setting – Other preparatory processes – Process flow charts – Machineries. Classification of dyes – Theory of dyeing – Banned dyes and chemicals – Water quality – Water analysis – Waste water treatment.

DAY VIII
9.30-10.30 AM

10.30-11.30 AM

154

11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Tea Dyeing of cotton – Dyeing of polyester – Dyeing of blends – Wool and Silk dyeing Yarn dyeing Lunch Woven fabric dyeing Knit fabric dyeing Tea Garment dyeing – Washing – Stone washing, acid washing, enzyme washing, bio polishing, bleaching, laser fading and ozone fading - laundering equipment and procedures – garment processing machinery.

DAY IX DAY X
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Industrial Visit – Exposure to Dyeing and Finishing
Finishing of woven fabrics – Finishing of knitted fabrics – Tubular and open-width finishing. Softener finish – Anti-shrink finish – Resin finish – Water proof finish – Fire retardant finish – Anti-bacterial finish. Tea Modern developments in chemical processing State and modernization of textile chemical processing industry Lunch Finishing: Optical brightening, stiffening, softening, crease resistant and crease retentive finish, anti-static finish, antibacterial finish, water proofing, flame proofing, soil release finish, mildew and moth proofing – Stain removal, care labels. Tea Mechanical finishing : raising, sueding, other surface effects. Product evaluation and profiling. Production System - Products and Services – POM functions – Operation Strategies Tea Competitive priorities of textile industry Productivity – Productivity Improvement Lunch Demand Forecasting – Delphi method – Moving Averages – Exponential Smoothing –Simple Regression and Correlation analysis Production Planning and Control in textile industry – Aggregate planning – Master production schedule – Tea Material requirement planning – Bill of material – Capacity requirement planning – Introduction to ERP

DAY XI
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

DAY XII

Practical Training in Garment CAD
155

DAY XIII
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM Inventory Management – Types of Inventory – Cost of Inventory – Fixed Order Quantity Systems – Fixed Order Period Systems Economic Order Quantity – Other Inventory models – ABC in Inventory classification – JIT in manufacturing – Kanban. Tea Manufacturing operations scheduling – Work centers – Work centre scheduling – Sequencing – Priority Rules and Techniques – Shop floor Control – Lunch Facility layout – Process layout – Product layout Line Balancing – Cellular layout Tea Job Design – Considerations in Job design – Work method analysis – Work Measurement – Time study – Work sampling – Work loads in textile manufacturing

DAY XIV DAY XV
9.30-10.30 AM

Practical Training in ERP Software
Determination and Description of Material QualityReceiving and Incoming Quality Inspection , Acceptance Sampling Plans, Vendor process capability; Cost reduction Techniques-Standardisation, Simplification and Variety Reduction; Value Analysis and Engineering Make or Buy Decision, Purchasing Research , Sources of Supply, Price Determination and Negotiation, Vendor Rating, Selection and Development,
Tea

10.30-11.30 AM

11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM

Legal aspects of Purchasing ;Public purchasing and Tendering ; International Purchasing- Procedures and Documentation;
Lunch

Purchasing of Capital equipment-Appraisal Methods, evaluating Supplier’s Efficiency Stores Layout, Classification and Codification; Material Logistics- Warehousing Management, Material Handling : Cases from Textile and Apparel Industry
Tea

4.15-4.30 PM 4.30-5.30 PM

Traffic and Transportation, Disposal of Scrap, Surplus and Obsolete materials; Inventory control of spare parts, Materials Information System.

DAY XVI DAY XVII
9.30-10.30 AM

Out Door activity based learning - Ooty Soft Skills and Management games
Introduction to energy management – need for energy

156

10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

conservation – Demand side management – Energy Consumption of textile machinery – Specific Energy Consumption (UKG) Cost of energy vs. sales value of textile products Tea Energy Conservation in textile industry – Energy conservation in lighting, compressors and boilers – Energy Audit in a textile mill Captive generation and different types of fuels – Non conventional energy Sources – Co-generation Lunch Types of effluents produced by textile industry – Effluent treatment processes Recent developments like Reverse Osmosis – Concept of zero discharge Tea Water quality and test methods – Quality requirement of process water and drinking water – Water Pollution – Effluent standards of pollution control boards – Solid water management Environment pollution and Industrialization – Environment impact assessment and environment management systems – Tea Air Pollution – Air pollution control and equipments in industry – Air quality monitoring Noise pollution Lunch

DAY XVIII
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Introduction to Business Communication – Meaning and significance – Types – Barriers – Principles of effective communication Style of business writing
Tea

Business letters, routine, bad news, sales, collection and application – Memorandum Individual; Presentation on Business topics relevant to Textiles and Apparel-Video Feedback. Group Discussions. Seminars aimed at improving presentation skills.
Tea

DAY XIX
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM

Individual feedback on Scope for improvement to be provided by Faculty and internal assessment components awarded on presentation skills
Lunch Principles of non-verbal communication and their application to clothing styles and body language -

157

3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Speeches, introduction, thanks, occasional and thematic Dialoged communication - Interviews, selection, appraisal, discipline Tea Group communication - Structured and unstructured. Internal and External Communication of an organization Components of organizational communication. Report writing - Structure of reports - Presentation skills Effective use of audio-visual media .Cases from Textiles and Apparel Tea

DAY XX
9.30-10.30 AM 10.30-11.30 AM 11.30 – 11.45 AM 11.45-12.45 PM 12.45 -1.15 PM 1.15-2.15 PM 2.15-3.15 PM 3.15-4.15 PM 4.15-4.30 PM 4.30-5.30 PM

Conducting Meetings – Procedure – Preparing agenda Minutes of meetings – resolutions Conducting seminars and conferences – Procedures of regulating group discussions.
Lunch

Small test / Feedback / Other Discussions
Tea Closing Ceremony Chief Guest

158

6.4 Human Resource Functional area.
\ The Training Module for Human resource Function

1.

Prospecting and Selecting Employees for various positions: HRM – Introduction Business Environment and HR Trends in HR (i) Environmental Scanning Forecasting the demand for employees Analyzing the current supply of Employees Decisions for Human Resource Planning (ii) Human Resource Information System (iii) Job Analysis: Writing Job Descriptions Job Specifications Job Design Sources of Recruitment: Internal and External sources Alternatives for Recruitment Cost Benefit Analysis on Recruiting Selection Process: Screening and Tests Interviews Cost Benefit Analysis on Selection

(iv)

(v)

2.

Laws related to Industrial Relation: (i)The Factories Act (ii) Employee’s State Insurance Act (iii) Workmen’s Compensation Act (iv) Industrial Disputes Act (v)Employees Provident Fund and Miscellaneous Act (vi) Minimum Wages Act

3.

Welfare Measures (i)Statutory (ii) Non Statutory welfare measures

4.

Training and Development

159

(i)Training Need Analysis (ii)Developing the Training Module (iii) Training Calendar (iv) On the job Training and Off the Job Training (v) Training Techniques (vi) Management Development Programmes (vii) Coaching (viii) Mentoring 5. Performance Management: (i) Need for Performance Appraisal (ii) Techniques (iii) Performance Counselling (iv)Performance Interviews. 6. Compensation Management: (i)Factors influencing the Compensation (ii) Pay Decisions – (iii).Pay structures – (iv) Direct and Indirect Compensation – (v) Incentives : Financial and Non financial

Days 1 2 3 4

Titles HRM – Introduction Business Environment and HR Trends in HR Environmental Scanning Forecasting the demand for employees Analyzing the current supply of Employees Decisions for Human Resource Planning Human Resource Information System Job Analysis: Writing Job Descriptions Job Specifications Job Design

5

Sources of Recruitment: Internal and External sources Alternatives for Recruitment

160

Cost Benefit Analysis on Recruiting 6. Selection Process: Screening and Tests Interviews Cost Benefit Analysis on Selection The Factories Act Employee’s State Insurance Act Workmen’s Compensation Act Industrial Disputes Act Employees Provident Fund and Miscellaneous Act

7. 8. 9. 10. 11.

12. 13. 14.

Minimum Wages Act Statutory and Non Statutory welfare measures Training Need Analysis Developing the Training Module Training Calendar On the job Training and Off the Job Training Training Techniques Management Development Programmes Coaching Mentoring Need for Performance Appraisal Techniques Performance Counselling Performance Interviews Factors influencing the Compensation Pay Decisions Pay structures Direct and Indirect Compensation Incentives : Financial and Non financial Case Discussion

15

16 17 18

19. 20

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6.5 Finance functional area
Time DAY I 9.00 – 10.00 AM 10.00 – 11.00 AM 11.15 – 12.15 AM 12.15 – 1.15 PM 1.15 – 2.15 PM 2.15 – 3.15 PM 3.15 – 4.15 PM 4.15 – 4.30 PM 4.30 – 5.30 PM DAY II 9.30 – 10.30 AM Title Registration Inauguration Financial Management-Introduction, Overview and Current practices Introduction to Book Keeping and Accounting– meaning and importance –Distinction between the Book Keeping and Accounting Lunch Detailed discussion on various aspects of accounting the Account - Debit and Credit – rules for debit and credit. The books of accounts - The Journal – The Ledger – The Trial Balance Tea Break The adjusting and closing process: Need for adjusting entries – Types of adjusting entries – closing entries Resource Person Chief Guest

Ruling and Balancing account – Summary of the accounting process – Subsidiary books – Internal controls. 10.30 – 11.30 AM Significant book keeping ideas- discussion with practical examples followed in the industry. 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Introduction to Computerised accounting methods 12.45 – 1.15 PM computers and accounting – need for computerized accounting methods 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM maintaining accounting data base systems- role of computers in accounting 3.15 – 4.15 PM manual accounting – its relationship to computerized accounting - advantages of computerized accounting methods over manual accounting 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM software packages for accounting – significance of accounting softwares
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DAY VII 9.30 – 10.30 AM DAY III

Receivables management- Introduciton- importanceobjectives – cost of credit extension – benefits Practical training on TALLY 1. Company Creation and Alteration 2. Creating and Displaying Ledger 3. Voucher Creation 4. Voucher Alteration and Deletion 5. Inventory Information – Stock Summary Practical training on TALLY 6. Inventory Information – Godown Creation and alteration 7. Final Accounts 8. Bank Reconciliation Statement 9. Accounting and Inventory Information’s 10. Bill wise Statements. Industrial visit to Textile companies – practical exposure to other Accounting Softwares

DAY IV

DAY V DAY VI 9.30 – 10.30 AM

Working Capital Management –Introduction – Concept – Need for working capital – Types of Working capital 10.30 – 11.30 AM Techniques for assessing the working capital requirements 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM sources of finance for working capital – Bank creditAppraisal of working capital by banks – Commercial paper 12.45 – 1.15 PM RBI guidelines on lending for working capital 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM Approaches for determining the working capital financing mix 3.15 – 4.15 PM Issues in managing the Optimum level of Working Capital 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Practical problems of managing working capital – Examples or case study from the industry
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10.30 – 11.30 AM 11.30 – 11.45 AM 11.45 – 12.45 PM 12.45 – 1.15 PM 1.15 – 2.15 PM 2.15 – 3.15 PM 3.15 – 4.15 PM 4.15 – 4.30 PM 4.30 – 5.30 PM DAY VIII DAY IX 9.30 – 10.30 AM 10.30 – 11.30 AM 11.30 – 11.45 AM 11.45 – 12.45 PM 12.45 – 1.15 PM 1.15 – 2.15 PM 2.15 – 3.15 PM 3.15 – 4.15 PM 4.15 – 4.30 PM 4.30 – 5.30 PM DAY X 9.30 – 10.30 AM 10.30 – 11.30 AM

credit policies – Credit terms, Collection policies Tea Break Issues in Receivables Management Inventory Management – Introduciton- importance-objectives classification and coding – cost of holding inventory- inventory models – inventory valuation LUNCH Inventory - classification and coding – cost of holding inventoryinventory models – inventory valuation Issues in Inventory Management TEA Practical problems in receivables and inventory management – examples from the industry Industrial Visit – How industries Manage their Working Capital Cash Management- Introduction – importance Motives for holding cash Objectives of cash management Tea Break - Basic problems in managing cash – Controlling the level of cash – controlling the inflows of cashcontrolling the outflows of cash- optimum investment of surplus cash. Lunch Cash Management models for determining the optimum level of cash balance - Baumol model- Miller –Orr model Practical issues in cash management Tea Break Practical problems of managing cash – Examples or case study from the industry Overview of Banking Services - Definition of banker and customer – Relationships between banker and customer Opening of account – special types of customer Types of deposit – Bank Pass book-Banking regulation Act 1949

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11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM RBI credit control Measure 12.45 – 1.15 PM Managerial functions in banks- Bank deposits accounts- Loans and Advances; 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM Lending practices; Types of advances 3.15 – 4.15 PM Principles of sound bank lending; 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM preparation of reports; credit plans; planning customers; limits of credit; security DAY XI 9.30 – 10.30 AM Negotiable Instruments - Meaning, Types, Cheque, Bills of Exchange and Promissory Notes, Features of Negotiable Instruments -Crossing and Endorsement. 10.30 – 11.30 AM Management of finance: Bank accounts; Records; Reports; 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Statement of advances 12.45 – 1.15 PM Evaluation of loan applications; 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM profit and loss account; balance sheet and statutory reports regarding cash revenue 3.15 – 4.15 PM Practical issues in banking – examples from the industry 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Practical issues in negotiable instruments– examples from the industry DAY XII 9.30 – 10.30 AM Investment Management – introduction- Nature of bank investment; Liquidity and profitability; 10.30 – 11.30 AM preparation of cheques; Book debts; Securities government and commercial. 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Bill of lading; 12.45 – 1.15 PM Other Banking Services- Foreign Exchange Management 1.15 – 2.15 PM Lunch
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2.15 – 3.15 PM Letter of credit. 3.15 – 4.15 PM Purchase and discounting bill 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Traveling cheque, credit card, Teller system DAY XIII 9.30 – 10.30 AM New Modes of Financing 10.30 – 11.30 AM – Leasing as Source of Finance – Forms of leasing 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Leasing- Current practices with examples from the industry 12.45 – 1.15 PM Venture Capital –Dimension Functions – Venture Capital in India. 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM venture capital - Current practices with examples from the industry 3.15 – 4.15 PM Factoring and Forfaiting – Types – Modus Operandi of Factoring – Factoring as Source of Finance Factoring 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Factoring - Current practices with examples from the industry DAY XIV 9.30 – 10.30 AM Securitisation of assets – Mechanics of SecuritisationUtility of Securitisation 10.30 – 11.30 AM Securitisation in India – Current practices 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Banks as Financial Intermediaries. 12.45 – 1.15 PM Role of Commercial Banks Financing/Term lending 1.15 – 2.15 PM Role of IDBI, IFCI, LIC, GIC, UTI 2.15 – 3.15 PM Banks as Mutual Fund and Investment Companies. 3.15 – 4.15 PM Role of banks as issue managers 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Role of banks in corporate restructuring DAY XV 9.30 – 10.30 AM Taxation – Introduction & Overview 10.30 – 11.30 AM Income Tax Act – Definition of Income – Assessment year – Previous Year. 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Assessee – Scope of Income – Charge of Tax –

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12.45 – 1.15 PM 1.15 – 2.15 PM 2.15 – 3.15 PM 3.15 – 4.15 PM 4.15 – 4.30 PM 4.30 – 5.30 PM DAY XVI 9.30 – 10.30 AM 10.30 – 11.30 AM 11.30 – 11.45 AM 11.45 – 12.45 PM 12.45 – 1.15 PM 1.15 – 2.15 PM 2.15 – 3.15 PM 3.15 – 4.15 PM 4.15 – 4.30 PM 4.30 – 5.30 PM DAY XVII 9.30 – 10.30 AM

Residential Status – Exempted Income. Heads of Income: Income from Salaries – Income from House Property -Profit and Gains of Business or Profession Lunch Income from Other Sources. Capital Gains –Introduction and Overview Tea Break Deductions from Gross Total Income – with illustrations Set off and Carry forward of losses Aggregation of Income Tea Break Computation of Tax liability Assessment of Individuals Lunch Practical problems in taxation Tea Break Illustrations from the industry

Special features of Indirect Taxes - Contribution to government revenues - Taxation under the constitution - Advantages and Disadvantages of Indirect Taxes. 10.30 – 11.30 AM Corporate Tax- Introduction and Overview 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Excise- Introduction and Overview 12.45 – 1.15 PM Levy and collection of Excise duty - Kinds of Excise Duty - Basic conditions for liability to Excise 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM Concept of Goods- Excisability and Intermediate Products- Packing, Labelling and branding of goods- Valuation of excisable goods 3.15 – 4.15 PM Registration in Central Excise -Procedure for Registration -Automatic or Deemed Registration. 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Customs – Introduction and Overview
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DAY XVIII

Out bound training – soft skills and personality development

DAY XIX 9.30 – 10.30 AM 10.30 – 11.30 AM

VAT: Terms and Definitions and Overview

VAT System in Tamilnadu – Registration of Dealers – Input and Output Tax – Exempted Sales and Zero Rated Sales – Penalties – Filing of Return 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM VAT as applicable to textile units 12.45 – 1.15 PM Main features of the Service Tax 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM Customs Duty - Different Types of Customs Import Duties 3.15 – 4.15 PM Abatement of duty in Damaged or Deteriorated Goods - Remission on duty on lost, destroyed or abandoned goods 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Customs Tariff Act 1985 - Customs Duty Drawback. DAY XX 9.30 – 10.30 AM Central Sales Tax Act 1956 – Overview-Objectives of the CST 10.30 – 11.30 AM Levy and Collection of CST – Sales and Deemed Sales - Subsequent sales 11.30 – 11.45 AM Tea Break 11.45 – 12.45 PM Practical examples – from the industry 12.45 – 1.15 PM Registration - Compulsory Registration - Voluntary Registration- Security from dealer-registration procedure. 1.15 – 2.15 PM Lunch 2.15 – 3.15 PM Feedback and Other Discussions 3.15 – 4.15 PM 4.15 – 4.30 PM Tea Break 4.30 – 5.30 PM Valedictory Chief Guest

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ANNEXURE: Questionnaire QUESTIONNAIRE FOR UNDERSTANDING THE GAP IN THE KNOWLEDGE LEVEL OF MANAGERS IN THEIR FUNCTIONAL AREAS WORKING IN THE GARMENT INDUSTRY AT TIRUPUR Section – A (Common to all) 1. Name: 2. Designation: 3. Address:

4. Phone no/Mobile no: 5. Qualification/s:

6. Experience (starting with present experience) Organisation Area of responsibility Experience

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7. What is your present functional responsibility? (If you are not assigned with any responsibility related to specific functional area, please mention NIL. You can also add other functional areas, if you are doing anything other than the area mentioned here) Functional Area Responsibility Production Merchandising Production Planning and Sourcing of Materials Human Resources Finance and Costing Any other(Please specify) 8. Do you posses HR management skills to manage labours and other members in the supply chain? Yes No

9. Do you feel the present area of experience and knowledge is sufficient to discharge your responsibilities of your area of function? Yes No

10.Is your technical knowledge upto date? Yes No
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11.If you wish to update your skills, what will be your convenient timings?

Section B (For Functional area -Production) 1. Are you thorough with A. production planning, B.budgeting and costing, C.machinery planning and D. layout 2. Do you know Standard Alerted Minute (SAM)?

3. Are you well versed in quality controlling techniques as well as newly developed fabrics?

4. Are you familiar in Lighting impact, ergonomics and other industrial engineering aspects?

5. Are you aware of Statistical Quality Control and Operations Research?

6. Do you know about Lean Manufacturing? (SECTION C-For Functional area -Merchandising) 1. Are you aware of Propecting, Vendor Evaluation?

2. Are you familiar with Sample and Product Development techniques?
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3. Are you familiar with Printing Dyeing and Washing methods?

4. Are you familiar with sketch studying and Garment Construction methods?

5. Are you familiar with department wise costing details?

6.How good are you in Communication, Interpersonal skills? 7. How familiar are you with fabric consumption details?

(SECTION D-For Functional area –Materials Sourcing) 1. How familiar are you with specification of Fabrics, Geographical availability and Price?

2. How familiar are you with Trims and Accessories-quality parameters?

3. Are you good in interacting with merchandiser for requisition/

4. How familiar are you in Negotiating and communication skills?

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5.How familiar are you with incoming quality inspection, Lot to lot variation of incoming materials/

(SECTION E_For Functional area –Human Resources) 1. How familiar are you with Prospecting and selecting employees for various positions?

2. How familiar are you with the various Laws of Industrial Relations? 3.How familiar are you with the various Welfare measures ? 4. How familiar are you with the procedures of Rewarding employees for Better performance?

5. How familiar are you in measuring performance of Employees?

6.How familiar are you with Training and Development of Employees? (SECTION F-For Functional area –Finance) 1. How familiar are you with Book Keeping Practice?

2. Do you follow a Computerised accounting method?

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3. How familiar are you with working capital Management Practices?

4. How familiar are you with cash Management?

5. How familiar are you with banking Procedures?

6.How familiar are you with various taxation Procedures?

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References
1. Apparel Export Promotion Council (AEPC), various issues, Handbook of export statistics, Ministry of Textiles, Government of India, New Delhi 2. Ministry of Textiles (2006), Report of the Committee to Assess the Requirement of Human Resources in the Textiles sector-Vision 2010 3. Rehman, Atiq ur and Ghulam Ali (2008). A Study of the Skills Gap along the Cotton Value Chain:Garments Segment. Retrieved from http://www.icac.org/tis/regional_networks/documents/asian/papers/ali.pdf 4. National Skill Development Corporation(NSDC),Human Resource and requirements in the textile sector (2022)

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