International Journal of Engineering Technology & Management Research
Journal homepage: www.ijetmr.org
Abstract–India‟s textile industry has shown a remarkable dynamism in terms of growth, development and export performance during the last few. By doing so environmental issues had been overlooked by textile processors all these years. However, due to increased awareness of the polluting nature of the textile effluent, social pressures are increasing on the textile processing units. Globalization of textile market has set new miles for competitiveness. Many more “restrictions or bans” are being imposed by buyers. In addition to these, a health hazard due to pollution has become headache to the processors. Certain foreign buyers are insisting upon processing of textiles in such a way that not only their environment or health but our environment and health also should not be affected. text ile manufacturing has a huge environmental footprint. It pollutes as much as 200 tons of water per ton of fabric, uses a suite of harmful chemicals, and consumes tremendous amounts of energy for steam and hot water needed in dyeing and finishing processes.
1. INTRODUCTION
The Trend of developing a green supply chain is gaining immense popularity now, and businesses in textile industry are focusing more on improving their supply chain visibility, efficiency and costs. A flawed supply chain creates pollution, threatening the existence of life on earth. With increasing awareness towards environmental issues, and global warming, consumers are now more focused towards the products they are buying. In the Textile industry, there have been various initiatives taken by the companies to implement Green supply chain management. It has been done in stages of procurement, processing, selling & reuses [16].
1.1 Indian Textile Industries
The textile industry in India is one of the oldest manufacturing sectors in the country and is currently it‟s largest. The Textile industry occupies an important place in the Economy of the country because of its contribution to the industrial output, employment generation and foreign exchange earnings. The textile industry encompasses a range of industrial units, which use a wide variety of natural and
Green Supply Chain Management Practices in Textile and
Apparel Industries: Research Methodology
Neha Nema
M. Tech Student,
Department of Mechanical Engineering Takshshila Institute of Engineering & Technology
Jabalpur (M.P.) [INDIA]
Mr. Sourabh Nougriaya
M. Tech Student,
Department of Mechanical Engineering Takshshila Institute of Engineering & Technology
Jabalpur (M.P.) [INDIA]
Mr. S.R. Soni
Associate Professor,
Department of Mechanical Engineering Takshshila Institute of Engineering & Technology
Jabalpur (M.P.) [INDIA]
Mr. Amol Talankar
HOD-Mechanical Engineering
Takshshila Institute of Engineering & Technology Jabalpur (M.P.) [INDIA]
synthetic fibres to produce fabrics. The textile industry can be broadly classified into two categories, the organized mill sector and the unorganized mill sector. Considering the significance and contribution of textile sector in national economy, initiative and efforts are being made to take urgent and adequate steps to attract investment and encourage wide spread development and growth in this sector. Figure 1 shows the structure of Indian textiles industries [14].
1.2 Concept of Go Green
No one was concerned about the environment earlier. To check the pollution hazard, the concern for environmental protection was introduced. It was just concerned to the concept of clean air and safe drinking water, although, the current decade has seen a huge deviation from the earlier environmental protection notion. At the pace with which the world is developing and numerous changes are surfacing, in terms of market, industries, consumers, technology etc, the whole eco-system is endangered. Corrective actions are the need of the hour and hence the concept of green has been applied to each and every field. From just cleaner air and safe drinking water, environmentalists have moved to hygienic ecosystems, safe food, toxin-free communities, safe waste management, and the refurbishment of the contaminated sites. The world is witnessing a revolution that is the green revolution. The major corporate houses and society today, with an objective of paying their dues back to the environment, are making great efforts to capture a socially and environmentally responsible corporate image.
The concern of “green‟ has arisen from many factors ranging from negative environmental impact, the growing interest of public opinion, the pressure groups and government regulations as well as the realization of achieving greater economic
performance by achieving greater efficiency through green practices .
Fig. 1.1 Green Supply Chain Management – Performance Measurement System Flow and Pressures (Aref a.hervani and marilyn m, (2005))
2 PROBLEM DEFINITION AND
OBJECTIVES OF THE STUDY
2.1 Problem Statement
GSCM is new to the textile and apparel industries, and in its processes, following problems need to be resolved for the full implementation of green supply chain toward environment:
Indian companies have more hesitations to adopt GSCM in practice.
Companies or industries have no proper way for disposal of waste materials.
Traditionally, purchasing and distribution function of organizations were regarded as supporting activities with limited part to play in achieving strategic goals and objectives. However, in the present era these activities are of utmost concern for managers as they help in the environmental performance of the company and improve the corporate image. This is also a reason why environmental aspects have been included in the strategic mission and goals of companies. Various questions have been raised with regard to the Green Supply Chain and its relation to improving the economic performance of the company. The basic questions that has been consistently tried to be answered are:-
1. How does green purchasing change the environmental performance of the firms in a supply chain/ network?
2. Do such changes contribute to companies‟ overall environmental performance and to sustainability? Large buyers are trying to evaluate this through rating their suppliers on environmental performance and then using this rating as a base to measure their environmental pressure. This ultimately provides them their own rating. Some of the questions that are being undertaken for suppliers are as follows:-
1. What environmental claims the company makes for its packaging? 2. Whether the supplier, in turn, audits /
reviews its own suppliers?
3. Whether the company takes an interest in the products it imports from underdeveloped countries? 4. Whether the company‟s products
contained timber.
5. Whether the company uses hazardous chemicals in its products or processes?
2.2 Objectives of Study
From the available literature it has been observed that the GSCM study is not a regular practice. So the concept of GSCM is not common in apparel industries in Indian context. Therefore a study has been made in this regard. The purpose of this study is to study how apparel industries practice GSCM and what dynamics of the industry are changed by the impact of GSCM. The aim of this study is to examine industries readiness for GSCM adoption in India. With the background information, the primary objective of the project to find out green supply chain management: drivers, practices, performance evaluation and environmental performance and how green supply chain management can be implemented in Indian textile industry companies for the potential improvement
of sustainable development. The specific objectives are as follows:
1. Study of Green Supply Chain Management Practices In textile and Apparel industries.
2. To find companies interest in participating in some level of Green Supply Chain Management of textile and apparel industries.
3. To study how and how much, waste ma t e r ia ls a r e ha r mfu l fo r environmental issues.
3. RESEARCH METHODOLOGY
The objective of this research is to identify the reasons for a company to adopt Green Supply Chain Management and the benefits that justify the introduction of Green Supply Chain Management into a market niche. As to the method used, this is qualitative research with an exploratory focus that relies for this case.
3.1 Data Collection and Respondent Profile
The first data collection phase included meeting attendance as to frame the area of concern. Analysis of meeting notes generated five areas of significant interest (development methods, organization, d ig it iz at io n, d iffere nt iat io n, a nd architecture). The data collection of the second phase mainly included recorded and transcribed semi-structured interviews. The interviews were based on an interview template developed on the basis of the themes identified in the first phase. The third phase was confirmatory in character. After completing the first two phases, process charts of the software development and differentiat ion process were developed. Data collection will do by questionnaire; questionnaire will be filling by medium level managers and staff of the apparel industry.
The data were collected from different cities (Indore, Bhopal, Nagpur, etc.) in
India. Totally 116 agreed to participate and completed the questionnaire, which represents a response rate of approximately 100 percent.
3.2 Questionnaire Design
The questionnaire consisted in the main of self-rated, non-comparative single-item rating scales used to assess respondents‟ level of agreement or disagreement with statements relating to the benefits and disadvantages of green supply chain, to their satisfaction with standard features and to the difficulty of choice between many alternative models. All items in the questionnaire are extracted from previous literature. We also include three control variables in our analysis – city, industry type, and plant size. This questionnaire is based on 5 points Likert scale. Questionnaire is designed on the bases of previous literature and study related to GSCM; through these questions we also find unpredictable demand of products and how waste materials influence consumer‟s and environmental issues.
3.3 Analysis Methods
SPSS software was designed to perform statistical analysis on quantitative data. In other words SPSS software is used for complex calculations to analyze numerical data. SPSS software is used in nonprofits agencies, educational institutions and even in business to analyze numerical data. It performs functions such as factor analysis, regression, which is a form of predictive calculation used to determine the relative effect of a single factor on a situation. 3.4 Data Collection
Research provides a framework to understand the impact of time based manufacturing practices and way to attain GSCM and value to the customer. Data were collected from small, medium and large manufacturing firms of various sizes and location to test the relationships in the framework. The study indicates that firms with high levels of time based
manufacturing practices have high level of GSCM and value to customer. For the study of GSCM in apparel and textile industries data was collected from different apparel and textile industries locate at different cities (Indore, Dewas, Bhopal, Nagpur, Janjgir, etc.) of India. I have conduct some personal meetings and interviews with managers, engineers and other workers for knowing about plant, manufacturing processes, machines, technologies, etc. About 150 copies of questionnaire I sent to respondents 40 questionnaire sent through email but only 6 respondent give reply this is the 15%; 100 questionnaire filled by respondent in meetings and in interviews; and 10 questionnaire filled by respondent with the help of friends support. This questionnaire only for the knowing in future GSCM will adopt by industries or not. These all companies are major integrated textile and apparel producer in India. These all produces different types of yarn, fabrics, processed fabric, clothes and many other products. We want to use sample size one ratio five (1:5) that means 100 because my questionnaire have 20 questions. According to this ratio; ratio of 20 questions are 100. It is sufficient for analysis of my objectives but I recieve 116 responces this is also more than enough to calculate factor analysis, reliability, regression, and correlation in SPSS. For checking readyness of companies to adapting GSCM more responces are needed.
3.5 Pie Charts
First chart (fig. 4.1) shows the types of organization (small, medium, and large). Size of organization is classified according to the number of employees working in industries. For less than 100 employees organization will be small, for greater than 100 and less than 500 organizations will be medium and for more than 500 employees industry will be large size. We visit 11 textiles and apparel industries in India in which only 2 companies are small that is
18% of total; 6 companies are medium that is the 55% of all and 3 companies are large size that is the 27 % of all visiting companies.
Figure – 4.1 Sizes of Organizations
Second chart (fig. 4.2) shows the job position of respondents (lower, middle and high level). All supervisors, line in charge, shift in charge and other workers are included in lower level, assistant manager and managers are included in middle level and general manager and owners are included in high level respondents. We take total 116 responses from different industries in which 100 respondents are fall in lower level this is the 86%; 15 respondents are in middle level this is 13%, and only 1 respondent is in higher level this is 1% of total responses.
Figure – 4.2 Job Positions of Respondents
Third chart (fig. 4.3) shows the experience of respondents in years (0-10; 11-20; 21-30). There are 73 respondents have 0-10 years working experience this is the 63%, 34 respondents have 11-20 years experience this is 29% and only 9 respondents have 11-30 years working experience this is 8% of total responses.
Figure – 4.3 Experiences of Respondents
Fourth chart (fig. 4.4) shows the age of respondents in years (20-30; 31-40; 41-50; 51-60). There are 58 respondents are fall in 20-30 years category this is 50%, 36 respondents are fall in 31-40 years category this is 31%, 20 respondents are in 41-50 years this is the 17%, and only 2 respondents are in 51-60 years age group this is the 2% of the total responses.
Figure – 4.4 Ages of Respondents
4. ANALYSIS OF DATA
The first step is description of research; according to questionnaire we take all important information about company, customer and market. These all data help us to find the readiness of textile companies for adopting GSCM. Second step is a visual examination of the factor analysis identifying those that are statistically significant. We used Varimax r o t a t io n m e t h o d w i t h K a i s e r Normalization. Through SPSS we apply KMO & Bartlett‟s test for sampling adequacy and sphericity respectively. Result of KMO is 0.651 that is acceptable and in Bartlett‟s result value of chi- square is 567.760, value of freedom (df) is 190, and significance is 0; these all values are small 18% midium 55% large 27% Size of Organization Lower 86% Middle 13% High 1%
Job Position of Respondents
0 to 10 63% 11 to 20 29% 21 to 30 8%
Experience of Respondents in Years
20 to 30 50% 31 to 40 31% 41 to 50 17% 51 to 60 2%
considerable. These all calculation completed in 6 iterations. Take all 20 variables for analysis and then find that this analysis provides 4 variances according to SPSS software; theoretically we also make 4 groups of variables
4.1 Factor Analysis
Factor analysis is the first multivariate technique because it can play a unique role in the application of other multivariate technique. Increasing the number of variables also increases the possibility that the variables are not all uncorrelated and representative of distinct concepts. Finally, if the number of variables is too large or there is a need to better represent a smaller number of concepts rather than the many facets, factor analysis can assist in selecting a representative subset of variables or even creating new variables as replacements for the original variables while still retaining their original character.
Table-5.1 Descriptive Statistics
Variables Mean Std. De-viation Analy sis N Product demand is UP (01) 1.6983 0.67528 116 GSCM eco design related SC o/p (02) 1.4224 0.54615 116 Sales is affected by EC (03) 3.2155 0.93999 116
You battle for MS (04) 1.4655 0.58132 116 PT is changing rapidly (05) 3.5517 1.13708 116 Your PDC is short (06) 2.8793 0.86633 116 GSCM is related to SCR (07) 1.8448 0.66729 116
You are involved with GCSC (08) 1.5345 0.53456 116 evaluation process includes CF (09) 3.0431 1.42885 116 GSC reduce the cost (10) 1.8103 1.00359 116 favor suppliers that have GBP 2.1897 0.70912 116 suppliers advertize their GC (12) 4.0000 1.01296 116
at least one person 1.0776 0.26868 116
effects on Improve BI (14) 4.3707 0.81859 116 GSCM generate new BO (15) 2.5172 1.34153 116 PC are automated (16) 2.0776 0.83563 116 company can go with GSCM (17) 1.8966 0.73873 116 Industry is based on NFT (18) 2.2414 0.75320 116 Ability to CWC (19) 2.0259 0.84916 116 Customers are able to know AP (20) 1.3966 0.50870 116
Legends: UP unpredictable, SC O/P supply chain output, EC economic cycles, MS market share, PT product technology, PDC product development cycle, SCR supply chain resource, GCSC greening company supply chain, CF carbon footprint, GSC greening supply chain, GBP green business practices, GC green credentials, RGI responsible for green initiatives, BI brand image, BO business opportunities, PC production capabilities, NFT new flexible technology, CWC communicate with customers, AP about products.
Table – 5.2 KMO and Bartlett's Test
RESULTS
1. Factor Analysis: - Questionnaire is divided in 4 groups with the help of SPSS soft ware; these groups are general information about company, market capabilities, technological capabilities, environmental preferences. Before using SPSS we already divide this questionnaire in same 4 groups; but few variables are different that means our factor analysis is correct.
2. Reliability Analysis: - After factor analysis check the reliability of all individual groups of variables and finally reliability of total number of variables. In reliability analysis values of Cronbach‟s Alpha for all 4 groups and all variables lie between 0.6 to 0.7 according to book of “Multivariate Data Analysis” (Hair and Anderson) this value is considerable.
3. Regression Analysis and Correlation: - In regression analysis ANOVA table
shows the correct regression. Correlation among all 4 groups shows the values between -1 to +1. In this test “-1” shows the perfect negative relation, “0” shows no relation and +1 shows perfect positive relation among all variables.
All hypotheses have perfect positive relationship on the basis of exist literature
REFERENCES :
[1] Beamon B.M(1999), "Designing the Green Supply Chain" ,Logistics information Management. Vol-12 – No.4
[2] Cristopher P.H. and Light B., (1999), “Global Enterprise Resource Planning Implementation”, Hawaii International Conference on System Science.
[3] Diane Mollenkopf, Hannah Stolze, Wendy L.Tate and Monique Ueltschy, (2010), “Green, lean, and global supply chains”, International Journal of Physical Distribution & Logistics Management. Vol - 40 No - ½.
[4] Fengfei Zhou, Tianjin Polytechnic University, Tianjin 300160, China ( 2 0 0 9 ) , “ S t u d y o n t h e Implementation of Green Supply Chain Management in Textile Enterprises”, Vol2.No.1.
[5] Giovanni Azzone and Giuliano Noci, (1998), “Seeing ecology and “green” innovations as a source of change”, of Organizational Change Management, Vol - 11 No - 2, 1998. MCB University.
[6] Jiuh-Biing, Sheu, Yi-Hwa, Chou & Chun-Chia Hu., (2005), “An integrated logistics operational model for green-supply chain ma nageme nt ” Transpo rtat io n. Research Part E41. P.287-313. [7] K.Abu Seman,Norhayati Zakuan,
Mohd Shoki (2012) "GREEN S U P P L Y C H A I N MANAGEMENT" Vol-3, No. 1 Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .651 Bartlett's Test of Sphericity Approx. Chi-Square 567.760 Df 190 Sig. .000
[8] Marilyn M. Helms, Joseph Sarkis, ( 2 0 0 5 ) , “ Benchmarking” an International Journal. Vol – 12 - No. 4, Emerald Group Publishing Limited.
[9] Messelbeck, J. and Whaley M., (1999) „Greening the health care supply chain: triggers of change, models for success‟ Corporate Environmental Strategy. 6/1: pp. 39-45.
[10] Zhu, Q. and Sarkis, J. (2006) „An inter-sectoral comparison of green supply chain management in China: Drivers and practices‟ Journal of Cleaner Production. Vol 14: pp. 71-74.
[11] Zhu, Q. and Sarkis J. (2004) „Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises‟ Journal of Operations Management, Vol 22: pp. 265-289.
[12] Zhou Fengfei (2009) “Study on the Implementation of Green Supply Chain Management in Textile Enterprises‟ journal of sustainable development.
[13] Hair, Anderson, Tatham and Black, (2005), “Mult ivar iate Data Analysis”, 5t h
Ed., Pearson Education.
[14] Hollen N and Saddler Jane, (1979), “Textiles”, 5th
Ed., Macmillan Publishing Company, Inc. New York.
[15] Chopra and Mendil, (2003), “Supply Chain Management”, Pearson Education. [16] www.fibre2fashion.com [17] www.na-businesspress.com [18] www. isca.in [19] www.shodhganga.inflibnet.ac.in [20] www.sciencedirect.com [21] www.mhprofessional.com [22] www.indiantextilejournal.com