Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/430969
Title: | Predictive analytics to augment textile and garment industry growth |
Researcher: | Vinodh kumar, S |
Guide(s): | Poonkuzhali, S |
Keywords: | Engineering and Technology Computer Science Computer Science Information Systems textile and garment augment |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | The textile and garment sectors have shown tremendous growth newlineinternationally towards global production of textile and clothing products for newlinethe past decade. Recent world s leading textile exporters are developing newlinecountries, especially those from the Asian region. Out of all Asian regions, newlineIndia holds second rank in the world s manufacture of textiles and garments, newlinewhich have leveraged the country s rapid economic growth. Both the textile newlineand garment industries have also had great impact in terms of direct and newlineindirect income generation in national as well as net foreign exchange newlineearnings. In addition, the wide range of ethnic products, fine blends with newlinefashion trends, abundant software knowledge, and the power to become newlineaccustomed to the fluctuating needs of the world, have made India a favoured newlinedestination for production outsourcing in the textile industry. Unfortunately, newlinethe large quantities of data generated in every dynamic e-commerce or offline newlinebusiness is not available with a single click on available software. Quickly newlineextracting relevant data from various business units, and modelling them with newlinepredictive analytics to make effective decisions using data mining techniques newlinewhich help the textile industry to stay competent with world leaders, is newlineimportant. According to Technopak Advisors, the Indian textiles and apparel newlineindustry has a huge potential to grow with its economy of 223 billion US$ by newline2021. Hence, there is huge potential to integrate IT components for efficient newlinedata analytics in this sector. newline |
Pagination: | xix, 160p. |
URI: | http://hdl.handle.net/10603/430969 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 247.56 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 2.56 MB | Adobe PDF | View/Open | |
03_content.pdf | 13.75 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 12.97 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 51.58 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 115.9 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 43.98 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 762.65 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 845.42 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 880.79 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 490.42 kB | Adobe PDF | View/Open | |
12_annexures.pdf | 151.33 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 104.11 kB | Adobe PDF | View/Open |
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