Fascinating Thesis Brings AI to the Clothing Industry

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Companies that adopt AI in the next few years will have a competitive advantage. AI could improve supply chain efficiency and provide greener consumption.

Customer needs evolve quickly, and nowhere is that more pressing than in clothing. The industry is looking to technology to improve customer experience and manage margins. A new thesis from Sheenam Jain, a doctoral student in the SMDTex program, is exploring the intersection of artificial intelligence and the clothing industry.

See also: Textile Leader Embraces Blockchain for Supply Chain Transparency

The complexity of clothing

Jain quickly realized that the solution would take more than four years and a single researcher to solve. Clothing is a complex system with supply chains, inventory systems, customer trends. The clothing industry has struggled with all these things in recent years due to changing eCommerce methods.

The purpose, in the beginning, was to unite data from clothing to allow for greater insights. This research could enable clothing companies to personalize clothing with greater efficiency. These models and data sources would transform the industry.

The role of AI in the industry

Jain refocused her thesis to focus on how important the development of AI systems will be to clothing managing the coming changes. Companies that adopt AI in the next few years will have a competitive advantage. In addition to advantage, AI could improve efficiency and provide greener consumption.

She developed three different models designed to solve a pain point for clothing industry members and deliver deep insights manufacturers and sellers need:

  • Clothing classification framework: Four algorithms were put through the paces to find out which one worked best. The winner? Random forest.
  • Decision support system: To help manufacturers choose the right fabrics by using a “fuzzy logic technique.” It’s designed to work with product configurators or to support custom clothing.
  • Data management for personalization: The model provides a management system that opens up the possibility of greater personalization without overloading the system.

Sheenam Jain conducted interdisciplinary research in three areas — automation, textile management, and textile engineering. Her research used a variety of sources offering a united look at the data across the industry.

A promising start for the clothing industry

In the future, she hopes to work in management and technology, “analytical fashions or circular fashion,” as she said. The algorithms could be used in the future to provide value based on previously disparate data and allow retailers and manufacturers to personalize, reduce waste, and improve margins.

Elizabeth Wallace

About Elizabeth Wallace

Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do.

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