Textile and Fashion

Market Outlook

Data science is continuously changing the fashion industry by gathering data, processing it, identifying it, and analyzing it to turn it into information, suggesting actionable insights.

AI can access and collect historical and operational information in real-time and provide insight that can enhance operational efficiency. AI is also being used to develop more efficient manufacturing processes. By using AI to analyze data from manufacturing plants, engineers can identify ways to improve efficiency and reduce waste.

Use-Cases

Fabric Inspection:

AI uses artificial neural network technology to make it easier to spot defects in models like weaving and knitting. An inspection that is AI-enabled can reduce human mistakes and hence improve efficiency. Inspection of fabric and fabric patterns facilitated by AI speeds up fabrication by decreasing pattern faults with the lowest workload and maximum accuracy.

Predictive Analytics:

AI analyses market performance on attribute level for each product. Business users can get insights regarding not only the products that are performing well but also on detailed attributes like colour, prints, sleeves, necklines and more. AI provides real-time data to observe shifting trends and stock performance as they are happening.

Trend Forecasting:

AI collects, analyses, and interprets data from social media, e-commerce platforms and other data sources to spot future fashion trends for each product category. This information is then combined with the data on past performance and customer behavior to manufacture and market a variety of products that would resonate best with a retailer’s consumer base.

Competitor Analysis:

Monitoring competitor pricing, AI recommends ideal price points to optimize revenue by gaining a competitive advantage. Retailers can spot the best seasonal timing to keep lower prices while retaining minimal margin and when to slightly increase prices to maximize profitability.

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Fabric Inspection:

AI uses artificial neural network technology to make it easier to spot defects in models like weaving and knitting. An inspection that is AI-enabled can reduce human mistakes and hence improve efficiency. Inspection of fabric and fabric patterns facilitated by AI speeds up fabrication by decreasing pattern faults with the lowest workload and maximum accuracy.

Predictive Analytics:

AI analyses market performance on attribute level for each product. Business users can get insights regarding not only the products that are performing well but also on detailed attributes like colour, prints, sleeves, necklines and more. AI provides real-time data to observe shifting trends and stock performance as they are happening.

Trend Forecasting:

AI collects, analyses, and interprets data from social media, e-commerce platforms and other data sources to spot future fashion trends for each product category. This information is then combined with the data on past performance and customer behavior to manufacture and market a variety of products that would resonate best with a retailer’s consumer base.

Competitor Analysis:

Monitoring competitor pricing, AI recommends ideal price points to optimize revenue by gaining a competitive advantage. Retailers can spot the best seasonal timing to keep lower prices while retaining minimal margin and when to slightly increase prices to maximize profitability.

Benefits

Targeted Marketing

Operational efficiency

Better customer insights

Enhanced Customer experience

Project Summary

Problem

Global Fashion Brand’s quality inspection of garments entails checking for defects in the garment manually by a quality inspector. If defects are found in a finished garment, the defect must be logged manually in the quality inspection mobile application. The defect logging process takes time to complete an audit which leads to inefficiency.

Solution

A computer vision model was built to identify the type of defect and highlight the defected area on the garment. The model is integrated within the quality inspection mobile application. The model has automated the defect logging process by detecting the defect automatically and prefilling existing fields based on output from the model.

Results

This model has reduced the defect logging process by half i.e., if one audit logging process took 60 seconds, it will now take 30 seconds.

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