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Artificial Intelligence - Biggest Revolution in the Apparel Industry ?

May 01, 2019 Pooja Bajaj
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Over the years fast fashion has been greatly influencing the designing of merchandise and AI plays a significant role in helping the designers to style merchandise faster, based on the current trends.

What is Fast Fashion Clothing?

Fast fashion clothing collections are inspired from the latest collection presented at Fashion Week in both the spring and the autumn of every year. The idea is to design and quickly manufacture the styles based on latest trends with emphasis being on optimizing certain aspects of the supply chain so that the consumer can buy current clothing styles at a lower cost. Large retailers such as H&M, Zara, Peacocks, Primark, Xcel Brands, and Topshop have been using this concept of quick manufacturing at an affordable cost. This has developed from a product-driven idea based on a manufacturing model to a market-based model of "fast fashion”.

Fast fashion brands can offer larger varieties of clothing styles with the ability to bring the latest styles on shop shelves in time, also catering to the likes of smaller, more targeted segments of consumers. They could also push pilot runs to test the waters for consumer demand or sell collections for hyper-short lifespans.

There is a change in the fashion seasons/ fashion cycle. Some fast fashion brands have around 52 micro-seasons per year. For example, Topshop introduces approximately 400 styles per week on its website and  traditional apparel brands have around 11 seasons a year.

Cheap alternatives to high-fashion clothing stay hot client commodities. Even amid the retail holdup, Zara's parent company, Spanish retail giant Inditex saw a 3% rise in net sales in FY 2018 topping €26.14 billion for the first time. As a result of which, the net profit rose by 2% (€3.44 billion).

Fast fashion does have its downside. Brands manufacture low-cost, low-quality apparel in factories with poor working conditions, relying on workers who receive low wages. The materials used to create cheap garments are not only inexpensive but also laden with chemicals. This cheaply made apparel harms both factory workers and the environment. According to the Environmental Protection Agency, some 12.8M tons of clothing is being sent to landfills annually, the inexpensive materials used in production are also laden with chemicals.

The demand for hyper-cheap fashions is rapidly increasing thanks to marketing and other social media strategies which help new trends travel fast. Platforms like Instagram and Pinterest which display products help shoppers to take quick buying decisions. Consumers are realizing the negatives of fast fashion and socially conscious shoppers are going in for slow fashion, which focuses on sustainable materials and transparent, ethical labor and manufacturing.

But even as the slow fashion movement gains momentum, the rise of social media and the fast-fashion model have transformed fashion as we know it.

Rapid Iteration and Production

Technology and e-commerce have significantly reduced the costs of starting a fashion.

Etsy's entry in the e-commerce market made it easy for anyone to start an online shop and build a following. Due to the decreased production costs, small or emerging brands could manufacture small runs of products at reasonable margins and build up online audiences from there.

A few years back, fashion labels had to manufacture hundreds or thousands of products in order to produce them at a reasonable price. However today, startups have made it easy for small labels to find small-batch manufacturing partners which will meet their needs at scale, with transparent standards around pricing and sourcing. Emerging brands can introduce small-batch runs (and transparent production standards) into their marketing. Large high-end brands are also rethinking their approach to production to be able to compete with fast fashion retailers.

Tommy Hilfiger makes the fashions in its new Tommy Now line available instantly, all around the world, in-store and online, as soon as they are showcased on the runway. So Tommy Now items hit stores three times faster than traditional collections, with just a 6-month window between product ideation and release.

According to the McKinsey Global Fashion Index, the fashion industry has grown at 5.5% annually in the past decade and in 2016 it was estimated to be worth $2.4 trillion. McKinsey further forecasts growth of 3.5 to 4.5 percent in 2019, slightly below 2018 growth which was predicted at 4 to 5 percent .

The demand for the latest trends is increasing rapidly and the turnaround time in the fashion retail space is shrinking. Consumers are demanding the latest styles in double quick time and because of the rate at which trends change, the speed of delivery has become important to retailers. This is possible only through the automation of the Apparel industry from the manufacturing side which involves the application of Artificial intelligence and also the use of Robots in garment manufacturing to handle repetitive tasks and to increase the speed, quality and efficiency of production.

Automation in the apparel manufacturing field started more than 2 decades back.

•          Initial automation started in the pattern department by the entry of Computer-aided designing (CAD) software, plotters and pattern cutters for product design – this helped in streamlining the pattern and fabric design department.

•          Cutting room automation started with the help of cut planning software and computer-aided manufacturing (CAM) machines like semi-automatic to fully automatic spreading and cutting machines - this helped in streamlining the cutting room department.

The next phase of fashion is all based on personal preferences and prediction. With more and more data, algorithms will be the trend hunters — predicting (and designing) for the next fashion cycle.

Since World War II, fashion has officially been broken up into seasons: spring/summer lines debut on runways in early fall, and autumn/winter lines debut in February.

The staggered timeline was designed to help brands to gauge the interest of retail buyers and customers. Brands assess demand during the time, when fashions are introduced and when they arrive on store shelves so that they can manufacture the right number of garments for the season.

But because of fast fashion and rapid iteration in the product range, most brands/retailers of seasonal fashion products have more than 6 selling seasons per year.

Garment manufacturing is labor-intensive, which is characterized by

•          Low-fixed capital investment

•          A wide range of product designs and input materials

•          Variable production volumes

•          High competitiveness, and

•          High demand for product quality.

Two decades back, we could only have imagined that raw material is fed in a machine and we get a final finished garment out. Today, we look at this possibility coming to a reality, as most of the manufacturing operations from raw material to final garment stage are automated, thanks to the latest technology. Due to its labour intensive nature, the garment industry can seek great benefits out of the AI intervention in their businesses.

The quality of a garment depends on several factors related to the manufacturing of yarn, preparation of fabric (weaving and knitting), dyeing/printing and processing, and garment manufacturing. The use of AI can help to control the tasks involved in the garment manufacturing process more efficiently for decision making, cut order planning, marker making, production planning, supply chain management (SCM), and retailing.

For example, ThreadSol’s Artificial Intelligence-based solution intelloCut helps garment manufactures in effective fabric planning and automation to boost their top-line.
Here is how it does that:

1. For every order, intelloCut generates an ANN (artificial neural network) based cutplan.

2. This ANN-based cutplan ensures product/customer based tailored cutplan to reduce consumption while ensuring its practical on-floor execution.

3. The ANN-based cutplan is self-iterative and gets better day by day with user input.

Want to know more about intelloCut?  click here


Artificial Intelligence And Automation In Garment Industry

What is Artificial Intelligence (AI)?

AI is a broader field of computer science dealing with emulating the human mind and reasoning. It focuses on training machines to use algorithms that discover patterns and generate insights and make predictions based on already existing data.

AI has been gaining momentum over the last two decades, in the apparel industry in different areas. The automation of various departments by the application of AI in spreading, cutting, sewing, and material handling can reduce the production cost and minimize faults.

Several types of research have been conducted to implement AI which can help to develop basic clothing patterns automatically. For example, Inui had developed an AI integrated CAD system (combination apparel CAD and GA) which uses search engines to find the preferred apparel designs.

Garment manufacturing uses CAD systems for creating designs, pattern making, grading and marker making operations. Researchers have made several attempts to integrate AI with CAD systems to generate designs automatically. To create an appropriate pattern design for different clothing styles, experienced designers are needed. The AI system can be used to utilize the knowledge of experienced designers.

Why the need to adopt AI in Garment Industry?


  • It can be used for designing of garments by fabric engineering and monitoring the garment manufacturing processes.

  • It can be used to predict the best cut order planning ratios for marker making.

  • It can be used to find the sew-ability of different fabrics during garment production.

The mechanical properties of the fabric affect their performance during spreading, cutting, and sewing. AI can predict the performance properties of the seam which are evaluated by seam puckering, seam slippage, and yarn severance.


About ThreadSol

“Apart from the aim of saving fabric and reducing wastage, ThreadSol solutions minimize human intervention by automating the planning processes. They also reduce the manual effort of data entry and data aggregation,” says Mausmi Ambastha, co-founder at ThreadSol.

ThreadSol, a tech start-up firm, offers
intelloCut its flagship product which uses ANN to predict the best cutting ratios for the cutting room. It generates detailed reports which can be used to analyze the factory data and make informed and intelligent decisions.

IntelloBuy is used to calculate the actual fabric that would be needed to complete an order. AI can be used for accurate fabric buying, so that when the buyer confirms the order, the garment manufacturer can order the right amount of fabric, thus saving on excess or shortfall of fabric. It helps in automating the task of marker order creation, which saves time and effort for the marker maker.

The latest product offering by ThreadSol is intello3c, a complete cost control tool. Its AI helps buyers in negotiating the cost of a garment making with their vendors, to arrive at much more competitive pricing from their vendors. As the buyer can clearly see the quotes from various vendors, they can create their own cross cost sheet template, to negotiate and get the best prices from the vendors. The strong AI helps in controlling the costs of garment production for the buyers.



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