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Why does the Fashion Industry need Artificial Intelligence?

June 17, 2019 Pooja Bajaj
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The changing face of the Fashion and Apparel Industry

Fashion brands all across the world are going digital and making use of technology to leverage their productivity and sales, owing to the huge competition in the fashion and apparel sector and the emergence of fast fashion.

  • If we look back in time, there was not much diversity in the fashion industry – lesser collections per year, exposure to lesser number of geographies and limitation to only the domestic market. The customers were more loyal to the brands or the stores they shopped their clothes from.
     
  • However, today’s industry is much more complex. The advent of fast fashion has changed many things in the fashion industry.

Fast fashion clothing collections are based on the most recent fashion trends presented at Fashion Week in both the spring and autumn of every year. The emphasis is on optimizing certain aspects of the supply chain for these trends to be designed and manufactured quickly and inexpensively to allow the mainstream consumer to buy current clothing styles at a lower price. This philosophy of quick manufacturing at an affordable price is used by large fashion retailers such as H&M, Zara, Peacocks, Primark, Xcel Brands, and Topshop. It particularly came to the fore during the vogue for "boho chic" in the mid-2000s.


The traditional approach used by fashion retailers
 

The fashion retailers in the past used the below process to forecast, design, develop and introduce new products in the market.

•  They would send designers and merchants around the globe in search of new fashions

•  Designs were selected 6-9 months in advance based on anticipated consumer trends

•  A retailer would make a large commitment to the products merchants and planners

•  They would launch new products and let the sales data determine how well the whole process worked
 

The current approach used by fashion retailers
 

The approach currently used by fashion retailers is different.

•  The development cycle is reduced

•  There is more variety in the collections - capsule collections, pre-collections, resort collections, etc.

Today’s consumer is more connected and aware of market trends. Hence, in today’s highly competitive retail environment, regardless of approach, it’s critical to know how consumers will react to products before they are launched. Some decades back when everything was manual this was done through actual physical surveys, by offering a free product to a consumer and asking them to fill up a survey form to get their feedback. However, this method was restricted to very small sample size. We cannot follow this method for a huge sample size.


Is it possible to manually track the data of thousands of consumers and their feedback?
 

Well, it is impractical today to manually track data considering the volume of styles and collections generated.

Digitalization is required to bridge the gap between the consumer and the fast-moving fashion sector, where good designing, styling, variety, good quality, on-time delivery, and competitive pricing are requisite to boosting the sales.

The use of technology will help in the following ways:

1. Analyzing huge volumes of data in a matter of minutes

2. Forecast current and future trends based on consumer feedback

3. Improve current strategies

4. Save time

6. Improve efficiency and Increase productivity and

7. Reduce the errors involved in the manual data processing.

There are too many retailers, brands, designers, discount stores and the predator e-commerce platforms offering huge discounts. This puts a huge pressure on the fashion retailers and brands to rethink their selling strategies and use technology which can help them to be at par with their competition.

What could be the possible reasons for the delay in digitalization of the apparel industry?

Let’s look at the reasons which are responsible for this delay.

A. Fear of change
This is one of the biggest deterrents as garment makers are reluctant to change their current way of working, as it involves time and energy to effectively implement any new technology.

B. Fear of data security
Data security concerns losing important data to hackers, data leakage, etc

C. Fear of losing control
Fear of losing flexibility and control on the current processes and aligning or integrating them in the software

D. Fear of high investment
Implementing any software involves a high initial investment, which is also one of the reasons companies are reluctant in adopting new technology
 

Current challenges in the product development cycle
 

•  Impact on product development - we need an accelerated cycle; new collections need to be developed as quickly as possible

•  Cross category expansion - Specific styles and specific size ranges need to be created

•  Different cycle control - Capsule, short, medium, long collections need to be developed at the same time

•  Relevant offers - Need to develop quickly to match the market needs

There’s a need to quickly develop more samples, a higher range of products to match the latest style trends to satisfy customers’ requirements. While shopping online is supposed to be convenient, it can often be confusing. Many online retailers show tens or thousands of brands and SKUs, and if you don’t know what exactly you're looking for, and how it will fit you, the experience can be pretty frustrating. To mitigate that, retailers with the resources to do so are working to use data collection and predictions to create more personalized shopping experiences — i.e., showing you products, it thinks you will like based on your previous buying history.

The turnaround time in the fashion retail space is shrinking as the demand for the latest trends is increasing. Consumers demand the latest style in the quickest time and because of the rate at which trends change, speed of delivery has become quintessential to retailers.

Companies like Zara, H&M and Tommy Hilfiger themselves have had to restructure their entire supply chain processes to keep up with the market demands.

The big retailers are now able to get the latest apparel into the markets within weeks of the production, but most items take anywhere between six to twelve months which leads to a lot of lost sales.

Hence, there’s a need for more intervention of technology in the apparel supply chain in order to take the production to the next level.

Stitch Fix, established in 2011 in San Francisco, identified a way to resolve the challenges of the garment industry by reinventing the retail industry through innovative technology. This online subscription-based styling service takes an input from the customer and works in collaboration with human stylists and Artificial intelligence, to provide a truly personalized shopping experience to their customers, who need not go out to shop for clothes or even browse online. Stitch Fix delivers personalized recommended styles right to the customer’s doorstep on a regular schedule. The customers can keep all of the products or return what they don’t like or need. The client feedback is entered into the company’s data vaults to improve their algorithms and to get better at determining the preferred style and identify trends for every individual.

To better appreciate the advantage software offers, let us take an example of a large-sized garment manufacturing unit, with a daily production capacity of 50,000 pieces.Right from the product development to bulk production, as a piece moves through the various stages of production, data is generated at each stage. For cutting 5 lakh pieces of bottoms per day, 8 to 10 cutting tables (15mts length) will be required, consuming around 75 to 80 thousand meters of fabric daily.

Is it possible to manually track 80 thousand meters of fabric daily?

2500 workers, 1500 machine and 50 operations, and an approximate 2 lakh pieces of WIP (finished and unfinished), all this data has to be correctly recorded, as this data is crucial for future planning and ensuring the order is completed within the stipulated time and budgetary constraints.

From the example given above, we can identify the following problem areas :

1. Is it practical to retrieve and utilize the huge amount of manually stored data for deriving strategic inferences for current and future order planning?

2. What is the cost of this manual data management?

Skilled labor, streamlined systems, time, and the cost of manual consolidation of fragmented data.

3. Is it possible to accurately record, retrieve, use, and analyze this manually stored data for KPI measurement and strategic planning within the factory without any software support?

As an industry, the garment industry still lags many of its counterparts when it comes to software implementation. We all know that it is extremely difficult to keep track of all manually stored data.

In the garment industry, we have always meticulously tracked our production, piled on pages of data for complete production cycles, but have rarely used it to its full capacity for future planning. Software applications available for the industry today not only equip us to better maintain our databases but also use that data for informed decision making. Software solutions will make it extremely easy for businesses to incorporate the gathered data into the businesses strategic planning.

So why not handle the manually stored data in a factory through technology and automation?

We have many industry examples that can prove the worth of software investments and the extent of benefits, the industry can derive from it. We need ‘process improvement’ which demands the ability to benchmark and measure success. Today, people want to drive productivity up without increasing costs. Technology equips firms to efficiently evaluate and analyze their productivity, thereby resulting in an immense positive push to the company’s bottom line.

If we take a look at the banking industry, can we even imagine the functioning of big banks and financial institutions on paper, where small features like online and mobile banking have made things not only easier but also exceptionally faster for the users? Without software, managing personal accounts, overseas transactions, financial deals, which used to take multiple days can now be accomplished in a fraction of minutes.

So, do you think the garment industry is ready for this next leap in its development cycle?

In the garment manufacturing industry, the need for R&D scientists would have been non-essential in the past. However, they are highly critical in today’s world for giving us the best technology for high-end manufacturing. Most companies are looking at improving their R&D process and reduce uncertainty when going into production by exploring different avenues like robotics, 3D printing, and artificial intelligence.

Software undoubtedly offers the garment industry a humongous opportunity to change the way things are being done today. Although this change will demand investment to start with, in the long run, it will not only save time and money for the company but also allow the company to focus its resources and efforts in the direction of strategic sustainability rather than basic data management.

Adeel Najmi, chief product officer at Symphony Retail AI, defines machine learning as "Learning occurs when a machine takes the output, observes the accuracy of the output, and updates its own model so that better outputs will occur. Any machine that does this is using machine learning. It does not matter if data science methods are used or not. It does not matter if neural networks or some other form of supervised or unsupervised learning technique is being used. It's important not to get bogged down on the specific technique. What matters is, if the machine is itself capable of learning and improving with experience." quoted in an article by Steven Banker in Forbes e-magazine

Artificial intelligence (AI) for instance – incorporating computer vision, natural language understanding, and deep learning – is being used to produce key insights on trends to both expedite the initial design process and better predict demand for hyper-localized products.

 

What is artificial intelligence (AI)?

Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:


 

Difference between AI and machine learning?

Artificial intelligence is the field of science covering how computers can make decisions as well as humans. Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provides the system the ability to automatically learn and improve from experience.

The most popular applications of machine learning use a neural network. A neural network is a computer system loosely inspired by the human brain. Just like a child learns different tasks through experience as it grows and moves through different stages of life and applies its learning in different situations, similarly the machine learns from data fed into the system and tries to improve and give better solutions.

Coats the worlds leading industrial thread manufacturer has opened up Innovation hubs in North Carolina (America), Bursa (Turkey) and Shenzen (China). These are dedicated centers around the world for collaboration with a range of partners including customers, brands, suppliers, universities and start-ups. The Innovation Hubs will develop pioneering new products and processes in Apparel and Footwear and Performance Materials, which encompasses hi-tech products for end uses in Automotive, Oil and Gas, Protective Wear and Telecom. They provide creative and inspiring spaces where an innovative idea can be developed collaboratively and rapidly worked up into a prototype design which is then manufactured in a stand-alone pilot factory.

Threadsol a software tech startup, now part of Coats Global Services offers valuable Artificial intelligence powered production planning solutions for garment makers across the globe not only to optimize their production and reduce wastages in the garment industry but also helps in measuring the KPI and do strategic planning for process improvement. It also offers a completing cost control and benchmarking solution for Brands which helps them in negotiating prices with their vendors.

 



References

1. https://www.cbinsights.com/research/fashion-tech-future-trends/ 

2. https://www.blog.google/around-the-globe/google-europe/project-muze-fashion-inspired-by-you/

3. https://all-about-z.com/project-muze/

4. https://en.wikipedia.org/wiki/Fast_fashion

5. https://www.techopedia.com/ai-technology-what-to-expect-in-2018/2/33096

6.https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/#443bbd0d7361

7.https://www.analyticsvidhya.com/blog/2018/01/ibm-tommy-hilfiger-fit-using-ai-collaborate-reimagine-retail/

8. https://www.forbes.com/sites/rachelarthur/2018/01/15/ai-ibm-tommy-hilfiger/#24981d178ac0

9.https://www.forbes.com/sites/bernardmarr/2018/05/25/stitch-fix-the-amazing-use-case-of-using-artificial-intelligence-in-fashion-retail/#1a05536d3292

10.https://www.forbes.com/sites/parmyolson/2018/10/03/a-two-minute-guide-to-artificial-intelligence/#1d401aa961c0

11. https://www.techopedia.com/definition/190/artificial-intelligence-ai

12. https://en.wikipedia.org/wiki/Stinkdigital

13. https://stitchdiary.com/apparel-industry-technology/


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