Here is an article by one of our Top 5 Budding Data Scientists, Lalit Garg. Read about his approach to transform the Apparels and Garment Industry with the help of Data Science and AI Technologies.
01. Introduction about Apparels and Garment Industry.
Brief Facts about World Garment Industry:
- Total World Garment Industry is estimated to be 480 Billion USD.
- India’s contribution is close to 16 Billion USD for last 5 years and stagnant and having a CAGR of -1%. India is contributing only 3% of Total World Population of the Garment Industry.
- On the yarn front, India’s contribution is 23% as compared to China having 13%.
- On the Garment Front, India’s contribution is only 6% as compared to China having 52% market share in export of the total world garment industry.
- In China, there is one company which is having its turnover of 4.6 Billion USD alone and out of 4.6 Billion USD, 3.2 Billion USD is achieved only because of outsourcing to small industries.
- In India, 95% garment industry belongs to small companies.
- Besides this, Bangladesh, Vietnam and Cambodia are growing in Garment Industry because of rapid adoption of technology.
02. Challenges in Apparel Industry
- 10 years back time to respond to buyers was around 8 months for end to end cycle from inquiry to shipment. Now its squeezed to 5 weeks (Tighten timelines).
- Companies are not nimble and agile i.e. they are not flexible in improving this time gap to respond to buyers.
- Buyers demand the right product at the right time at the right place.
- Earlier any fashion product was ok to have 2 seasons or 3 seasons. Now it’s going to change very fast and almost every month fashion is changing very rapidly and companies have to respond in a fast way.
- Because of Instagram and other social sites in place, any product style is very much popular and buyers are demanding it in huge quantities. Companies are not able to cope up such drastic demands.
Other perspective to look at some other challenges:
Who is the 2nd Largest retailer in the Garment Industry?
- 2nd Largest retailer amazon. How because their servers are learning on a constant basis.
- How market segmentation is done by Amazon. For Amazon, each individual customer is their market.
- So what exactly to buy, amazon knows much better than human beings or companies. Even better than GAP and H&M
- Effect is 25% of malls are becoming empty now.
03. Some disruptive milestones in the history:
- 66 million years ago – dinosaur’s extinction
- 1698 – Steam engine invention (End of bullock cart, the way we travel)
- 1908 – Model T – Henry Ford car model for the general public
- 1975 – First personal computer
- 1991 – World Wide Web
- June 2007 – biggest disruptor in the apparel industry apple iPhone launch that disrupted how people perceive fashion.
- Principia Mathematica – Invention of Calculus
- Invention of Flight – Wright Brothers
- Gene discovery – Chromosome
- Landing to the moon – 1969 – Apollo 11
- Gary Kasparov was beaten by Deep Blue Computer – 1997
- Lee Seedol was beaten by Alpha Go – 2016
- Google maps are self-evolving with accuracy of more than 95% correct with accurate time of reaching destination.
03.1 Data Science and AI strategy and its impacts.
- Business problem to address:
- Outstanding customer service is required.
- Companies have to be nimble and agile and not bulky in responding to ever-changing buyers demand.
Example:
- If buyer sends any tech specs, companies take 4 days to respond to the buyer and companies can’t afford the cost of 5 merchandisers and 5 designers to work on that.
Buyers don’t provide that much flexibility. So how it can be reduced to minutes or seconds or how it can be automated.
- Buyers are asking it in real-time.
- So Reaction time has to be measured in seconds now to keep competitive in the market. (Metric).
- Costing should be done in minutes or seconds.
Thought Process:
- Collaboration through technology:
- Digitization of transactions.
- Ex: What is the status of order. So merchandiser interaction has to be digitized [Data Mining]. So that decision can be taken.
- What needs to be tracked. Average number of days for reaction time for each buyer.
- Average number of minutes for doing costing.
- Average type of problems.
What needs to be done:
- Companies need to institutionalize their experience in their company through digitization. (If person with experience left the company some or other day. Then experience also goes along with him if not institutionalized or digitized.)
- For ex: How to handle crib fabric quality like shrinkage and other factors. Where is the repository for capturing such data?
- Capturing events like shipment becomes late or the buyer has asked for revision of the garment. So if such events are captured, data can be mined and decisions can be taken based on it.
Solution: An AI-enabled Chabot tool will interact which reads that buyer mail, goes to the event manager, picks up repository data, analyze it, prepare response, send mail back to the buyer with cost sheet.
- Literally we have merchandiser who works 24×7 automatically without human intervention.
Another Business Problem:
- For retailers, inventory churn is a major area of worry. How retailer earns because of maximum churn rate on its inventory. Earlier It was 20 weeks now it is reduced to 8 weeks.
- So the retailer has to predict less in the future because the churn rate is high. So less risk in his assumption.
Technical Solutions:
- Time and Action calendar
- Automated costing
- Product Life cycle management
- Collaborative capacity planning tool
- Automated Production Planning
- Active merchandising and sales analytics engine.
Desired targets to achieve:
- Reduction in costs
- Reduction in lead times
- Merchandising productivity enhancement
- Improved bottom lines
- Improved inventory turns
- Order sheets reading through PDF, Scanned images, Sale orders. Check inventory levels, free stock, raise PO, costing cycle. All can be automated from 8 days’ task to few minutes.
So How AI can impact Fashion / Apparel / Garment manufacturer industry overall.
- Planning – How much I have to manufacture based on my past data putting into my Do I need to manufacture 10000 pcs or less or more? How demand patterns will be generated over a period of time.
- Design: For example, through market research, you come to know that sunflower design is popular among youth, so your computer can suggest you at least 50 different good designs of sunflower and integrate with your CAD system.
- Quality: How the human being is checking each garment, machines can be trained over a sample size and then productivity can be maximized. – During Fabric inspection, the fabric is rolled into a machine and run over it for spotting any dirty marks, cutting areas, any lines in the yarn areas and others.
– How image recognition helps in detecting the pattern of the color shade of the fabric and using a laser light, it marks the circle whenever there is wrong classification is done in the fabric. So the machine is literally replacing a human being and doing it automatically while fabric testing using image recognition technology by using sensors, high-resolution cameras, and laser lights. - Production: Smart machines can be deployed which learns itself for say collar stitching or any other piece and then human errors can be reduced like straight stitching and productivity can also be improved a lot. So there will be the death of dumb machines in the near future.
- Hand dexterity: A lot of research is also going in this direction where hand dexterity shall be fixed in the next 10 years. So machines will be doing the job of manufacturing of cloth.
- Smart Communication: How merchants communicate with buyers will be changed. Like Email Communication, so like google mails filter spam automatically because of AI inbuilt into its software. So merchants can communicate similar fashion by use of AI. The system will filter the emails which are important and what and when to respond to buyer in an intelligent way.
04. Companies using Data Science and AI on the global front.
Stitch fix is using a lot of recommendation algorithms to their customers for the brands they are visiting and could be interested also in the future to growing demand among its existing customer base. They are using machine learning and artificial intelligence for their personalized promotions to their target audience.
https://armoneyandpolitics.com/automated-couture-robots/
How Tianyuan company in china is producing a T-shirt in 22 seconds with its 21 automated assembly lines with 330 robots using suction arm to lift the cloth, specialized camera for image processing algorithm for needle placement with accuracy of 0.5 mm.
Total 8,00,000 garments in a month and 1 million garments in a year just because of Technology. Cost of manufacturing a T-shirt is 33 cents. Having customers like Reebok, Armani, Others. It’s difficult to beat this price at any cost.
Use of future technologies like 3D printing and visualization by Hugo Boss Company to provide their customers real-time store experience and to put their brands online so that customers don’t need to visit the store and can purchase directly there.
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