The fashion industry is one of the most wasteful industries in the world. Globally, the industry creates 13 million tons of textile waste each year. It’s hardly a surprise that the majority of landfill waste comes from throwing out clothes.
What consumers may not be aware of is that 35% of all materials in the supply chain end up as waste before a product even reaches a customer. Some of this is cutting waste, unusable stock due to last-minute design changes, or spoilage in transport. However, the largest contributor to waste is excess inventory that does not sell on the retail market. Among 150 billion garments produced per year on average, only 20% to 30% sell at full retail price, and only 40% to 50% sell at a discount. Thirty percent do not sell at all and go straight to a landfill or incinerator.
The cost is not only ecological; it’s also bad for business. Brands’ profitability is at high risk. The industry loses about $500 billion each year due to discarding clothes before ever being sold, according to the United Nations. If it’s creating significant losses, why is overproduction still common practice?
The root cause of this issue stems from the lengthy apparel development process. From designing, sampling, merchandising and pattern grading to cutting and sewing, the whole process is inefficient, requiring a lot of manual work and rounds of revisions between multiple teams. Even for large brands, it generally takes three to six months before new products reach a retail store.
To account for this long lead time, brands have to predict consumer demands months in advance. Fashion demands are incredibly challenging to predict, especially considering that trends can change in a matter of hours in the age of social media. Many brands are now using big data and machine learning to analyze consumer behaviors, help predict trends and design products. However, the lengthy production time makes it less efficient than it could be.
Manufacturers are part of the problem. Most apparel manufacturers are low-tech and inflexible, relying heavily on manual labour and outdated practices when compared to factories in other industries. Lacking advanced technology, it becomes both time- and resource-consuming to set up a production line for each new style of garment. To be cost-effective, they require a minimum order quantity. Therefore, brands have to order a large amount of each design before manufacturers would agree to produce, further contributing to overproduction and waste.
The price for excess inventory is twofold. To mitigate losses from unsold products, brands must cut costs elsewhere while keeping retail prices competitively low. It may be the reason why fast fashion product quality is declining every year — it has become a challenge to wear a garment more than five times — and why it’s still common to pay factory workers in developing countries well below decent living wages, as revealed in a report by Deloitte.
So how can we tackle this crucial problem? Theoretically, the only way to eliminate overproduction is to produce on-demand, meaning that a garment is only created after a customer makes a purchase. This perfectly balances supply and demand and ensures that there’s no unnecessary wastage.
Tailors and niche fashion brands are already operating this way, although, with the current supply chain setup, on-demand manufacturing isn’t feasible for the mass market. It’s neither cost-effective for producers nor attractive for customers, who would have to wait weeks for their purchase.
On-demand production, however, can become viable with technology. The fashion industry is notorious for being a laggard in developing and adopting new technology, especially on the manufacturing side. Most new inventions in fashion tech only offer short-lived PR moments, like color-changing T-shirts or jackets with inbuilt speakers. Few fashion tech companies aim to solve more pressing problems, like overproduction. While not yet mainstream, the technology already exists to cut down production lead-times to mere hours, making on-demand and sustainable production achievable at a fraction of the regular cost.
Tech companies, such as Six Atomic, specialize in artificial intelligence-driven (AI) solutions for the apparel supply chain. This includes automating high touch-point and mundane tasks like grading sizes or generating 3D samples, all within seconds (instead of the weeks it takes when performed by humans). AI can shorten design lead time by analyzing millions of products that brands already have and synthesizing them into modular design libraries. Designers can easily create new products in minutes by mixing and matching modular assets like collar styles, sleeve lengths and silhouettes. Algorithms can instantly generate factory-ready patterns and assets to route and execute garment production anywhere in the world.
On the factory side, a pioneering production line digitization, T BTM2M, makes this achievable. It has solved the problems of planning, operating and managing an entire production line using a central application. From automatically driving a cutting machine to guiding production line workers through precise sewing processes, is enables on-demand garment factories to be extremely agile and flexible.
By automating apparel production and shortening lead time from months to hours, these technologies make on-demand production cost-competitive within traditional mass-production. As more brands and manufacturers integrate technologies like these, on-demand production will become mainstream, and excess inventory waste will be eliminated. Only then will the fashion industry have well and truly solved the $500 billion overproduction problem.
Taime Koe has over 10 years of experience as a product strategy expert. She has directed a UX strategy agency and lectured at university in brand strategy and customer experience design. She has made an impact on various international businesses including startups, NGOs and corporations, such as Agoda, BNP Paribas and UNICEF. She was part of The Octalysis Group before launching Six Atomic.
