Can artificial intelligence combat oversupply and minimise deadstock in fashion?
Updated: Feb 26, 2021
Originally published on Eco-Age.
Fashion tech innovator, writer and public speaker Brooke Roberts-Islam investigates the role that AI could play in reducing the environmental impact of unsold fashion.
Artificial Intelligence, for all its futuristic and sci-fi connotations, is a way of analysing data that helps to make smarter decisions. It’s true that AI can be much more – it can ‘learn’ and evolve based on data, but in the case of fashion, its most common application is to find out what’s selling, what’s not and to do something about it.
Why do we need artificial intelligence to solve such problems? What is wrong with the current system? Followers of fashion and sustainability will be no strangers to the news that brands, both big and small, fast and slow, are grappling with the current business model that relies on predicting what styles will be popular in several months’ time and in what volumes. Additionally, ensuring that the product is available in the right place, at the right price at the right time is increasingly difficult. Add to that the influence of social media on fast-changing trends and it is hard to keep up, and operate in a financially viable, let alone sustainable manner. The very cycle of fashion that requires predictions months ahead of the product being available is outdated and slow and leads to product sitting unsold in warehouses and eventually being discounted, or worse, landfilled or incinerated.
The McKinsey Notes From The AI Frontier report quantifies the benefits of AI for retail as being mostly in marketing and sales (targeting customers with the right products and boosting conversion) and supply chain and manufacturing (manufacturing the right product in the right quantity and making it available at the right time). While this may sound basic, it’s the current fast fashion business model’s inability to get this right that is causing overstock and catastrophic resource and waste consequences.
How can AI help to cut down on oversupply of stock and thereby reduce the environmental impact of unsold fashion? One of the key ways is by capturing data through online sales based on geographical areas to determine the types of products that are on demand (and those that aren’t) in specific locations. A prime example of this is the Nike Live store ‘Nike by Melrose’ in West Hollywood, which is a fusion of online and offline stores. The first of its kind, the store requires shoppers to sign up to the Nike Plus app in order to unlock the services and perks available in the store. In signing up, the shopping behaviour and preferences of each customer are known to Nike, allowing it to only stock what local shoppers want. No overstock and no need to have regular sales to get rid of stock that wasn’t right for the local customers. This data feeds back to the manufacturing systems and influences the styles and quantities manufactured.
Casting the net wider to take in global shopping preferences and real-time purchasing behaviour, The Trending Store, which opened in London’s Westfield Shopping centre this week, is using AI to analyse social media data to extrapolate the ‘top 100 fashion items’. The trending products, which span high-street and designer looks across fashion, accessories and footwear, are then collected each morning by a team of stylists from the retail stores at Westfield London. The data analytics are being performed by NextAtlas, who track 400,000 early adopters spread across 1,000 cities worldwide to determine the top 100 items. Screens in The Trending Store show exactly where each trend originates from, so shoppers can see which city or country is influencing the popularity of the item. This is perhaps the first foray into shopping centres providing AI-driven multi-brand solutions that helps get trending styles and colours in front of customers at the right time.
It bears noting that these are reactionary solutions that require linked-up data and transparency across the supply chain to genuinely reduce overstock and deadstock – that is to say, the data should ultimately drive the design and manufacturing process to ensure that only the product that is ‘needed’ is manufactured. As fast fashion retailers struggle to manage the vast quantity of products they manufacture globally, this data could allow retailers to make smarter and more sustainable decisions. However, digitalisation of the entire supply chain (allowing the capture of data at all points from the initial design and manufacturing to global sales) is necessary in order to fully harness and act on this powerful data. Watch this space.