Enhance sustainability within the fashion industry with AI


The convenience of online shopping means more and more people choose to shop from the confines of their own homes. However, when shopping for clothes, customers are faced with the significant challenge of not being able to try them on. This has led many customers ordering multiple sizes, then returning the ones that don’t fit. The disparity of every retailer’s fitting characteristics, the complexity and sometime inaccuracy of old-fashioned size charts are the main culprits for this bad digital age behaviour!

Since part of the inventory is circulating in logistics networks for customers to try on, retailers are forced to manufacture more goods than the actual “real” market demand. At the end of each season, the excess production will have to be aggressively discounted in order to find a home. As you can imagine, the additional requirements on packaging and transport to adequately move the extra merchandise is not helping our little planet by fuelling greenhouse gas emissions. Fortunately, artificial intelligence and modern machine learning can help mitigate these negative impacts significantly.

Plastic Bags
A significant amount of plastic bags are used throughout the life cycle of an item, from being manufactured to the point it is delivered to the customer. More often than not, retailers will deliver orders using plastic bags. In addition, each individual item has its own plastic bag. For example, for 3 items ordered, there will be at least 4 bags used for packaging. Of course, there are reasons for using that many bags. The main purpose is to protect clothes from being damaged while handled through the supply chain, retailer’s warehouses and eventually reaching the customer.

The additional items being moved around, for the sole purpose of being tried on, will obviously put significant additional load on transportation. This includes from retailer to customer, from customer to retailer, and very often movement from branch to branch to eliminate the production surplus.

But how much pollution are we talking about?
When looking at reasons for returned garments, about 40% are being purchased with the sole intention to be tried on. Of course, there are multiple reasons to try an item, but the leading reason is to select the right size.

Let’s look at an example: A retailer with 10 million turnover, 200,000 orders per annum and a returns ratio of 25% will be using in excess of 20,000 plastics bags to cater only for items to be tried on. In addition, the retailer carries at least 3% more inventory than required due to customers mistrust of size charts, adding another 12,500 bags, in which the individual items are packed.

In total, we are looking at the production of 32,500 plastic bags involving the use of non-renewable energy resources, mainly fossil fuels, leading to the emission of about 500 kg of CO2 into the atmosphere.

The impact of transportation is even more significant. With 20,000 orders to be tried on, that’s an additional 7,200 kg of CO2 being emitted.

The production of a single polyester T-shirt results in the emission of 5.5kg of CO2, while cotton generates 2.1kg of CO2. Based on an estimated 3% excessive inventory, and considering a cotton T-shirt, which emits less CO2 to produce, 26,000 kg of CO2 emissions would be produced. And this is not taking into account transportation from factory to retailer.

The solution?
Prime AI have identified that the inaccuracy and impracticality of conventional size charts are very much at the heart of the customers’ behaviour. Prime AI offers an intelligent size recommendation tool, that takes advantage of modern artificial intelligence to find the perfect match between customers biometrics, customer purchase habits and each brands unique sizing characteristics. Delivering an instant, and much more accurate, size recommendation to each customer, increasing the likelihood of them finding the perfect fit at first try, but also boosting customer confidence when proceeding through their purchase.

By replacing traditional size charts with an easier to use, more accurate and more adaptable size recommendation tool powered by artificial intelligence, a significant reduction of the need for customers to order multiple items can be achieved. And with this, a significant reduction in carbon emissions.

Coming back to our example; the retailer with 10-million turnover retailer could reduce its impact to the environment by 28,000 kg of CO2. To put it into perspective, this equates to an average car running for 45 days non-stop.

Get in touch with us to find out more and see what we can do for your business at our contact form.

Or why not request a demonstration here.