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
Transportation
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?
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.
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