The fashion industry has long had difficulty with inventory distortion and subsequently, poor sustainability practices. The frequent overstocks lead to enormous levels of waste and overproduction, in general, contributes to the decline of our world’s environment.
In fact, nearly 20% of global wastewater is produced by the fashion industry. Hence, brands are responsible for almost a quarter of the damage that wastewater causes. However, the environment isn’t the only thing suffering from overstocks.
UN estimates that the fashion industry loses roughly $500 billion of value yearly due to its lack of recycling and clothing disposal practices. So, as you can see, there’s also a major effect on corporate bottom lines.
The good news is – Artificial intelligence (AI) can help companies tackle the overstocks problem. In fact, it is an incredibly powerful weapon in the fight for sustainability. Primarily, because it helps avoid common overproduction causes like inaccurate predictions, failed promotions, and reliance on limited data.
What Causes Fashion Overstock?
To understand how to get rid of overstocks, it’s first important to understand what causes them. Below, let’s take a look at the four main reasons items end up wasting away in stores.
- Wrong demand forecasts: By having inaccurate demand estimates brands produce too much of an item that ends up not performing as well as expected;
- Failed promotions: Marketing initiatives that aren’t backed by data can result in poor campaign performance which leads to the promoted items stuck on the shelves and in warehouses;
- Fear of understocks: Sometimes retailers are so afraid of not meeting large demand that they overproduce certain products just in case;
- Reliance on historical data: Often, instead of looking to upcoming trends and consumer behavior changes, retailers rely solely on historical data to plan for the upcoming season;
All of these fashion overstock causes are unfortunately quite frequent and not often battled correctly. Thus, leading to excess items being discarded without enough thought put into how to get rid of the initial problem.
How is Fashion Overstock Typically Handled?
Prior to the emergence of modern technologies, overstock was handled in various ways. Some have actually proven quite effective, but unfortunately still result in significant monetary loss for the brand.
Often, retailers discount the items that haven’t been sold by the end of the season. These discounts typically range from 10-70%, depending on seasonality and the amount of stock that’s left. As you can imagine, it’s a good strategy to win back at least some money from the investment into production, but large discounts still have a negative effect on a brand’s budget.
Unfortunately, many brands still choose to throw away their excess inventory. In the luxury industry, it can be an especially common practice as demand is largely driven by low supply. However, considering the environmental implications and the backlash that brands like Burberry faced a few years ago for burning unsold clothes, this isn’t the best tactic for getting rid of overstocks.
Finally, donating to charitable organizations is a great solution for when you have to deal with excess stock. After all, this way you minimize waste and simultaneously improve your brand’s reputation. Of course, there’s still a significant monetary loss, but at least you aren’t unnecessarily polluting the environment.
Why Artificial Intelligence In Fashion Is The Ultimate Solution To Overstocks
Specifically, to rationalize like people do and choose an action that a person would deem to be best for achieving a certain goal. In essence, it’s a technology that learns from data and past experiences, adapts, and solves distinct problems.
In recent years there’s been increasing attention on the potential of artificial intelligence in various industries. Fashion retail included. In fact, some leading brands have already turned to AI to improve their existing processes.
For instance, in 2018 Alibaba introduced its “FashionAI” concept store with intelligent garment tags, smart mirrors, and omnichannel capabilities.
Additionally, Tommy Hilfiger turned to AI to better identify upcoming trends and enhance the entire creative design process. As you can see, top retailers are pushing hard for AI adoption.
However, there are 3 key applications of artificial intelligence in fashion for battling overstock that we’ll discuss today.
First and foremost, AI plays a significant role in retail by automating repetitive tasks. Since machine learning algorithms can go through data at a speed that no human can, data collection and processing happens at a much quicker rate.
As a result, decision-makers can focus on key strategic initiatives and make data-informed decisions regarding stock levels instead of wasting valuable time on looking for insights.
An excellent example of AI being used for fashion retail automation is Vue.ai. A platform that not only automates product tagging but also personalizes customer journeys to increase demand from the products that you may want to sell.
So, consider checking it out if you want to leave mundane tasks behind and focus on improving your inventory allocation.
CUSTOMER BEHAVIOUR AND TREND PREDICTION
Another key application of artificial intelligence in fashion is customer behavior and trend prediction. Once again, by analyzing large amounts of data, AI can detect customer behavior changes and deliver these insights to fashion retailers.
In turn, brands are alerted to a change in customer preferences and upcoming trends well in advance and can adjust their production accordingly.
Solutions like Dynamic Yield are already on the market and working on unifying customer data across various channels to generate meaningful insights that fashion retailers can immediately act upon.
Finally, the most direct way to battle fashion overstock is by leveraging AI and ML for demand forecasting.
The truth is, demand patterns can be clearly identified with the help of algorithms. Yet, merchandisers tend to rely on their personal opinions and past experiences instead of data that showcases how seasonality, trends, and other outside factors affect item performance.
The good news is, once you embrace smart demand predictions for your collection planning process, you’ll be able to correctly determine how much stock to produce, minimize overproduction, and become more sustainable.
Innovative platforms like GFAIVE’s Smart Demand Forecast are already helping retailers incorporate external data into the prognosis and make highly accurate demand predictions that inform leaders of the right amounts to order.