How AI Helps Fashion Retailers Minimize Overstocks

Overstocks and understocks have been an issue even before the pandemic struck. Retailers often faced challenges with predicting how an item might perform. However, with COVID-19 the supply chain got completely disrupted.


According to Statista, inventory distortion during 2020 caused a whopping $580 million loss for stores.



As a result, fashion brands had to figure out what to do with inventory that nobody needed anymore and stock that was left across the world and unable to reach customers.


However, even though the hectic early stages of the pandemic are behind us, retailers are still facing the overstocks problem on a daily basis. This not only leads to large levels of waste but also money loss. So, today we want to cover the main causes of inventory discrepancies and share how they can be reduced. Let’s dive in.


Examples Of Fashion Retailers Dealing With Excess Inventory


First, let’s take a look at some fashion brands that are already approaching overstocks with a new mindset. Specifically, by getting rid of the extra inventory through setting up online marketplaces and selling materials at a discount.


LVMH. The luxury conglomerate has launched its own deadstock site called Nona Source. It will resell fabrics and leathers that were previously selected by the designers of the fashion houses under the brand.


Burberry. The British label has turned to outlets and discounted its 2020 overstock. Moreover, the company has chosen to donate some of the clothes to charity and recycle the rest. A good move considering the backlash the brand has received previously for burning unsold clothes.


Alibaba Group. China’s e-commerce company has also embraced outlets and even launched a new platform targeted at young consumers and aiming to get rid of the excess inventory that has accumulated during the COVID-19 lockdown.


As you can see, leading fashion retailers are getting creative with ways of dealing with overstocks. However, wouldn’t it be much easier and less costly to reduce inventory distortion all together? Keep reading to find out how that can be done!


5 Most Common Causes of Fashion Retail Overstocks


There are several reasons retailers may find themselves having to deal with excess inventory. Today, we’ll take a look at the five most common ones.



1. Sole Reliance On Historical Performance


The first typical cause of inventory distortion is relying only on historical performance. If you only look at past sales, typical weather conditions for a certain time of year, and other insights that don’t take into account the new reality - you’ll face difficulties.


Historical data is a great starting point, but to accurately forecast upcoming trends and hence prepare your inventory for various demand levels - you’ve got to incorporate external data and market trends!


2. Inaccurate Data


Next up is inaccurate data. This issue refers to situations when numbers simply don’t match up. The truth is, a retail business is made up of shipments, product placements, orders, returns, theft, and many other aspects that increase the likelihood of a mistake being made.


Hence, discrepancies can be quite common. Yet, in the end, they may lead to producing too much or too little of certain items. Simply because a few human errors occurred.


3. Supply Chain Inefficiency


Another common cause of understocks and overstocks is supply chain inefficiency. It can come in the form of poor communication with suppliers which leads to delayed orders and the entire logistics chain being disrupted.


Moreover, at times, when trying to avoid overstocks, retailers might wait until the last minute to place an order for new items. As a result, they don’t arrive on time and money is left on the table because the store is understocked.


4. Unsuccessful Marketing Campaigns


Marketing campaigns and promotions are common in fashion retail. They help get rid of unbought items that are out of season or garner attention before new product releases.


However, such campaigns don’t always rely on advanced analytics that connect inventory levels with forecasted demand and the effect of a promotion on other products. Consequently, retailers find themselves once again dealing with inventory distortion due to unexpected products flying off the shelves while others whither away.


5. Poor Assortment Planning


Finally, we’ve reached the last cause of inventory issues that we’ll cover today. Creating a bestselling assortment mix isn’t always easy. Many elements have to be taken into account and retailers are often left relying on their own expertise and past experiences.


As you know, this approach doesn’t always work. Figuring out what your customers will want in which location and at what price is crucial, but it involves analyzing enormous amounts of information. So, whenever things don’t go as planned, brands once again find themselves with too much or too little product on their hands.


How Can ML and AI Reduce Overstocks

Now that you know the most common causes of overstocks, it’s only right to discuss a solution to them. As you’ve seen, most inventory discrepancies arise from errors, lack of information, and too much reliance on the human mind. So, what if we told you that there’s a way to get rid of most of these underlying sources?


New technologies like artificial intelligence, machine learning, and predictive analytics have the power to completely transform the business of a fashion retailer.


These innovations can:


  • Help brands minimize inventory distortion

  • Identify which assortment mix will perform best

  • Determine how in-demand a certain product will be

  • Improve forecast accuracy by at least 1%, leading to a 3% increase in sales


Still not convinced? Reach out to our team and score a limited-time offer to try out our solutions! We’ll be happy to show you how predictive analytics can serve your brand by reducing waste and boosting your bottom line!

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