Imagine walking into your favorite clothing store, excited to purchase that trendy jacket you’ve been eyeing online. You finally spot it, but alas, your size is nowhere to be found. Frustrated, you leave empty-handed. This common scenario highlights a significant challenge in fashion retail: aligning inventory with customer demand, especially concerning size availability. AI inventory planning helps fashion retailers optimize stock allocation, reducing stockouts and overstock situations.
The Cost of Poor Size Planning in Fashion Retail
In 2024, the fashion industry faced substantial hurdles with inventory management. Despite efforts to balance stock levels, approximately one-third of brands continued to struggle with excess inventory. Luxury giants like LVMH and Kering reported combined excess stock worth nearly $5.4 billion in 2023, with impaired inventory accounting for about 4 to 8 percent of total sales. To mitigate these issues, many brands had to offer discounts, which, while clearing stock, often diluted profits.
For instance, Nike reported that markdowns affected around 44 percent of its assortment in 2024, a significant increase from 19 percent in 2022. (Business of Fashion). At the same time, out-of-stock sizes continue to rank as the top customer complaints, resulting in lost sales. Overstocking is not the solution as it requires more capacity at the warehouse and potentially hurts sustainability targets due to a higher carbon footprint as well as higher unsold stock that needs to be dealt with. As of 2026, destroying unsold products will be illegal in the European Union.
A critical aspect of this challenge is size planning and allocation at the store level. Inaccurate stock buying across sizes can lead to an average monthly profit loss of 20% for a brand. When a store lacks the appropriate size distribution, it frustrates customers and results in lost sales and excess stock of less popular sizes.
So, why do fashion retailers continue to struggle with this issue? Let’s explore the unique planning challenges that make size allocation so complex.
Unique Size Planning Challenges in Fashion Retail
1. Regional Variations in Size Preferences
Fashion retailers operate in diverse markets where size preferences vary significantly. A size medium in New York City may sell out quickly, while extra-large sizes dominate demand in the Midwest. Without a granular approach to store clustering based on size-selling profiles, retailers risk sending the wrong size mix to different locations, leading to localized stock imbalances. However, for retailers using prepacks for efficiency in allocation and replenishment operations, defining different prepack configurations to account for store-level variances can be challenging.
2. New Product Launches Without Historical Data
For staple items, retailers can rely on past sales data to predict demand. But what about new products with no historical size-level data? Traditional methods often rely on gut feelings or broad market trends, which can result in overstocking unpopular sizes and understocking high-demand ones. AI and machine learning enhance forecasting and replenishment based on product attributes (e.g., fabric type, fit, silhouette), significantly improving accuracy.
3. The “Phantom Demand” Problem Due to Stockouts
A common issue in size planning is misinterpreting sales data due to frequent stockouts. If a store consistently runs out of size small in a best-selling dress, the raw sales data might suggest that size small isn’t that popular—when, in reality, it was never available long enough to be purchased. AI models can adjust for these “phantom demand” distortions by estimating lost sales based on similar products and historical patterns.
4. E-commerce vs. Brick-and-Mortar Size Distribution Gaps
With the rise of omnichannel retail, brands must optimize inventory differently for online and physical stores. Some sizes might sell better online due to fitting hesitations, leading to higher returns. Meanwhile, in-store sales can be more influenced by immediate availability. Many retailers struggle to balance size allocation between these two channels, leading to e-commerce overstock and in-store shortages.
5. Fast Fashion and Short Product Lifecycles
In fast fashion, styles turn over quickly, leaving little room for inventory correction. Unlike classic wardrobe staples, trendy items need to sell out at optimal levels before they become obsolete. Poor size distribution can mean excess stock in undesirable sizes or lost sales on high-demand ones, with no opportunity for a reorder. AI can help predict size-level demand dynamically as trends shift.
How AI Inventory Planning is Revolutionizing Size Planning and Allocation
AI and machine learning are reshaping how fashion retailers tackle these size-planning challenges. Here’s how:
1. Store Clustering Based on Size-Selling Profiles
AI analyzes vast amounts of sales data to group stores with similar size-selling patterns. By clustering stores based on these profiles, retailers can tailor size distributions to meet the specific demands of each cluster, ensuring that popular sizes are adequately stocked where they’re most needed.
2. Demand Forecasting for New Products at the Size Level
Predicting demand for new products is notoriously challenging. AI models can forecast demand at the size level by analyzing product attributes, historical sales, and even social media trends. This approach allows retailers to make informed decisions about size allocations for new items, reducing the risk of stockouts or overstocking.
3. Removing Bias from Size-Level Sales Data to Adjust for Stockouts
Stockouts can skew sales data, leading retailers to underestimate the true demand for certain sizes. AI can adjust for these biases by analyzing patterns and predicting the sales that would have occurred had the stock been available. This corrected data provides a more accurate foundation for future size planning.
4. Dynamic Allocation Between E-commerce and Physical Stores
AI enables real-time allocation adjustments by analyzing where sizes are selling best. If the size medium is selling out online but sitting in stores, AI can recommend rebalancing stock by shifting inventory across channels to reduce markdowns and lost sales.
5. Dynamic Purchase Order Planning and Open-to-Buy Management
AI can also enable purchase order planning for in-season replenishment and open-to-buy management, where inventory levels are continuously adjusted based on real-time sales data and demand forecasts. Automated recommendations for purchasing ensure that popular sizes are restocked quickly, reducing the risk of lost sales and improving customer satisfaction.
Final Thoughts: Smarter Size Planning for a Profitable and Sustainable Future
Aligning inventory with customer demand at the size level remains one of fashion retail’s biggest challenges. The traditional approach of broad allocation strategies no longer works in today’s fast-paced, omnichannel world.
In a world where customer expectations are higher than ever, getting size planning right is no longer optional—it’s essential. The retailers who succeed will be those who harness the power of AI to align inventory with demand, creating a seamless shopping experience that keeps customers coming back for more. Getting size buying and allocation right is also good for the environment and will reduce the end-of-life stockpiles that need to be collected, redistributed, or recycled.
By leveraging AI-powered store clustering, demand forecasting, and stockout bias correction, fashion retailers can make smarter, data-driven decisions that ensure the right sizes are in the right places at the right time.
The result? Happier customers, fewer lost sales, and a healthier bottom line.
An Intelligent Platform for Size Planning
Solvoyo is a notable vendor recognized in the 2024 Gartner® Midmarket Context: Magic Quadrant™ for Supply Chain Planning Solutions.
Solvoyo offers AI-powered solutions that help retailers gain real-time visibility and keep shelves stocked correctly. Our platform allows you to address discrepancies proactively, boost operational efficiency, and—most importantly—keep your customers happy.
Are you ready to say goodbye to phantom inventory? Contact us today to learn how we can help transform your inventory management and turn these hidden costs into visible profits.
