Forecasting accurate quantities of stock, sizes, and styles for a fashion business is one of the most challenging tasks in modern retail.
Today, brands and retailers deal with thousands of SKUs, each with multiple sizes and different colours, to be stocked at multiple locations. These factors make it hard to rely on traditional business tools and manual calculations, as swift fashion trend adoption means retailers need to remain agile across stock management and replenishment - a tough task with lengthy lead times, tariff upheavals, and supply chain disruptions.
Yes, fashion retail forecasting is an intricate, fragile process - all it takes is an incorrect formula to imbalance an entire season - but it doesn’t need to be this hard.
Here’s what teams need to know to move their merchandising into the future with fashion demand planning software, to reduce overstocking, avoid early size breaks, and plan for inventory replenishment to capture every sale.
What is inventory forecasting in fashion?
Fashion inventory forecasting is the process of predicting the amount of stock to match perceived customer demand for a fashion season, without being left over or understocked. Seasonal inventory forecasting involves the practice of analyzing historical data trends with market trends, social media signals, and known future events to estimate the correct style and stock quantities required across each sales channel for that season. Planning will also determine the range architecture, including what categories, silhouettes, colors, and price points will form the seasonal assortment.
Why is accurate fashion inventory forecasting important?
Inventory forecasting in fashion retail requires deep analysis to ensure there’s enough stock cover for each style, in each of its sizes, to match demand and secure the right amount of sell-through to protect profit margin. There are several impacts brands and retailers must deal with if the forecast is inaccurate - too much stock, or not enough - including:
- Profitability is at risk when demand for styles is incorrectly estimated, as these products must be marked down or discounted, which reduces profit margin.
- Cash tied up in excess stock amounts and stock management reduces the open-to-buy budget for the next season.
- Customer satisfaction is diminished when customers are unable to purchase their preferred sizes due to early size breaks.
- Inaccurate forecasts lead to overproduction and deadstock, which have significant environmental impacts when not handled sustainably.
What are the current challenges of forecasting fashion inventory?
With inventory accuracy falling to an industry average of 65%, forecasting variables present an ongoing variety of challenges for estimating stock cover and perceived demand. According to the Business of Fashion McKinsey State of Fashion 2025 report, 33% of brands continue to struggle with inventory positions, while in 2024, brands produced between an estimated 2.5 billion and 5 billion items of excess stock, worth up to $140 billion in sales.
Many of the challenges come from external pressures such as volatile micro-trends, seasonality, the weather, lead times, and supply chain disruptions, as well as changing consumer behavior.
However, much of the inventory inaccuracy problem is due to internal errors. That is, manual ordering using traditional business intelligence tools such as spreadsheets, which are unable to access all the data points needed to make accurate inventory forecasts.
“Merchandising teams are often ordering manually, using spreadsheets to plan product, store, and size breakdowns,” said Michaela Wessels, co-founder and CEO of Style Arcade. “That process is not only complex—it reinforces existing inaccuracies in the data.”
Manual ordering leads to inaccurate purchase order quantities, as the numbers have been reinforced by skewed sales data due to out-of-stocks at the store and size level. Retailers need to be able to connect and cleanse all available data to generate accurate order recommendations with high-level accuracy.
What are the differences between DTC and wholesale inventory challenges?
DTC challenges: Though direct-to-consumer brands have more control over stock allocation than wholesale retailers, many lack historical third-party data, especially for new collections or trend-led drops, which makes it difficult to predict demand and more directly impacts cash flow and margin.
Other DTC forecasting challenges include the high reliance on inventory capital, tying up significant amounts of cash in inventory, which increases risk if demand forecasting misses expectations. Then there are demand-driven logistics pressures, where brands must coordinate an influx of orders, last-mile deliveries, and returns while avoiding stockouts and managing slow movers.
Wholesale challenges: Multi-channel retailers face major challenges of channel visibility and coordination, while having to balance the stock levels for each sales channel. With limited visibility into real-time sell-through, retailers face longer response times to fast and slow sellers, compared to what was originally forecasted.
Further difficulties can be the flow-on effect of multi-brand retailers holding too much stock cover, which ties up open-to-buy capital for the next season, and leads to higher markdowns and margin loss. The slow adoption of fashion demand planning software for many of these legacy wholesale retailers is also holding them back from more efficient planning.
How is technology changing fashion inventory forecasting?
Artificial intelligence (AI) and machine learning algorithms are supporting accurate forecasting decisions that go beyond gut feel and historical data, in a way that supports fashion businesses specifically. A McKinsey study confirms that AI-driven demand forecasting can reduce forecasting errors by 20% to 50%, leading to a decrease in lost sales and product unavailability of up to 65%.
The problem halting user adoption in forecasting technology for fashion retailers is that standardized AI demand forecasting was designed for the fast-moving consumer goods (FMCG) industry, which does not consider the complexities of the fashion retail industry, like sizing and seasonality.
For example, with forecasting software like Style Arcade, the platform is user-centric, rather than AI-centric. By focusing on automating real-world tasks, the technology has advanced far enough that merchandising teams can process bulk coreline re-orders, single or multi-product forecasting, or yearly rolling forecasts for suppliers without error.
How to choose inventory forecasting software
Intelligent demand forecasting and merchandising platforms set brands and retailers up for greater accuracy at the scale required by all growing retail businesses, from DTC to large enterprises. When choosing the right platform, ensure human error is minimized, and day-to-day tasks that take up strategic decision-making time are automated. Consider whether the platform:
- Automatically analyzes historical data and seasonality trends. This should save you from collating performance data from previous seasons manually, so you can immediately identify what sold well, which products underperformed, how promotions impacted velocity, and when peak demand occurred.
- Offers smarter size curves. Your forecasting software should be able to predict demand based on the True Rate of Sale of the product, that is, when all sizes were in stock, not when sizes have been fragmented or discounting has occurred.
- Automatically forecasts demand by product, size, and location. Granular forecasting is essential, by SKU, category, size, and geography, with the flexibility to adjust for current assortment changes and external factors.
- Aligns delivery and supply chain timelines. Customizable weeks cover, lead times, and delivery frequencies based on specific periods to optimize sales to ensure you have inventory when you need it.
- Helps you visualize future cash flow. Seeing when sales are set to peak and trough enables brands to drive revenue through smarter stock planning and financial alignment with sales events.
With inventory distortion costing the fashion industry trillions, it's time brands and retailers looked to technology to foster sustainable and profitable growth into the future. Intelligent forecasting tools and software are leading the way for fashion businesses to optimize their stock cover, reduce lost sales opportunities, lower markdown spend, and boost profit margins - bypassing retail’s biggest data inaccuracies and process inefficiencies.
Leading global brands and retailers like Ralph Lauren, Princess Polly, and The Iconic have chosen Style Arcade to power inventory forecasting accuracy by product, size, and location based on True Rate of Sale logic to complete thousands of product lines in seconds, not spreadsheets.
Find out more about Style Arcade's Product Forecasting
