Bringing new fashion lines into your assortment is one of the most exciting (and high-stakes) moves a brand can make. It keeps your collection fresh, drives customer engagement, and can unlock new growth. But it’s also risky. Demand planning for a product with no sales history can be incredibly complex, and getting it wrong can lead to stockouts, overstocks, or lost margin.
In today’s unpredictable market, fashion inventory forecasting is a critical capability for every merchandising and planning team. So, how do you forecast demand for new products with no historical data? The answer lies in combining data-driven insights with market understanding and proven inventory forecasting techniques.
1. Analyze past product launches
One of the best starting points is to analyze your previous new product introductions. How have similar launches performed? Did they experience early demand spikes, or did they grow steadily over time?
If you’ve launched seasonal products before, like spring dresses, tailored coats, or capsule collections, look at how quickly they sold, what drove performance, and when demand began to taper off. Use this sales pattern to guide projections for your new line.
2. Review brand, category, and collection trends
Forecasting inventory demand doesn’t mean starting from scratch. Often, your upcoming product has similarities with existing items, whether it’s in the same category, collection, or brand. Reviewing these performance trends can help model future outcomes.
If a particular category, like wide-leg pants or maxi dresses, is trending up across your range, it’s a strong signal that new entries in this space could follow the same trajectory. The same logic applies to brand or vendor performance across core product lines.
3. Assess historical size performance
When buying or forecasting products that come in multiple sizes, like denim, footwear, or lingerie, understanding historical size performance is essential. Getting the forecast by size right ensures you’re investing in the sizes your customers actually want, reducing overstock in fringe sizes and minimising sell-out risk in core ones.
For example, if your past data shows that 40% of sales for a specific category come from size M, 30% from S, and 30% from L, you can apply this ratio to your future buys. This helps you align stock with demand from day one, improving full-price sell-through and reducing markdowns.
4. Merge sales history for similar styles
If you’re replacing a bestselling item with a similar update, say, last season’s blazer is getting a slight fabric refresh, it makes sense to link the historical sales data from the outgoing product to the new one. This allows you to make informed decisions about demand, even without new data.
Style evolution is constant in fashion, but small updates (like a change in hardware, length, or fabric weight) don’t always mean major shifts in consumer behavior. Smart forecasting accounts for continuity where appropriate.
5. Understand technology’s role in accurate forecasting
While there’s plenty of room for gut instinct on new products (no one knows your customer better than you), strategic demand planning in fashion today is only getting more difficult, and the stakes are higher with accelerated consumer expectations. Brands are navigating shorter trend cycles, rising costs, tariffs, and slimming margins - and have become accustomed to predicting how many products and sizes to match customer demand up to 10 months in advance.
For fast-scaling brands acquiring thousands of new customers a month, and buyer preferences shifting overnight, it’s almost impossible to accurately and strategically forecast inventory without the right technology to support decisions at scale.
Style Arcade works by matching supply with demand and recommending order quantity, by size, for specific items. The issue for inaccurate inventory forecasting isn’t a lack of data; it's the lack of technology that matches the complexity and pace of critical buying and planning tasks, with the required level of granularity.
Getting fashion inventory forecasting as close to correct as possible using product data sets and automated processes will set your fashion brand up for a sustainable, profitable future.
Find out more about Style Arcade's Product Forecasting
Image credit: Aje
