Apparel's Biggest Profit Killer Is Buying Too Much of the Wrong Sizes

01 /The Concept

Syrup helps apparel and footwear retailers buy the right inventory – enough to sell through at full price, not so much that it piles up in the warehouse waiting for a clearance sale.

At its core, Syrup builds forward-looking demand forecasts so that stores order only what they can realistically sell at regular prices within a reasonable window. The sweet spot: not overstocked, not understocked.

Setup requires connecting Syrup to the store's existing commerce and ERP platforms – Oracle NetSuite, SAP, Salesforce, Shopify, and similar systems – so it can pull in the data it needs. Full onboarding takes three to four weeks, with the startup's team handling integration and configuration.

Forecasts draw on more than just historical sales data. Syrup also ingests weather data, local holidays, social media trends, and other external signals that have historically influenced demand for specific product types – and might again.

One of the platform's key differentiators for apparel specifically is that it forecasts at the size-and-color level – not just by product type. This granularity matters enormously in fashion: the bestselling style in size M and navy might be a completely different inventory situation than the same style in XL and olive.

Syrup also accommodates the reality that every buyer has their own constraints and intuitions. Minimum order quantities, assortment requirements, supplier lead times, and category preferences can all be configured as rules the platform respects when generating recommendations.

The output is a stream of purchase recommendations. Syrup monitors sales velocity and inventory levels, and when conditions warrant, surfaces a recommendation to the buying team: here's what to order now. Each recommendation can be accepted, edited, or rejected – and every decision feeds back into the model's ongoing training, gradually improving alignment with how that team actually thinks.

The platform launched three years ago and has built a meaningful customer base, including recognized brands. Stores using Syrup have cut overstock inventory by 20% while reducing the manual work required by 10x. Full-price sell-through has increased 10%, and stockouts on in-demand items have dropped 80%.

Across all Syrup's customers, the platform currently oversees $5.2B in inventory across 13.7 million store-product combinations – before accounting for size and color variations. Those customers have collectively generated over $100M in incremental revenue.

The growth arc is solid: $1M pre-seed in summer 2021, $6.3M the following summer, and $17.5M in December of this year.

02 /Why It Matters

Syrup's growth isn't just because the platform is technically smart – it's because the problem it solves is genuinely painful and expensive.

Sitting inventory is frozen capital. Money tied up in unsold goods can't be used to buy what actually sells. And to move the overhang, retailers resort to markdowns – which erode margin and, over time, train customers to wait for sales instead of paying full price.

To feel the scale of this: at any given moment, roughly $500B worth of inventory sits idle in retail warehouses. For apparel specifically, only 24% of stock purchased for a warehouse sells within two months, 45% within six months, and just 69% within a full year. That means at any point, at least 75% of a retailer's working capital is locked up for at least two months.

Retailers would rather pay for better forecasting than pay interest on the credit lines they need to fund those purchases – if they can even get the credit.

More accurate demand planning, as Syrup provides, is one approach to the overstock problem. Others exist.

Ghost, [covered previously](/review/tvoi-dengi-lezhat-u-nih-na-sklade) in August, helps retailers offload excess inventory without running public sales. It built a private marketplace where large overstock lots are sold to discount chains, international distributors, and other wholesale buyers. Ghost has raised $68M.

Max Retail, [covered previously](/review/srazu-stat-globalnym), operates on a similar model but serves smaller retailers and buyers. It raised $8.2M.

Yaysay, [covered previously](/review/500-milliardov-dollarov-za-30-minut) in October, built a consumer-facing flash sale app for designer apparel, shoes, and accessories. Each user gets a daily personalized list of items; anything not purchased that day disappears from their feed permanently. Items added to cart have a 30-minute purchase window before they vanish too. The scarcity mechanics are the feature. The app had just launched at the time of the earlier review but had already raised $10.3M.

03 /Opportunities

Syrup points to the size-and-color dimension as its deepest technical challenge.

Even with a relatively small brand catalog averaging 15,000 SKUs per brand, the number of store-product combinations already reaches 13.7 million. Add in color and size, and the combinations multiply to a scale no human buying team can handle manually – it's an AI problem by definition.

But those additional dimensions are also where the real forecasting accuracy comes from. Syrup reports that some customers have seen 46% improvement in efficiency compared to older legacy forecasting tools.

Size-and-color-aware demand planning should soon become the baseline expectation for apparel and footwear retail. The global apparel and footwear market is projected to approach $2 trillion by 2027. Platforms that can operate at this level of granularity will face enormous demand – and one platform won't serve the whole market.

The direction: build platforms that forecast at the style-size-color-store level. The technical challenge is real, but it's a solvable engineering problem – not a market risk. And "no one wants this" is a concern that clearly doesn't apply here.