The challenge
Overstocking is a quiet margin killer. It doesn’t show up as one bad decision — it shows up gradually, in FBA storage fees, long-term storage penalties, inbound and removal costs, and 3PL carrying costs that stack up month after month until a brand is profitable on paper and thin in practice.
Several clients came to us with exactly this pattern: healthy sales, full warehouses, and margin eaten by inventory sitting too long, in the wrong quantities, in the wrong places. The instinct is usually to order less. That fixes the fee problem and creates a worse one: stockouts, lost Buy Box eligibility and broken Prime delivery promises.
What we did
The fix isn’t ordering less. It’s forecasting better — accurately enough to hold the minimum stock needed to serve every customer, without paying to store more than that. That is the problem Velmont Foresight, our proprietary forecasting system, was built to solve. It works in three layers.
Historical and pipeline data
Actual sales history combined with known upcoming demand, integrated with the client’s Seller Central account, 3PL systems and factory production timelines.
Market-based estimation for thin-history products
When a product doesn’t have enough sales history to forecast confidently, the system brings in category and competitor sales-velocity data to estimate realistic demand.
External-factor monitoring
Production and shipping delays, tariff changes and demand-moving events are tracked and factored in, rather than assuming today’s conditions will hold for the next quarter.
In internal testing across active client accounts over the past year, the historical and market-based layers have proved reliably accurate in the large majority of cases. The external-factor layer — inherently harder, since it forecasts policy and shipping disruption rather than sales patterns — has been directionally reliable more often than not. We treat Foresight as a maturing internal capability: it sharpens our team’s forecasting decisions, and is not sold as a standalone tool.
The results
| Client A — storage fees as % of sales | 4.28% → 2.23% (−48%) |
|---|---|
| Client B — storage fees as % of sales | 2.12% → 1.40% (−34%) |
Two brands, two categories, the same pattern: meaningfully lower storage cost as a share of sales, without a matching rise in stockouts or lost availability. That repeatability — the same mechanism producing savings across different accounts — is what makes this a system-level result rather than a one-off win.