How to build a wholesale forecast from account level order history

Building a wholesale forecast starts with account level order history, then applies seasonal patterns, growth assumptions and adjustments for shorter lead times. Wholesale accounted for 57 per cent of brands' total business and 52 per cent of respondent brands indicated that wholesale is their most profitable distribution channel in recent data from more than 14,000 brands and 675,000 retail buyers, making accurate forecasts essential to profitability in 2026.

What historical data should you aggregate for a wholesale forecast?

Start with at least 24 months of historical sales at SKU-size-colour level, segmented by SKU or style, size, colour, channel (DTC, wholesale, marketplace), region, and full-price versus markdown period. For wholesale specifically, aggregate confirmed purchase orders by account, ship window and product category. Map confirmed wholesale POs by ship window and add expected reorder probability per account tier, then create a view that separates first orders from replenishment.

Independent retailers now drive growth. Speciality stores and boutiques increased their share of total wholesale transactions on the platform from 49 per cent in 2020 to 62 per cent in 2025, and their presence is even stronger in Europe, the Middle East, and Africa (EMEA) and Asia Pacific (APAC), where they represent 74 per cent and 78 per cent of total sales, respectively. Segment your account base accordingly.

How do you identify seasonal patterns in wholesale order data?

Examine order placement and shipment timing across at least two comparable seasons. Fashion wholesale exhibits clear peaks: search interest for wholesale terms reached its highest point in October 2025, and interest began to pick up from April 2025 and steadily increased through May, June, and July, indicating that buyers start their sourcing research and activities several months before peak demand, with October critical for wholesale buyers to finalise their inventory for the upcoming holiday season.

Lead times have compressed dramatically. The average time from a wholesale order being placed to the product shipping has fallen dramatically, from 263 days in 2019 to just 102 days in 2024. In-season buying is the order of the day, and it is set to be on the rise in 2026, as retailers will try to minimise the risks of unsold inventory. Adjust your planning calendar to reflect this reality: pre-season orders now represent a smaller share of total bookings, with replenishment accounting for more volume.

What growth assumptions should inform your forecast?

Apply different growth rates by account tier and product category. Not all accounts grow at the same rate, and not all categories perform equally. The share of brands developing capsule collections has risen from 19 per cent to 37 per cent in the last two years, and evergreen styles are becoming an increasingly major business driver, growing from 37 per cent of gross merchandise value (GMV) on Joor in 2019 to 49 per cent of GMV in 2024. Evergreens deliver more predictable repeat orders; trend-driven capsules require conservative first-order assumptions.

Consider macro conditions. Revenue growth is expected to stabilise in the low single digits for fashion in 2025, per industry forecasts. Layer in product mix shifts: the share of brands producing full new collections has declined gradually, while those focusing on smaller capsule collections of new styles has risen from 19 per cent to 37 per cent over the past two years. Tighter assortments mean fewer SKUs to forecast but higher stakes for accuracy on each.

How do you handle accounts with limited or no order history?

For new accounts or those with fewer than two seasons of data, when a size or colour has too little history for a stable variant estimate, aggregate only as far as needed: first to a similar colour group or size curve, then to the parent style, category, or an analogous collection, and record the level used and keep the variant allocation rule separate from the total style forecast. Compare the new account to similar accounts by tier, geography or door count, then apply the peer group average as a proxy.

Demand forecasting software for apparel is a system that predicts unit-level demand by style, colour, size, and channel over a forward horizon, using historical sell-through, current on-hand and on-order positions, signed wholesale commitments, and seasonality curves. For wholesale-heavy brands, the critical inputs are channel and signed wholesale commitments, not just aggregate demand.

What adjustments improve forecast accuracy in 2026?

Account for stockouts and censored demand. Mark stockout-censored periods and any lost-sales estimate; observed sales are not complete demand when customers could not buy. If an account ordered 100 units but you shipped only 60 due to inventory constraints, the true demand signal is higher than historical shipments show.

Build flexibility for in-season reorders rather than committing all inventory upfront. Retailer desire has increasingly shifted toward shorter lead times, reshaping expectations around production and delivery timelines, and in 2026, brands should anticipate being asked to hold more stock to facilitate these in-season orders. Reserve a portion of your production capacity or finished goods inventory to meet fill-in demand from top accounts.

Track performance at multiple aggregation levels. Report the measurement level: total collection, style, colour, size, channel, and week can produce very different results, using unit-weighted rollups where appropriate and inspect the segments that drive the total. A forecast that looks accurate in aggregate may hide significant errors at the account or SKU level.

How should the forecast translate into action?

Use the forecast to drive cut quantities, allocation pools and open-to-buy decisions. Overlay DTC unit plan by month and do this in one view, then allocate inventory by channel with explicit reserve rules. Separate your available inventory into committed wholesale, DTC and a reserve pool for in-season opportunities. Update the forecast monthly as new orders arrive and as you observe early sell-through signals from key accounts.

Validate assumptions with buyers. The forecast is a planning tool, not a contract, but sharing projections with strategic accounts can surface misalignment early. If your forecast assumes a 20 per cent increase from an account that is planning flat or down, better to know in June than in September.