Warm September slows autumn clothing sales as British retail volumes slip

Autumn stock reached the shelves, but the weather gave shoppers little reason to buy it. Britain's September retail figures, released on 23 October, point to a difficult start for seasonal clothing and a more complicated picture than a simple slowdown across the high street.

The Office for National Statistics estimates that retail sales volumes fell 0.4% in September after a revised 0.7% increase in August. Over the third quarter, however, volumes were 0.7% higher than in the previous three months and 1.5% above the same quarter of 2025. These are figures for Great Britain, including automotive fuel.

Online clothing retailers told the ONS that unusually warm weather affected autumn and winter launches. Supermarket volumes also declined after two months of growth. Non-food stores recorded a monthly fall of 0.4%, with clothing, footwear, sports equipment and hardware among the weaker areas. The ONS cautions that monthly movements can be volatile.

The important question is what has happened to the stock

For a clothing wholesaler, a late seasonal start creates two possible problems. Retail customers may be carrying unsold opening orders, while the wholesaler still has replenishment stock due from suppliers. If both businesses assume demand will simply arrive a few weeks late, they can end up holding more of the same range than the remaining season can absorb.

That is an operational interpretation of the figures, not an ONS forecast. The release does not establish that a particular jacket, retailer or region is overstocked. Buyers need their own sales and availability records to make that decision.

A useful review separates opening orders already delivered, firm customer commitments still outstanding, and speculative replenishment. Those quantities have different levels of commercial flexibility. A customer delaying a new order is not the same as cancelling a confirmed one, and neither changes a supplier's agreed production commitment automatically.

A late start needs a dated decision

Consider an illustrative wholesaler holding 600 winter jackets. Its retail customers normally take 100 a week, but current orders are running at 60. The stock now represents ten weeks of demand at that slower rate, rather than six. That calculation does not predict future sales. It shows how much depends on the expected recovery.

The buying team can set a review date before its next cancellable inbound order becomes firm. Sales staff collect customer-level evidence: remaining opening stock, weekly sell-through and any confirmed promotional plans. The planner then produces a base case and a weaker-demand case. Purchasing acts within the cancellation and delivery terms already agreed with the supplier.

This is more useful than ordering against last year's calendar alone. It also avoids asking the warehouse to solve an assortment decision by accepting whichever cartons happen to arrive.

Keep strong lines available while reviewing the weak ones

An overall category decline can hide a product that is selling well. Cutting every replenishment order by the same percentage could leave the business short of popular sizes while retaining slow colours and expensive variants.

Review at the level where the buying decision can actually change: style, size, price band and customer group. Compare units sold with units available throughout the period. A line that sold little because it was unavailable needs different treatment from one that sat on the shelf.

Wholesalers should also agree how they will communicate any revised delivery dates or promotional offers. The commercial team owns customer commitments; purchasing owns supplier amendments; finance checks the margin effect of clearance proposals. The seasonal calendar keeps moving even when the weather does not cooperate. The valuable decision is how much stock can still sell profitably within the time that remains.

Source: ONS, Retail sales, Great Britain: September 2026, released 23 October 2026. The stock example and operational analysis are illustrative.