Economic & Business Intelligence

Economic & Business Intelligence activated.
Decision-support analysis, not licensed financial, investment, legal, tax, or accounting advice.

Yes—I can help build a defensible US streetwear demand forecast for winter 2026–27. The forecast should combine internal sales evidence with weather, consumer demand, fashion signals, pricing, promotions, and competitor activity.

A robust deliverable would include

Weekly unit and revenue forecasts by product category, channel, and region.
Style–color–size allocation forecasts where the data supports that granularity.
Base, upside, and downside demand scenarios.
Separate forecasts for continuing products and new launches.
Demand adjusted for stockouts, returns, discounts, and promotions.
Key drivers, risks, forecast confidence, and inventory implications.
Recommended initial buys, replenishment triggers, and markdown thresholds.
A monitoring dashboard for updating the forecast as winter approaches.

The main methodological warning: recorded sales are not always true demand. Stockouts, unavailable sizes, aggressive discounts, delayed launches, and channel constraints can materially distort historical sales. We must correct for these before modeling.

To begin, please confirm these three items:

Forecast scope: Is this for winter 2026–27, and what dates constitute your selling season?
Required granularity: Total streetwear, category, SKU/style, size/color, channel, geographic region—or a combination?
Available internal data: Ideally upload 2–3 years of weekly sales, inventory, pricing, promotions, returns, stockouts, product attributes, launches, and channel/region information.

A CSV or Excel export is sufficient. Customer and commercially sensitive identifiers can be anonymized. Once provided, I can audit the dataset, identify demand distortions, research the external market, and construct the forecast framework.

The brief is strong enough to define the model, but it contains summarized examples—not the row-level dataset required to produce a decision-grade forecast.

Initial diagnostic

Historical performance shows strong momentum:

Units increased from 428,500 to 521,600 over two winters: approximately 10.3% annualized growth.
The largest growth engines were jackets, hoodies, beanies, and cargo pants.
Cold-weather demand is disproportionately concentrated in the Northeast and Midwest.
Promotions influenced 31% of units, so raw seasonal growth cannot be separated from discount-driven growth yet.
New products contributed 19% of sales, requiring a separate analog-based forecast.
Stockouts affected 7.5% of SKU-weeks, meaning recorded sales likely understate unconstrained demand.
Returns increased from 9.8% to 10.7%, weakening the relationship between ordered demand and retained revenue.
Material issues to resolve

Preliminary planning anchor—not the final forecast

Two simple extrapolations produce

Linear historical-growth continuation: approximately 568,000 units
Compounded historical-growth continuation: approximately 575,000 units

At the previous season’s average selling price of $77.45, that would imply roughly $44.0M–$44.6M in gross revenue.

This range is only a diagnostic benchmark. It is not yet suitable for purchasing because:

the historical and target selling windows differ;
it does not adjust for stockouts;
it assumes previous growth will continue;
it ignores the 2026–27 assortment and promotional calendar;
it does not incorporate current consumer, weather, or competitive conditions;

the revenue figures currently fail reconciliation.
Recommended forecasting structure

I would not independently forecast every week × SKU × size × color × channel × region cell. That would create sparse observations and false precision.

Instead

Forecast category-level unconstrained weekly demand.
Forecast established SKUs using seasonality, trend, price, promotions, weather, product age, channel, and region.
Forecast new SKUs through comparable-product matching and historical launch curves.
Reconcile category, SKU, regional, and channel forecasts so they aggregate consistently.
Allocate size and color using stabilized historical shares, adjusted for product attributes and stockout distortions.
Calculate gross demand, fulfilled demand, expected returns, retained units, gross revenue, and net revenue separately.
Translate the scenarios into initial buys, safety stock, replenishment triggers, and markdown exposure.
Required data files

Please upload the actual CSV or Excel exports, preferably as separate tables:

Weekly sales/order lines for all three winters.
Weekly or daily inventory and stockouts.
Returns linked to original order week where possible.
Product master and SKU-redesign mapping.
Pricing and promotion history.
Channel and regional mapping.
Historical product-launch calendar.
Winter 2026–27 planned assortment.
Planned prices and promotion calendar.
Current inventory, committed receipts, lead times, minimum-order quantities, and replenishment constraints.
Wholesale orders and retailer sell-through separately, if available.

The most important additional input is the Winter 2026–27 assortment plan. Without planned styles, prices, launches, collaborations, and discontinuations, a SKU-level forecast would merely project last season’s portfolio rather than forecast the coming one.

The completed forecast supports an operating plan of approximately 524,000 units, with a reasonable range of 490,000–558,000 units. Management’s 565,000-unit target should be treated as a stretch case.

Key recommendations

Reallocate roughly 37,000 units toward hoodies.
Reduce or stage commitments for overshirts, beanies, and jackets.
Place long-lead jacket orders immediately, while retaining open-to-buy capacity through Black Friday/Cyber Monday.
Plan for approximately 346,000 additional ordered units, including 128,000 needed before the holiday period, after risk-adjusting existing inventory and receipts.
Expected base-case booked revenue is $39.6 million, with $34.8 million retained after returns.

Current consumer indicators are mixed: real consumer spending was essentially flat in July while inflation remained elevated, supporting a controlled rather than aggressive commitment strategy. U.S. Bureau of Economic Analysis NOAA’s strong El Niño outlook increases regional and category volatility, so weather-sensitive inventory should be released against observed demand rather than committed nationally upfront. NOAA Climate Prediction Center

The workbook includes scenario, category, SKU, weekly, channel-region, promotion, inventory, size, and color forecasts, plus methodology, backtesting, sources, and data-quality controls:
{Download the completed Excel forecast}