No more Demand surprises. Ward sees them first.
Insight cards surface Demand patterns your dashboards miss.
Ward's Demand engine for Fashion retail
Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
What changes for your team
- Store-SKU-day level precision
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
Why demand matters
in fashion retail.
Most fashion SKUs have zero sales history — they're new every season, so time-series models fail. Ward takes an attribute-based approach, clustering new styles against historical analogues by silhouette, colorway, price point, and fabric weight, then calibrating in real time as early sell-through data arrives.
Pre-season buy planning, fall collection
The buying team is finalizing quantities for hundreds of new fall styles with no sell-through history. Ward maps each to attribute clusters from prior seasons and adjusts for current trend velocity. The result is store-cluster-level buy recommendations that materially reduce first-allocation error, meaning fewer stockouts on winners and less dead inventory on misses.
What a Ward card looks like.
72-hour heat wave predicted for Dhaka region. Historical model suggests +18% on beverages, +12% on ice cream. Pre-position recommended.
Fashion demand:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Store-SKU-day level precision
- ✓Weather-driven adjustment
- ✓Event and holiday modeling
Fashion KPI impact.
Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
Questions about demand.
TLS 1.3, AES-256 at rest. SOC 2 Type II in progress. On-prem available.
Based on store count and data volume. POC engagements at a fixed fee.
First cards within 48 hours. Robust baselines in roughly 2 weeks.
More Fashion insight cards.
Insights surface
Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.
Insights become actions
Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.
Your team responds
Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.
Outcomes measured
Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.
Agents get sharper
Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.
Fashion retailers: see what Demand problems Ward catches.
Root causes, not just alerts. See it on your data.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches — and the ones your current tools miss.