Ward monitors stockout so your Fashion team can act on it early.
Stockout Prediction at scale, running across your whole Fashion estate without another dashboard to check.
Why stockout matters
in fashion retail.
Fashion stockouts are invisible, they show up as "size not available," not "product missing," and the POS never records the lost sale. Ward monitors sell-through velocity by style-size-color-store and detects when popular size runs are depleting faster than replenishment can cover within the remaining selling window.
Benchmarks. Fashion full-price sell-through targets: 60-75% by week 6, 75-85% by week 10. Broken size runs (a key size missing while others remain) typically affect 15-25% of styles in week 4 and 30-40% by week 8 without active rebalancing.
Mid-season rebalancing, 85-store fashion chain
Ward detects a spring jacket selling far above plan in key sizes at urban stores while sitting in suburban locations. At current velocity, the hot sizes will stock out well before end of season. Ward recommends inter-store transfers from underperforming locations to high-velocity stores, recovering full-price sales that would otherwise become end-of-season markdowns.
Three pitfalls Ward catches
in fashion stockout.
- 01 A style at 82% chain sell-through can be 100% out on size M while size XL sits at 40%, chain averages hide the broken assortment that defines a customer's in-store experience.
- 02 E-commerce inventory pools are counted at the chain level but allocated by warehouse, so "in stock" online routinely cancels because the assigned DC ran dry.
- 03 Pre-season size curves are set from prior-year history and rarely re-run mid-season, locking in a misread on emerging size demand.
How Ward runs stockout
for fashion retailers.
-
01
Track velocity at style-size-color-store grain
Ward establishes per-SKU velocity benchmarks by store cluster and selling week, then projects time-to-stockout per size.
-
02
Recommend inter-store transfers
Cards flag styles where transfers from slow stores to hot stores recover at-risk full-price sales, with the freight cost-benefit calculated.
-
03
Feed back into next-season size curves
Recurring size demand misreads (size M consistently under-allocated in urban cluster) become a calibration input for the next pre-season buy.
What a Ward card looks like.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.
| Signal | Finding |
|---|---|
sell_through | Week 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters |
size_curve | Waist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand |
markdown.ladder | First markdown is 3 weeks later than LY, weeks-of-supply now 11.4 |
Recommend: transfer 30–32 out of the 12 overstocked doors, hold the ladder on core indigo, and take the first markdown on light wash now while it still clears at 20%.
sell_through_weekly…
Dashboards
Pinned views built from saved data-lake queries.
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
shopify_orders_daily | import | 2m ago |
shopify_returns_reasons | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
cegid_store_sales | import | 1h ago |
retail_size_curve_actuals | import | 1h ago |
retail_markdown_ladder | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-markdown | permit | Model::"markdown_ladder" |
vendor-blocked | forbid | Model::"labor_*" |
ecom-read-returns | permit | Model::"returns_reasons" |
Fashion stockout:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Reduce lost sales by catching gaps early
- ✓Automated replenishment recommendations
- ✓Supplier-aware lead time 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 fashion stockout.
Fashion stockouts are invisible, they show up as "size not available," not "product missing," and the POS never records the lost sale. Ward monitors sell-through velocity by style-size-color-store and detects when popular size runs are depleting faster than replenishment can cover within the remaining selling window.
Ward detects a spring jacket selling far above plan in key sizes at urban stores while sitting in suburban locations. At current velocity, the hot sizes will stock out well before end of season.
Requires style-size-color velocity tracking, sell-through benchmarking against plan, inter-store inventory visibility, and time-remaining-in-season context. Ward also flags recurring size curve inaccuracies as a planning problem distinct from replenishment.
First stockout insight cards arrive within 48 hours. Stable fashion baselines form within two weeks.
Fashion stockout
by data source.
More Fashion insight cards.
Fashion retailers: see what stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
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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.