Stockout Prediction for Fashion & Apparel, scoped to finance
Your P&L surprises are born on the store floor. Ward keeps a running read on stockout throughout 15,000+ fashion SKUs and hands back the finance read on a daily cycle.
Stockout Prediction on a fashion footprint, scoped to finance
What stockout prediction does: Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
In a Fashion & Apparel store base the job is 15,000+ SKUs over locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Your P&L surprises are born on the store floor. Ward writes the finding at the altitude a CFO works at.
How it runs. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
The short list
- Reduce lost sales by catching gaps early
- Automated replenishment recommendations
- Supplier-aware lead time modeling
- Priority ranking by revenue impact
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…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
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" |
Why Stockout matters for 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.
Why this combination
is its own problem.
A CFO in fashion retail owns numbers that move faster than the reporting cycle that covers them. Sell-through rate, markdown %, return rate shift store by store, daily. A monthly pack cannot represent that. Your P&L surprises are born on the store floor.
- 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 Pre-season size curves are set from prior-year history and rarely re-run mid-season, locking in a misread on emerging size demand.
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.
What Ward has eyes on.
Coverage is store by store, category by category. Ward keeps a running read on sell-through rate, markdown %, return rate throughout 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven locations are not.
Know before the shelf empties. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
At the metric level. 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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to whatever holds your transaction and inventory data. Ward starts building baselines the same day. First cards land in two days, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward hands back cards every morning, each with the driver and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many cards arrive, it is how many get acted on.
Your P&L surprises are born on the store floor.
- ×Margin erosion only surfaces at month-end close
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ×Capex decisions for store remodels lack unit-economics evidence
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
- ✓Store-level unit economics cards for capex prioritization
Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group
Fashion KPI impact
Frequently asked questions
Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. For Fashion retail specifically, Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Sell-through rate, Markdown %, Return rate, Style velocity, Size accuracy at the store-category level. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Finance decision-making. Each card includes what changed, why it matters, and what to do next.
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.
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.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
See what Fashion stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
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