Head of Procurement: fashion fill rate in cards
Merchandising wants Ward. You sign the contract. Ward monitors fill rate over 15,000+ fashion SKUs and hands you the procurement read daily.
Fill Rate Monitoring for Fashion procurement
Fashion & Apparel changes the scale of the problem: 15,000+ SKUs, every one of your locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Merchandising wants Ward. You sign the contract. Ward brings up the inputs that change a procurement decision.
Fill Rate Monitoring is a card type Ward runs continuously. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
What Ward does with that: Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Key capabilities
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
- Estate-wide fill rate dashboard
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 Fill Rate matters for Fashion retail
Fashion fill rate must be measured at the style-size-color level. A store can hold 200 units of a dress and zero in the most popular size, technically "in stock," functionally a stockout. Ward surfaces broken assortments where key sizes are missing from otherwise healthy inventory positions.
What Ward has eyes on.
The fill rate model runs daily, not on a reporting calendar. It detects the pattern, accounts for the cause, and attaches a recommended action before the number reaches a review deck.
Ward watches 15,000+ SKUs over your locations, at the store-category level rather than the chain roll-up. The metrics under watch include sell-through rate, markdown %, return rate. A roll-up hides a single-store problem inside a healthy average, which is how markdown timing stays invisible for a quarter.
At the metric level. Ward tracks style-size-color availability, broken assortment rates, size-level sell-through velocity, and transfer opportunity value. It distinguishes supply-driven stockouts from allocation-driven gaps where inventory exists but sits in the wrong stores.
Why this combination
is its own problem.
Fill Rate Monitoring produces a lot of output that is technically correct and operationally useless to procurement. Ward filters on whether the finding changes a decision a Head of Procurement can actually make.
- 01 OMS shows "in stock" when size XL is the only thing left; conversion craters but the dashboard reports availability.
- 02 Inter-store transfer thresholds are set on freight cost rather than at-risk full-price revenue; the math usually justifies more transfers than ops authorizes.
Benchmarks. Healthy fashion size-level availability runs 85-92% on top styles by week 6 and 70-80% by week 10. Operators with active size rebalancing typically maintain 5-10 percentage points higher size-level availability and recover 2-4 points of full-price sell-through.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward ingests your existing systems on a read-only connection. Nothing is written back. First cards arrive inside the first 48 hours.
-
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 delivers cards on a daily cycle, each with the cause and a recommended action. 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.
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Fashion KPI impact
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. 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 tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks style-size-color availability, broken assortment rates, size-level sell-through velocity, and transfer opportunity value. It distinguishes supply-driven stockouts from allocation-driven gaps where inventory exists but sits in the wrong stores.
Ward reveals that a significant share of top styles have broken size runs across the chain, popular sizes depleted while other sizes sit. Ward recommends urgent inter-store transfers for the highest-revenue styles and a size curve recalibration for the next allocation cycle. Operations executes within 48 hours to protect at-risk revenue.
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 fill rate problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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.