The fill rate problem, solved. Ward for Home.
No dashboards. No queries. Fill Rate findings delivered every morning.
Why fill rate matters
in home retail.
A store can report 96% fill rate while missing the one fastener that completes every deck project basket. Ward monitors fill rate through a project-basket lens, flagging when project-critical items drop below threshold even if aggregate availability looks healthy.
Industry benchmarks
Home improvement project-basket completion: healthy chains run 88-94% on the top-100 project baskets; below 80% on a top basket cuts category revenue 3-7% over the affected weeks. Pro customer fill rate matters disproportionately, Pros typically defect after 2-3 stockouts on flagged SKUs.
Project-basket fill rate alert, outdoor season
Estate-wide fill rate looks healthy, but Ward's project-basket analysis shows the "deck build" basket has far lower complete-basket availability because a single specialty fastener is out of stock. A standard fill rate report would bury this item among 50,000 others. Ward surfaces it through basket completion analysis, and the supply chain team expedites the item to restore project-level availability within days.
What Ward actually tracks
Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Data signals
POS at SKU-store-day with basket linkage, current inventory positions, Pro account order history, planogram and endcap positions, and project basket affinity graph.
Three pitfalls Ward catches
in home fill rate.
- 01 Fill rate measured at the SKU level masks project-basket completion; a deck-project basket that needs 12 SKUs has a much lower complete-basket availability than any individual SKU's availability suggests.
- 02 Pro customer order fill rate is benchmarked against estate-wide DIY fill, hiding the much steeper Pro defection curve when a flagged SKU goes out of stock.
- 03 Endcap and seasonal positioning is tracked separately from fill rate, but the customer experience is shaped by both, a fully-stocked product hidden in a non-seasonal aisle still functions like a stockout.
How Ward runs fill rate
for home retailers.
-
01
Define top-100 project baskets
Ward identifies the most common multi-SKU project baskets (deck build, bathroom remodel, paint refresh) and tracks completion availability per basket per store.
-
02
Surface gaps at the basket level
Cards flag stores where a single missing SKU drops a top-basket completion below threshold, even if the individual SKU is low-velocity standalone.
-
03
Pro customer fill rate gets separate treatment
Ward tracks Pro order fill separately from DIY and triggers higher-priority alerts because Pro defection on stockouts is faster and more permanent.
What a Ward card looks like.
Estate fill rate at 94.2%, up 1.2pp vs last week. Stores 22 and 37 dropped below 85% threshold. Fresh produce is the driver.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
Light and dark themes are available. Your choice is remembered per browser.
Home fill rate:
the shift.
- ×Project basket identification
- ×Seasonal pre-positioning
- ×Long-tail inventory
- ✓Estate-wide fill rate dashboard
- ✓Threshold-based alerting
- ✓Store-vs-estate benchmarking
Home KPI impact.
Ward requires 6\u201312 months to baseline seasonal categories. Pro vs DIY segment separation is critical for accurate modeling.
Questions about home fill rate.
A store can report 96% fill rate while missing the one fastener that completes every deck project basket. Ward monitors fill rate through a project-basket lens, flagging when project-critical items drop below threshold even if aggregate availability looks healthy.
Estate-wide fill rate looks healthy, but Ward's project-basket analysis shows the "deck build" basket has far lower complete-basket availability because a single specialty fastener is out of stock. A standard fill rate report would bury this item among 50,000 others. Ward surfaces it through basket completion analysis, and the supply chain team expedites the item to restore project-level availability within days.
Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
First fill rate insight cards arrive within 48 hours. Robust home baselines form within two weeks. Ward requires 6\u201312 months to baseline seasonal categories. Pro vs DIY segment separation is critical for accurate modeling.
Home fill rate
by data source.
More Home insight cards.
Home retailers: see what fill rate 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.