Convenience fill rate: insight cards, not dashboards.
Convenience data into fill rate insight cards. What changed. Why. What to do.
Why fill rate matters
in convenience retail.
With replenishment only 2-3 times per week, a Tuesday stockout might not resolve until Thursday. Ward monitors sell-through velocity between delivery windows and predicts which items will deplete before the next drop, giving operators time to adjust orders or arrange emergency fills on high-margin categories.
Industry benchmarks
C-store inside fill rate: top-50 SKUs at 95-98% is healthy. Below 92% on high-margin items (tobacco, beverages, premium beer) maps to roughly 0.6-1.0% category revenue erosion per missing point. Between-delivery depletion is the single biggest fill rate driver in c-store.
Between-delivery fill rate management, 280 stores
Mid-week with the next delivery two days out, Ward detects dozens of stores on pace to stock out on top tobacco SKUs, a category representing a major share of inside gross profit. Ward issues fill rate alerts with recommended emergency orders from the nearest distribution point. Store managers receive automated alerts with pre-built order lists.
What Ward actually tracks
Ward tracks inter-delivery depletion velocity, delivery-window-aware stockout prediction, high-margin category availability, and planogram compliance as a proxy for visual availability.
Data signals
POS at hour-store-SKU, current inventory, delivery schedules (central and DSD), category margin overlay, and planogram compliance reports.
Three pitfalls Ward catches
in convenience fill rate.
- 01 Average daily depletion masks the daypart spike that empties shelves before the next delivery; high-margin tobacco and beverages can run out by midweek even when the daily total is on plan.
- 02 DSD vendor stockouts (Frito, Coca-Cola, Anheuser) sit outside the central inventory system and only surface as POS gaps after the fact.
- 03 Planogram compliance is checked weekly, but a misexecuted reset can leave a hot SKU in the wrong slot for days, depressing sales without registering as a stockout.
How Ward runs fill rate
for convenience retailers.
-
01
Project depletion to next delivery window
Ward computes per-SKU per-store time-to-zero using current velocity and on-hand, mapped against the actual confirmed delivery schedule.
-
02
Prioritize by margin at risk
Cards rank predicted stockouts by category margin and basket-pull effect, focusing operator attention on the SKUs that move P&L.
-
03
Trigger emergency fills selectively
For high-margin gaps, Ward suggests inter-store transfers or emergency vendor orders with the freight-vs-revenue math exposed.
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.
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Convenience fill rate:
the shift.
- ×Daypart demand variation
- ×Planogram compliance
- ×Impulse category optimization
- ✓Estate-wide fill rate dashboard
- ✓Threshold-based alerting
- ✓Store-vs-estate benchmarking
Convenience KPI impact.
Value compounds across multi-site operators. Chains with 100+ locations see the strongest returns. Fuel-dominant locations should expect impact concentrated on forecourt-to-store attach rate.
Questions about convenience fill rate.
With replenishment only 2-3 times per week, a Tuesday stockout might not resolve until Thursday. Ward monitors sell-through velocity between delivery windows and predicts which items will deplete before the next drop, giving operators time to adjust orders or arrange emergency fills on high-margin categories.
Mid-week with the next delivery two days out, Ward detects dozens of stores on pace to stock out on top tobacco SKUs, a category representing a major share of inside gross profit. Ward issues fill rate alerts with recommended emergency orders from the nearest distribution point. Store managers receive automated alerts with pre-built order lists.
Ward tracks inter-delivery depletion velocity, delivery-window-aware stockout prediction, high-margin category availability, and planogram compliance as a proxy for visual availability.
First fill rate insight cards arrive within 48 hours. Robust convenience baselines form within two weeks. Value compounds across multi-site operators. Chains with 100+ locations see the strongest returns. Fuel-dominant locations should expect impact concentrated on forecourt-to-store attach rate.
More Convenience insight cards.
Convenience 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.