Ward detects. You decide. Stockout Prediction for Convenience.
Ward delivers stockout findings as insight cards with recommended actions.
Why stockout matters
in convenience retail.
The c-store value proposition is instant availability, a customer who can't find their energy drink drives to the next location, not to the next aisle. Ward models hourly sell-through by daypart, traffic flow, weather, and local events to predict which SKUs will empty before the next delivery window.
Industry benchmarks
C-store top-50 SKUs cover 35-55% of inside-store revenue. Healthy availability on the top-50 runs 95-98%; each percentage drop maps to roughly 0.4-0.7% inside-store revenue loss because of basket-walk-away.
Friday night energy drink rush, 340-store chain
Ward detects energy drink velocity running well above normal at university-adjacent stores during homecoming weekend, an event its model picked up from local data. Standard delivery won't replenish until Monday. Ward issues stockout prediction cards for the affected stores and recommends emergency redistribution from lower-velocity suburban locations to protect weekend revenue.
What Ward actually tracks
Requires hourly velocity modeling across dayparts, delivery window alignment, planogram compliance tracking, and weather-adjusted demand curves for beverage and impulse categories.
Data signals
POS at hour-store-SKU, current on-hand, DSD delivery schedules, weather forecasts, traffic counts, and local event feeds tied to store geocodes.
Three pitfalls Ward catches
in convenience stockout.
- 01 Daily order quantities use chain-average lift factors, missing site-specific events (concerts, sports, construction reroutes) that can swing demand 50-200%.
- 02 DSD direct-store-delivery vendors operate on a fixed cycle; when they short-ship, the gap doesn't surface until the next visit.
- 03 Stockouts in front-of-store impulse hit fuel-attach revenue more than POS revenue suggests; the basket effect isn't modeled.
How Ward runs stockout
for convenience retailers.
-
01
Model demand at the hour-store-SKU grain
Ward fits demand using historical hourly POS, weather, traffic, and local event signals, producing a per-hour expected velocity by store.
-
02
Align to the actual delivery cadence
Each prediction is run against the next confirmed delivery window so stockouts are flagged only when the next replenishment misses them.
-
03
Trigger emergency action
High-velocity stockouts trigger inter-store transfer suggestions or DSD vendor escalation cards before the gap forms on shelf.
What a Ward card looks like.
23 SKUs trending toward zero-on-hand within 48 hours. Replenishment recommendation attached. Priority: dairy and produce categories.
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.
Convenience stockout:
the shift.
- ×Daypart demand variation
- ×Planogram compliance
- ×Impulse category optimization
- ✓Reduce lost sales by catching gaps early
- ✓Automated replenishment recommendations
- ✓Supplier-aware lead time modeling
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 stockout.
The c-store value proposition is instant availability, a customer who can't find their energy drink drives to the next location, not to the next aisle. Ward models hourly sell-through by daypart, traffic flow, weather, and local events to predict which SKUs will empty before the next delivery window.
Ward detects energy drink velocity running well above normal at university-adjacent stores during homecoming weekend, an event its model picked up from local data. Standard delivery won't replenish until Monday. Ward issues stockout prediction cards for the affected stores and recommends emergency redistribution from lower-velocity suburban locations to protect weekend revenue.
Requires hourly velocity modeling across dayparts, delivery window alignment, planogram compliance tracking, and weather-adjusted demand curves for beverage and impulse categories.
First stockout 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.
Convenience stockout
by data source.
More Convenience insight cards.
Convenience retailers: see what stockout 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.