Head of LP: convenience stockout, read from NCR Voyix
Shrinkage costs you more than you think. Ward finds out where. Ward flags convenience stockout movement in your NCR data early, with the driver and a recommended move attached.
Stockout Prediction for Convenience on NCR, scoped to loss prevention
Stockout Prediction, in one sentence. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
A Convenience & C-Store operator is watching 3,000+ SKUs over locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
Shrinkage costs you more than you think. Ward finds out where. Ward raises the readings that change a loss prevention decision.
How Ward hands back Stockout findings: Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
The connection itself. Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
What it does
- Automated replenishment recommendations
- Supplier-aware lead time modeling
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled the 6–9a daypart for the 14 Route 9 sites against the chain baseline. Two causes, one of them scheduling.
| Signal | Finding |
|---|---|
daypart_sales | 6–9a revenue −11% vs. chain, coffee units −18% |
labor_scheduling | Second associate clocks in at 7:30a, peak starts 6:40a at 9 of 14 sites |
foodservice.waste | Breakfast sandwich waste 14%, hold times past 4 hours at 6 sites |
Recommend: move the second open to 6:15a at those nine sites, cut the breakfast batch by one tray, and re-check attach in two weeks.
daypart_sales…
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 |
|---|---|---|
ncr_pos_transactions | import | 2m ago |
pdi_fuel_transactions | import | 2m ago |
verifone_forecourt_events | import | 14m ago |
ncr_planogram_audit | import | 1h ago |
retail_daypart_sales | import | 1h ago |
retail_foodservice_waste | import | 1h ago |
retail_labor_scheduling | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
ops-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fuel-team-forecourt | permit | Model::"fuel_transactions" |
Why Stockout matters for 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.
Why this combination
is its own problem.
A Head of LP in convenience retail owns numbers that move faster than the reporting cycle that covers them. Transactions/hour, attach rate, basket size shift store by store, daily. A monthly pack cannot represent that. Shrinkage costs you more than you think. Ward finds out where.
- 01 DSD direct-store-delivery vendors operate on a fixed cycle; when they short-ship, the gap doesn't surface until the next visit.
- 02 Daily order quantities use chain-average lift factors, missing site-specific events (concerts, sports, construction reroutes) that can swing demand 50-200%.
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.
What Ward has eyes on.
The stockout model runs on a daily cycle, not on a reporting calendar. It picks up the pattern, explains root cause, and attaches what to do about it before the number reaches a review deck.
Every one of your locations gets its own baseline. Ward watches transactions/hour, attach rate, basket size against it and pulls forward only the deviations that hold up. The two that show up most in convenience retail are daypart demand variation and planogram compliance, and both are baseline problems before they are P&L problems.
The connection to NCR Voyix is read-only and runs on your schedule. Ward reads straight from POS transactions, item-level sales, and the rest of the feed, then cross-references it with external context your POS does not carry: weather, local events, competitor pricing.
At the metric level. Requires hourly velocity modeling across dayparts, delivery window alignment, planogram compliance tracking, and weather-adjusted demand curves for beverage and impulse categories.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of NCR Voyix with read-only credentials and begins ingesting POS transactions and item-level sales. No config changes on your side. First daily cards arrive within 48 hours.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward delivers insight cards every morning, 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 insight cards arrive, it is how many get acted on.
How Ward connects to NCR Voyix
Ward integrates with NCR Voyix POS and Aloha for convenience and restaurant retail. Transaction-level data powers daypart analysis and impulse optimization.
Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
Data Ward reads from NCR
Impact metrics with NCR
Data lake enrichment
Ward enriches NCR data with: POS transactions, Weather & events, Loyalty data, Competitor proximity, Demographic data
Shrinkage costs you more than you think. Ward finds out where.
- ×Shrinkage lands as a year-end surprise
- ×Cannot distinguish theft from spoilage from admin error
- ×High-shrinkage stores only identified during audits
- ×No correlation between operational changes and loss patterns
- ×Exception-based reporting misses slow-bleed patterns
- ✓Store-level shrinkage tracking with cause attribution
- ✓Anomaly detection flags stores deviating from estate average
- ✓Receiving dock discrepancy patterns identified automatically
- ✓Correlation analysis links operational changes to loss shifts
- ✓Trend analysis catches slow-bleed patterns audits miss
US retail shrinkage hit $112.1 billion in 2022. Up 19.4% year over year. Source: National Retail Federation
Convenience KPI impact
Frequently asked questions
Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. For Convenience retail specifically, Ward monitors 3,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Transactions/hour, Attach rate, Basket size, Planogram compliance, Daypart mix 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.
Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration. Data points include: POS transactions, Item-level sales, Tender data, Daypart summaries, Loyalty data.
Yes. Ward reads NCR data and combines it with contextual signals (weather, events, demographics) to generate Convenience-specific insight cards. No custom development required.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Requires hourly velocity modeling across dayparts, delivery window alignment, planogram compliance tracking, and weather-adjusted demand curves for beverage and impulse categories.
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.
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 Convenience stockout problems Ward catches.
Root causes, not just alerts. See it on your data.
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