A convenience Head of Procurement running stockout on NCR
Merchandising wants Ward. You sign the contract. Ward flags convenience stockout movement in your NCR data while it is still fixable, with the cause and the next step attached.
Stockout Prediction for Convenience on NCR, scoped to procurement
Convenience & C-Store changes the scale of the problem: 3,000+ SKUs, every one of your locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
Merchandising wants Ward. You sign the contract. Ward brings up the indicators that change a procurement decision.
Here is stockout prediction in plain terms. Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
How Ward returns Stockout insight cards: Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
What you get
- Supplier-aware lead time modeling
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
- Automated replenishment recommendations
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.
The procurement problem in convenience retail is not missing data. It is that transactions/hour, attach rate, basket size live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and returns the two-line version.
- 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.
Ward monitors 3,000+ SKUs over your locations, at the store-category level rather than the chain roll-up. The metrics under watch include transactions/hour, attach rate, basket size. A roll-up hides a single-store problem inside a healthy average, which is how daypart demand variation stays invisible for a quarter.
Ward reads NCR rather than replacing it. POS transactions, item-level sales, tender data come across on a read-only connection, get enriched with contextual data, and come back as findings. Your POS is untouched.
Know before the shelf empties. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
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: connect
Read-only credentials to NCR Voyix. Ward ingests POS transactions, item-level sales, tender data and starts building baselines. First cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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 insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Findings arrive on a daily cycle and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
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
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
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.
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 Transactions/hour, Attach rate, Basket size, tailored for Procurement 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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