A convenience Director Store Ops running fill rate on NCR
Every empty shelf is a lost sale. Ward runs it on NCR data across your convenience estate, scoped to store operations.
How a convenience Director Store Ops runs fill rate on NCR Voyix
What fill rate monitoring does: Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
Applied to Convenience & C-Store, the surface area is 3,000+ SKUs across locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
Managing 800 stores from a spreadsheet is insane. Ward brings up the readings that change a store operations decision.
Under the hood. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
Capabilities
- Threshold-based alerting
- Store-vs-estate benchmarking
- Category-level drill-down
- Estate-wide fill rate dashboard
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 Fill Rate matters for 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.
What Ward has eyes on.
Ward reads straight from POS transactions, item-level sales, tender data from NCR Voyix on a read-only connection. Nothing is written back, and your POS configuration does not change. NCR stays the system of record.
Every empty shelf is a lost sale. 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.
Every one of your locations gets its own baseline. Ward tracks 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.
At the metric level. Ward tracks inter-delivery depletion velocity, delivery-window-aware stockout prediction, high-margin category availability, and planogram compliance as a proxy for visual availability.
Why this combination
is its own problem.
NCR Voyix was built for transactions, not for store operations decisions. The data is right there and it is in the wrong shape. Ward reads straight from POS transactions and item-level sales and rewrites them as a decision.
- 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 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.
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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads from NCR Voyix with read-only credentials and begins ingesting POS transactions and item-level sales. No config changes on your side. First findings arrive within 48 hours.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: operating rhythm
Cards arrive daily 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
Managing 800 stores from a spreadsheet is insane.
- ×Morning check-ins rely on phone calls and email chains
- ×No single view of which stores need attention today
- ×Labor scheduling is disconnected from demand signals
- ×Planogram compliance is checked manually, quarterly
- ×Exception management is reactive and inconsistent
- ✓Morning brief delivered at 06:47 with prioritized action list
- ✓Estate-wide heat map of store performance, updated hourly
- ✓Staffing recommendations correlated with predicted traffic
- ✓Planogram compliance anomalies detected and flagged
- ✓Consistent exception handling with recommended actions
Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research
Convenience KPI impact
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. 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 tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
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.
Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Store Operations decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks inter-delivery depletion velocity, delivery-window-aware stockout prediction, high-margin category availability, and planogram compliance as a proxy for visual availability.
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.
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
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