Convenience · Fill Rate · NCR

Convenience fill rate without leaving NCR

Ward reads straight from POS transactions, item-level sales, tender data from NCR Voyix and hands you convenience fill rate daily cards on a daily cycle. Read-only, no config changes.

How Ward turns NCR data into convenience fill rate

Fill Rate Monitoring, in one sentence. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.

For Convenience & C-Store retailers, that means tracking 3,000+ SKUs throughout locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.

The mechanism. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.

Getting the data in. Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.

Capabilities

  • Category-level drill-down
  • Estate-wide fill rate dashboard
  • Threshold-based alerting
  • Store-vs-estate benchmarking
app.getward.ai Live demo
Acme Convenience @Store Ops: Regional Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why is the morning daypart down at the Route 9 sites?
You · 9:42 AM
Schema Scout · routed to Store Ops Agent

I pulled the 6–9a daypart for the 14 Route 9 sites against the chain baseline. Two causes, one of them scheduling.

SignalFinding
daypart_sales6–9a revenue −11% vs. chain, coffee units −18%
labor_schedulingSecond associate clocks in at 7:30a, peak starts 6:40a at 9 of 14 sites
foodservice.wasteBreakfast 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.

7 parallel queries 3 sources cited confidence 0.91
Show me the schedule change by site.
You · 9:43 AM
Labor Agent · drafting shift diff
Querying daypart_sales
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Inside-store sales
$12.9M
+2.4% WoW
Foodservice margin
47.3%
−2.1pp
Attach, fuel-to-store
31.2%
−1.6pp
Shrink, cigarettes
1.94%
+0.6pp
Inside-store sales 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
ncr_pos_transactionsimport2m ago
pdi_fuel_transactionsimport2m ago
verifone_forecourt_eventsimport14m ago
ncr_planogram_auditimport1h ago
retail_daypart_salesimport1h ago
retail_foodservice_wasteimport1h ago
retail_labor_schedulingimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
ops-read-defaultpermitModel::*
lp-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
fuel-team-forecourtpermitModel::"fuel_transactions"
Fill Rate for Convenience on NCR data, live product demo.

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.

Coverage is store by store, category by category. Ward scans continuously transactions/hour, attach rate, basket size over 3,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven locations are not.

Ward reads 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.

The fill rate model runs every morning, not on a reporting calendar. It flags the pattern, traces the driver, and attaches the next step before the number reaches a review deck.

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.

Signals · POS at hour-store-SKU, current inventory, delivery schedules (central and DSD), category margin overlay, and planogram compliance reports.

Why this combination
is its own problem.

NCR Voyix is the system of record for most convenience operators of your size, which means the readings Ward needs are already there. The gap is not data collection. It is that nobody has time to read POS transactions and item-level sales every morning throughout every store.

  • 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.

  1. 01

    Week 1: connect

    Read-only credentials to NCR Voyix. Ward reads POS transactions, item-level sales, tender data and starts building baselines. First insight cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.

  2. 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 daily cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: operating rhythm

    Insight cards 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

POS transactions
Item-level sales
Tender data
Daypart summaries
Loyalty data

Impact metrics with NCR

Attach Rate
Adjacencies mapped per daypart
Impulse cross-sell patterns identified by time of day.
Daypart Revenue
Underperforming hours exposed
Traffic and weather data pinpoint revenue-light dayparts.
Shrinkage
Slow-bleed loss detected
POS anomaly patterns caught that periodic audits miss.
Basket Size
Bundle opportunities surfaced
Item-level sales mined for actionable upsell patterns.

Data lake enrichment

Ward enriches NCR data with: POS transactions, Weather & events, Loyalty data, Competitor proximity, Demographic data

Convenience KPI impact

Attach Rate
Impulse adjacencies
Daypart-specific cross-sell opportunities surfaced.
Daypart Revenue
Weak hours identified
Which hours and categories underperform, and why.
Planogram Compliance
Sales-correlated flags
Deviations flagged once they start costing revenue.
Shrinkage
Slow-bleed detection
Transaction-level anomalies that periodic audits miss.

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.

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.

See what Convenience fill rate problems Ward catches.

Root causes, not just alerts. See it on your data.

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

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.

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