Convenience · Fill Rate · NCR · CFO

Fill Rate Monitoring for Convenience on NCR, built for finance

Ward reads POS transactions, item-level sales, tender data from NCR Voyix, watches fill rate over 3,000+ convenience SKUs, and hands back the finance read daily.

Fill Rate Monitoring for Convenience on NCR, scoped to finance

Here is fill rate monitoring in plain terms. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.

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.

Your P&L surprises are born on the store floor. Ward writes the finding at the altitude a CFO works at.

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

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

  • Store-vs-estate benchmarking
  • Category-level drill-down
  • Estate-wide fill rate dashboard
  • Threshold-based alerting
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.

The fill rate model runs each day, not on a reporting calendar. It picks up the pattern, traces the driver, and attaches a recommended move before the number reaches a review deck.

Ward scans continuously 3,000+ SKUs throughout 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.

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. 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.

Running Ward on NCR in a convenience estate skips the usual first step. There is no ingestion project, because NCR Voyix already holds POS transactions, item-level sales, tender data. Ward pulls from what is there.

  • 01 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.
  • 02 DSD vendor stockouts (Frito, Coca-Cola, Anheuser) sit outside the central inventory system and only surface as POS gaps after the fact.

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 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: 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.

  3. 03

    Weeks 4 to 12: operating rhythm

    Daily cards arrive each day 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

Your P&L surprises are born on the store floor.

Pain points
  • ×Margin erosion only surfaces at month-end close
  • ×Inventory carrying costs are a black box
  • ×Working capital tied up in slow-moving stock nobody is watching
  • ×Same-store sales comps lack decomposition into actionable drivers
  • ×Capex decisions for store remodels lack unit-economics evidence
How Ward helps
  • GMROI tracking by category with weekly insight cards
  • Inventory carrying cost alerts when capital efficiency drops
  • Working capital optimization recommendations based on turnover trends
  • SSS decomposition into traffic, conversion, and basket components
  • Store-level unit economics cards for capex prioritization

Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group

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.

Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.

Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Finance 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.

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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