Demand Forecasting for Convenience on NCR, built for supply chain
You find out about stockouts after customers do. Ward spots convenience demand movement in your NCR data in time to act, with the cause and what to do about it attached.
The full picture: convenience demand, NCR data, Supply Chain decisions
You find out about stockouts after customers do. Ward pulls forward the indicators that change a supply chain decision.
Demand Forecasting, in one sentence. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
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.
Under the hood. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
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
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
- Store-SKU-day level precision
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 Demand matters for Convenience retail
C-store demand is the most volatile in retail, weather, construction detours, school schedules, and local events can swing traffic dramatically in the same store. Ward builds location-specific models incorporating real-time traffic data, weather forecasts, and event calendars to help operators order precisely for each delivery window.
What Ward has eyes on.
The demand model runs on a daily cycle, not on a reporting calendar. It spots the pattern, explains the cause, and attaches a recommended action before the number reaches a review deck.
Coverage is store by store, category by category. Ward monitors transactions/hour, attach rate, basket size throughout 3,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the footprint is fine and knowing which seven locations are not.
The connection to NCR Voyix is read-only and runs on your schedule. Ward pulls from POS transactions, item-level sales, and the rest of the feed, then joins it with external context your POS does not carry: weather, local events, competitor pricing.
At the metric level. Ward depends on traffic-correlated models, hourly weather impact curves, local event detection, and delivery-window-aware order recommendations. Forecast accuracy is measured by daypart because a model that nails the daily total but misses the morning-to-evening split is useless for a c-store.
Why this combination
is its own problem.
Most demand projects stall at data access. This one does not, because NCR already exposes POS transactions, item-level sales, tender data through an API Ward reads directly.
- 01 Traffic count data is often state-DOT level and weeks delayed; operational decisions need real-time or near-real-time signals.
- 02 Construction and event impact lasts beyond the chain's standard demand-curve lookback, so the system relearns slowly and over-orders for weeks after the cause clears.
Benchmarks. C-store daypart accuracy: 18-30% MAPE for morning rush is healthy; over 35% indicates the model isn't capturing the operational signal. A 5-point daypart accuracy gain typically reduces fresh waste by 20-35% and lifts coffee-attach revenue by 1-3%.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to NCR Voyix with read-only credentials and begins ingesting POS transactions and item-level sales. No config changes on your side. First insight cards arrive inside the first 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 daily cards are directionally right and the thresholds are still moving.
-
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
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate problems announce themselves at the receiving dock
- ×Safety stock levels get set once a year and left alone
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. Source: IHL Group
Convenience KPI impact
Frequently asked questions
Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level. 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 builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
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.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Supply Chain decision-making. Each card includes what changed, why it matters, and what to do next.
Ward depends on traffic-correlated models, hourly weather impact curves, local event detection, and delivery-window-aware order recommendations. Forecast accuracy is measured by daypart because a model that nails the daily total but misses the morning-to-evening split is useless for a c-store.
A highway on-ramp closure reroutes commuters past some of your stores and away from others. Within 48 hours, Ward detects the shift: stores on the new route are depleting morning coffee and breakfast by mid-morning while stores that lost traffic are over-ordering and generating waste. Ward issues demand adjustment cards for all affected locations with revised quantities for the construction period.
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 demand 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.