Assortment Planning for Convenience on NCR, built for finance
A convenience CFO on NCR should not be hunting for assortment problems. Ward pulls forward them every morning.
How a convenience CFO runs assortment on NCR Voyix
For Convenience & C-Store retailers, that means continuous review 3,000+ SKUs over 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 hands you cards scoped to finance decision-making.
What assortment planning does: Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
How it runs. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
The connection itself. Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
The short list
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
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 Assortment matters for Convenience retail
With 3,000 SKUs on a compact selling floor, every product must earn its place, and the right assortment is hyper-local. Ward clusters stores by traffic profile, daypart mix, and surrounding demographics to recommend variations that maximize revenue per square foot at each location.
What Ward has eyes on.
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 assortment model runs each day, not on a reporting calendar. It flags the pattern, accounts for the driver, and attaches a recommended move before the number reaches a review deck.
Ward watches 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.
At the metric level. Ward tracks revenue per facing, velocity by daypart and cluster, redundancy analysis, and attach-rate contribution. It also monitors new-item performance against the displaced SKU to measure true assortment productivity.
Why this combination
is its own problem.
A generic assortment model applied to a convenience fleet produces alerts nobody trusts. The thresholds are wrong, because convenience baselines are wrong for it. Ward learns the baseline from your own locations instead of importing one.
- 01 Chain-wide planograms over-allocate space to slow tail SKUs in high-volume stores and starve depth on the items that drive 60% of basket starts.
- 02 New-item performance is measured against the new item's standalone sales without accounting for what was displaced; net assortment productivity often goes backwards.
Benchmarks. C-store top-200 SKUs typically generate 50-65% of inside revenue. Cluster-aware planograms usually free 10-20% of facings without revenue loss, redirecting that space to higher-velocity items and lifting same-store inside revenue 2-5%.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to NCR Voyix. Ward reads POS transactions, item-level sales, tender data and starts building baselines. First daily 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
Insight 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
Your P&L surprises are born on the store floor.
- ×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
- ✓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
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
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 revenue per facing, velocity by daypart and cluster, redundancy analysis, and attach-rate contribution. It also monitors new-item performance against the displaced SKU to measure true assortment productivity.
A standardized planogram runs across all 500 locations. Ward identifies distinct store clusters, highway/travel, urban commuter, residential, university-adjacent, each overindexing on different categories. Ward recommends reallocating shelf space per cluster to match actual demand. Pilot stores show meaningful revenue uplift from better product-location matching with zero cost increase: same SKU count, just the right ones in the right stores.
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 assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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