A convenience Head of IT running assortment on Oracle
The business wants AI. You sign off on the architecture. Ward detects convenience assortment movement in your Oracle data before it compounds, with the driver and a recommended move attached.
Assortment Planning for Convenience on Oracle, scoped to technology
For Convenience & C-Store retailers, that means monitoring 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.
The business wants AI. You sign off on the architecture. Ward sends cards scoped to technology decision-making.
What assortment planning does: Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Under the hood. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Getting the data in. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Capabilities
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
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.
The connection to Oracle Retail is read-only and runs on your schedule. Ward ingests sales audit, inventory positions, and the rest of the feed, then stitches together it with external context your ERP does not carry: weather, local events, competitor pricing.
Stock what sells. Cut what doesn't. 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.
Coverage is store by store, category by category. Ward monitors transactions/hour, attach rate, basket size across 3,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven locations are not.
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.
Assortment Planning needs sales audit and inventory positions at minimum. Oracle Retail carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
- 01 Daypart adjacency (breakfast next to coffee) is a layout decision but isn't modeled in the assortment math; it shows up as "low SKU productivity" when really it's a placement issue.
- 02 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.
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 Oracle Retail. Ward ingests sales audit, inventory positions, allocation and starts building baselines. First findings land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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: steady state
Ward delivers daily cards daily, each with what caused it and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.
How Ward connects to Oracle Retail
Ward integrates with Oracle Retail Merchandising (RMFCS), Oracle Retail Demand Forecasting, and Oracle Retail Analytics. Full stack visibility.
Setup: Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Data Ward reads from Oracle
Impact metrics with Oracle
Data lake enrichment
Ward enriches Oracle data with: Sales audit data, Weather & events, Competitor pricing, Demographic data, Supplier scorecards
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
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 from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments. Data points include: Sales audit, Inventory positions, Allocation, Replenishment, Demand forecasts, Price management.
Yes. Ward reads Oracle data and combines it with contextual signals (weather, events, demographics) to generate Convenience-specific insight cards. No custom development required.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Technology 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
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Root causes, not just alerts. See it on your data.
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