Convenience · Oracle · Head of Procurement

Convenience Oracle data, read for procurement

Your Oracle feed covers 3,000+ SKUs across your locations. Ward reads it for procurement.

What a convenience Head of Procurement gets from Oracle Retail

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.

Merchandising wants Ward. You sign the contract. Ward raises the signals that change a procurement decision.

Getting the data in. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.

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"
for Convenience on Oracle data, live product demo.

What Ward delivers

  • MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
  • Month-to-month contracts. No multi-year lock-in. No auto-renew traps
  • Transparent pricing set by scope and store count
  • 14-day insight guarantee. If Ward doesn't deliver, month two is on us
  • AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature

Metrics Ward monitors

Transactions/hour
Attach rate
Basket size
Planogram compliance
Daypart mix

Convenience challenges Ward solves

  • Daypart demand variation
  • Planogram compliance
  • Impulse category optimization
  • Fuel attach rate
  • Labor scheduling

What Ward has eyes on.

Ward pulls from Oracle rather than replacing it. Sales audit, inventory positions, allocation come across on a read-only connection, get enriched with contextual data, and come back as cards. Your ERP is untouched.

Every one of your locations gets its own baseline. Ward watches transactions/hour, attach rate, basket size against it and surfaces only the deviations that hold up. The two that show up most in convenience retail are daypart demand variation and planogram compliance, and both are baseline problems before they are P&L problems.

Why this combination
is its own problem.

The procurement problem in convenience retail is not missing data. It is that transactions/hour, attach rate, basket size live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and hands you the two-line version.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Oracle Retail. Ward reads straight from sales audit, inventory positions, allocation 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 cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: steady state

    Ward hands back daily cards every morning, each with root cause and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many findings 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

Sales audit
Inventory positions
Allocation
Replenishment
Demand forecasts
Price management

Impact metrics with Oracle

Fill Rate
Allocation gaps caught
Replenishment outputs checked against actual shelf conditions per store.
Demand Forecast Accuracy
Accuracy gap closed
External signals enrich Oracle forecasts where they drift.
Markdown Waste
Slow movers caught early
Triggers shallower markdowns before inventory ages out.
Inventory Carrying Cost
Overstock freed up
Demand-aligned inventory releases locked working capital.

Data lake enrichment

Ward enriches Oracle data with: Sales audit data, Weather & events, Competitor pricing, Demographic data, Supplier scorecards

Merchandising wants Ward. You sign the contract.

Pain points
  • ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
  • ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
  • ×Multi-year commits with auto-renew. No exit if the pilot stalls
  • ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
  • ×Security and DPA reviews start after the team has already committed
How Ward helps
  • MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
  • Month-to-month contracts. No multi-year lock-in. No auto-renew traps
  • Transparent pricing set by scope and store count
  • 14-day insight guarantee. If Ward doesn't deliver, month two is on us
  • AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature

Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner

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 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.

Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.

Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.

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 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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info