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Home Oracle data, read for supply chain

Ward reads Oracle Retail and hands you home findings scoped to supply chain. You find out about stockouts after customers do.

What a home VP Supply Chain gets from Oracle Retail

You find out about stockouts after customers do. Ward hands you findings scoped to supply chain decision-making.

For Home Improvement retailers, that means watching 50,000+ SKUs over stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.

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 Home @Merchandising: Seasonal Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did the spring mulch pre-build miss in the Southeast?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled the spring pre-build against the last three seasons and the weather signal for the 22 Southeast stores.

SignalFinding
seasonal_prebuildMulch on-hand hit 61% of plan the week temps broke 70°F
dc_pushDC push started 9 days after the first-warm-week trigger, LY it was 2
attach.projectSoil and edging attach fell 18% at stores that gapped on mulch

Recommend: tie the push trigger to the 10-day forecast instead of the calendar week, pre-position two truckloads at the 8 stores that gapped, and re-set the attach endcap.

9 parallel queries 4 sources cited confidence 0.88
Which stores get the pre-position first?
You · 9:43 AM
Supply Chain Agent · ranking stores
Querying seasonal_prebuild
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
Revenue vs. plan
$57.3M
+2.9% WoW
Gross margin, seasonal
31.2%
−2.4pp
Mulch on-hand vs plan
61.4%
−18pp
Special order cycle time
11.6 d
+3.1 days
Revenue vs. plan 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
epicor_pos_transactionsimport2m ago
epicor_special_ordersimport2m ago
netsuite_inventory_snapshotimport14m ago
retail_seasonal_prebuildimport1h ago
retail_sku_velocityimport1h ago
retail_pro_account_salesimport1h ago
retail_weather_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
pro-team-read-accountspermitModel::"pro_account_sales"
vendor-blockedforbidModel::"labor_*"
supply-read-orderspermitModel::"special_orders"
for Home on Oracle data, live product demo.

What Ward delivers

  • 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

Metrics Ward monitors

Project basket value
Seasonal accuracy
Long-tail turn
Pro customer share
Attachment rate

Home challenges Ward solves

  • Project basket identification
  • Seasonal pre-positioning
  • Long-tail inventory
  • Pro vs DIY segmentation
  • Weather-driven demand

Why this combination
is its own problem.

The supply chain problem in home retail is not missing data. It is that project basket value, seasonal accuracy, long-tail turn 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 Ward has eyes on.

Every one of your stores gets its own baseline. Ward scans continuously project basket value, seasonal accuracy, long-tail turn against it and raises only the deviations that hold up. The two that show up most in home retail are project basket identification and seasonal pre-positioning, and both are baseline problems before they are P&L problems.

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 insight cards. Your ERP is untouched.

What the first 90 days
actually look like.

  1. 01

    Week 1: read-only connection

    Ward reads from Oracle Retail with read-only credentials and begins ingesting sales audit and inventory positions. No config changes on your side. First cards arrive in two days.

  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 daily cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: operating rhythm

    Daily 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 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

You find out about stockouts after customers do.

Pain points
  • ×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
How Ward helps
  • 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

Home KPI impact

Seasonal Accuracy
Weather + event driven
Pre-positioning adjusted for peak season signals.
Long-Tail Turn
Dead weight separated
Which tail SKUs serve project needs vs sit idle.
Project Basket Value
Cross-sell surfaced
Project purchasing patterns drive attachment.
Inventory Carrying Cost
Capital freed
Demand forecasting reduces slow-moving overstock.

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 Home-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 Project basket value, Seasonal accuracy, Long-tail turn, tailored for Supply Chain 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 Home 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