Home demand from Oracle, briefed to procurement
See demand before it arrives. Ward runs it on Oracle data across your home fleet, scoped to procurement.
The full picture: home demand, Oracle data, Procurement decisions
A Home Improvement operator is monitoring 50,000+ SKUs throughout stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.
Here is demand forecasting in plain terms. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
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 from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
The short list
- 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 spring pre-build against the last three seasons and the weather signal for the 22 Southeast stores.
| Signal | Finding |
|---|---|
seasonal_prebuild | Mulch on-hand hit 61% of plan the week temps broke 70°F |
dc_push | DC push started 9 days after the first-warm-week trigger, LY it was 2 |
attach.project | Soil 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.
seasonal_prebuild…
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 |
|---|---|---|
epicor_pos_transactions | import | 2m ago |
epicor_special_orders | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
retail_seasonal_prebuild | import | 1h ago |
retail_sku_velocity | import | 1h ago |
retail_pro_account_sales | import | 1h ago |
retail_weather_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
pro-team-read-accounts | permit | Model::"pro_account_sales" |
vendor-blocked | forbid | Model::"labor_*" |
supply-read-orders | permit | Model::"special_orders" |
Why Demand matters for Home retail
Home improvement demand is the most weather-dependent in retail, a warm spring can shift seasonal demand forward by weeks across a large chain. Ward integrates 10-day weather forecasts, historical correlations, and housing market indicators to predict demand at a granularity that static seasonal plans can't match.
What Ward has eyes on.
Coverage is store by store, category by category. Ward tracks project basket value, seasonal accuracy, long-tail turn over 50,000+ 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 stores are not.
Ward ingests 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.
The demand model runs daily, not on a reporting calendar. It flags the pattern, explains root cause, and attaches what to do about it before the number reaches a review deck.
At the metric level. Ward integrates hyperlocal weather data, housing market indicators (home sales, building permits), seasonal project activation curves, and Pro customer pipeline data. Forecast accuracy is measured by department and weather-sensitivity tier.
Why this combination
is its own problem.
A generic demand model applied to a home estate produces alerts nobody trusts. The thresholds are wrong, because home baselines are wrong for it. Ward learns the baseline from your own stores instead of importing one.
- 01 Housing market indicators (permits, sales) are leading signals for project demand but rarely make it into pharmacy or operational forecasting workflows.
- 02 Seasonal calendars are set chain-wide but the actual project-season start varies 4-8 weeks across regions; Northeast deck season is meaningfully later than Southeast.
Benchmarks. Home improvement seasonal forecast accuracy: 22-35% MAPE on weather-sensitive categories (paint, lawn/garden, outdoor power) is healthy. A 5-point accuracy gain typically reduces seasonal markdown by 20-30% and recovers 1-3% category revenue from better stock positioning.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Oracle Retail. Ward pulls from 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: 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
Findings arrive each day 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
Impact metrics with Oracle
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.
- ×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
- ✓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
Home 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 Home retail specifically, Ward monitors 50,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Project basket value, Seasonal accuracy, Long-tail turn, Pro customer share, Attachment rate at the store-category level. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
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.
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 Project basket value, Seasonal accuracy, Long-tail turn, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward integrates hyperlocal weather data, housing market indicators (home sales, building permits), seasonal project activation curves, and Pro customer pipeline data. Forecast accuracy is measured by department and weather-sensitivity tier.
February temperatures run well above normal across the Midwest. Ward detects early-spring project categories activating weeks ahead of plan while winter products decelerate faster than expected. Ward issues demand adjustment cards for Midwest stores: accelerate spring resets, cut winter closeout buys, and increase DC allocation of seasonal products. Stores that act capture early-season revenue that would have stocked out under the original plan.
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 Home demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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