A furniture Head of Procurement running stockout on Oracle
A furniture Head of Procurement on Oracle should not be hunting for stockout problems. Ward raises them every morning.
The full picture: furniture stockout, Oracle data, Procurement decisions
What stockout prediction does: Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice.
A Furniture Manufacturing & Retail operator is monitoring 10,000+ SKUs across locations. ERP-locked production data, long lead times, and margin erosion you don't see until quarter-end. Ward connects your internal systems and surfaces what matters.
Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.
How it runs. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
The connection itself. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Key capabilities
- Priority ranking by revenue impact
- Reduce lost sales by catching gaps early
- Automated replenishment recommendations
- Supplier-aware lead time modeling
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled upholstery cost of goods by BOM line against the last price file. Three quarters of the drop is material and freight, not discounting.
| Signal | Finding |
|---|---|
bom_cost_actuals | Foam and frame stock +9.2% since the March price file, never carried to list |
freight.inbound | Inbound container cost +$412 per unit-equivalent on the Vietnam lane |
channel.mix | Wholesale share up 6pp, and wholesale runs 11pp under DTC margin |
Recommend: reprice the six affected SKUs at the next list cycle, quote the alternate foam vendor, and hold wholesale allocation flat until list catches up.
bom_cost_actuals…
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_production_stage_log | import | 2m ago |
epicor_bom_cost_actuals | import | 2m ago |
sap_inventory_snapshot | import | 14m ago |
netsuite_sales_orders | import | 1h ago |
retail_showroom_pos | import | 1h ago |
retail_freight_inbound | import | 1h ago |
retail_dealer_orders | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
finance-read-default | permit | Model::* |
sourcing-read-bom | permit | Model::"bom_cost_actuals" |
dealer-blocked | forbid | Model::"bom_*" |
plant-read-production | permit | Model::"production_stage_log" |
Why Stockout matters for Furniture retail
In furniture, a stockout is not a one-day gap, it is an 8 to 16 week hole. Replenishment often means a new container from an overseas plant, so the reorder decision has to fire weeks before the shelf runs dry. Ward models sell-through against inbound container schedules and supplier lead-time variance, flagging the reorder point early enough that a hero SKU never goes dark through a full production cycle.
What Ward has eyes on.
Coverage is store by store, category by category. Ward scans continuously inventory carrying cost, order-to-delivery cycle, gross margin by channel across 10,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven locations are not.
Ward pulls from sales audit, inventory positions, allocation from Oracle Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. Oracle stays the system of record.
Know before the shelf empties. 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.
At the metric level. Ward tracks sell-through velocity by SKU and configuration, inbound container ETAs, supplier lead-time variance by plant, and on-hand plus in-transit position. Long lead times mean the cost of a late reorder is measured in lost months, not lost days, so Ward weights revenue-at-risk across the full replenishment window.
Why this combination
is its own problem.
Running Ward on Oracle in a furniture fleet skips the usual first step. There is no ingestion project, because Oracle Retail already holds sales audit, inventory positions, allocation. Ward reads what is there.
- 01 In-transit inventory is invisible in the ERP until receipt, so buyers double-order or panic-air-freight instead of trusting the container already on the water.
- 02 Reorder points are set on domestic lead-time assumptions when the real lead time is a 14-week overseas container, so the trigger fires far too late.
Benchmarks. Furniture lead times commonly run 8 to 16 weeks for imported goods. A stockout on a top-20 floor SKU typically costs 4 to 8% of category revenue for every month it persists, because customers who cannot buy the piece they came for rarely substitute and often leave the sale entirely.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Oracle Retail. Ward reads 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.
-
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 hands you cards on a daily cycle, each with the driver and what to do about it. Volume settles at a level a single person can read over coffee. The measure of success is not how many 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
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
Furniture KPI impact
Frequently asked questions
Ward detects SKUs trending toward zero-on-hand and alerts your team with replenishment recommendations before customers notice. For Furniture retail specifically, Ward monitors 10,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Inventory carrying cost, Order-to-delivery cycle, Gross margin by channel, Raw material cost variance, Custom order cycle time at the store-category level. Ward analyzes sell-through velocity, current inventory levels, lead times, and supplier reliability to predict stockouts 24-72 hours before they occur.
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 Furniture-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 Inventory carrying cost, Order-to-delivery cycle, Gross margin by channel, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks sell-through velocity by SKU and configuration, inbound container ETAs, supplier lead-time variance by plant, and on-hand plus in-transit position. Long lead times mean the cost of a late reorder is measured in lost months, not lost days, so Ward weights revenue-at-risk across the full replenishment window.
A best-selling sectional in one fabric colorway starts selling 40% above forecast across the showroom network after a catalog feature. On-hand covers six weeks, but the factory lead time is 14 weeks and the next container is not booked. Ward issues a stockout prediction card the moment velocity clears threshold, with the recommended reorder quantity and the booking deadline to avoid a two-month floor gap. The buyer places the PO with ten weeks of runway instead of finding out at the empty slot.
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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