A home VP Supply Chain running fill rate on Oracle
Ward reads straight from sales audit, inventory positions, allocation from Oracle Retail, keeps a running read on fill rate over 50,000+ home SKUs, and hands you the supply chain read daily.
Fill Rate Monitoring for Home on Oracle, scoped to supply chain
You find out about stockouts after customers do. Ward filters to what a VP Supply Chain can act on and drops the rest.
Here is fill rate monitoring in plain terms. Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold.
In a Home Improvement store base the job is 50,000+ SKUs across stores. Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
The mechanism. Ward tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
Setup: Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
The short list
- Category-level drill-down
- Estate-wide fill rate dashboard
- Threshold-based alerting
- Store-vs-estate benchmarking
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 Fill Rate matters for Home retail
A store can report 96% fill rate while missing the one fastener that completes every deck project basket. Ward monitors fill rate through a project-basket lens, flagging when project-critical items drop below threshold even if aggregate availability looks healthy.
What Ward has eyes on.
Coverage is store by store, category by category. Ward watches project basket value, seasonal accuracy, long-tail turn across 50,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the footprint is fine and knowing which seven stores are not.
Ward reads 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.
The fill rate model runs daily, not on a reporting calendar. It spots the pattern, attributes what caused it, and attaches a recommended move before the number reaches a review deck.
At the metric level. Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Why this combination
is its own problem.
A VP Supply Chain rarely logs into Oracle. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.
- 01 Fill rate measured at the SKU level masks project-basket completion; a deck-project basket that needs 12 SKUs has a much lower complete-basket availability than any individual SKU's availability suggests.
- 02 Pro customer order fill rate is benchmarked against estate-wide DIY fill, hiding the much steeper Pro defection curve when a flagged SKU goes out of stock.
Benchmarks. Home improvement project-basket completion: healthy chains run 88-94% on the top-100 project baskets; below 80% on a top basket cuts category revenue 3-7% over the affected weeks. Pro customer fill rate matters disproportionately, Pros typically defect after 2-3 stockouts on flagged SKUs.
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 cards 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 insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
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
Impact metrics with Oracle
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.
- ×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
- ✓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
Frequently asked questions
Ward monitors on-shelf availability across your entire estate and flags stores or categories dropping below threshold. 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 tracks expected vs actual on-shelf availability at the store-category level and escalates when fill rate drops below configurable thresholds.
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.
Ward tracks project-basket completion rates, department availability with project-dependency weighting, seasonal merchandise positioning timing, and Pro customer order-fill rates, since Pros expect near-perfect availability and defect immediately on gaps.
Estate-wide fill rate looks healthy, but Ward's project-basket analysis shows the "deck build" basket has far lower complete-basket availability because a single specialty fastener is out of stock. A standard fill rate report would bury this item among 50,000 others. Ward surfaces it through basket completion analysis, and the supply chain team expedites the item to restore project-level availability within days.
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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