Oracle plus Ward: shrinkage detection for home retail
Ward reads straight from sales audit, inventory positions, allocation from Oracle Retail and sends home shrinkage findings every morning. Read-only, no config changes.
How Ward turns Oracle data into home shrinkage
In a Home Improvement estate 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.
Shrinkage Detection is a finding type Ward runs continuously. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
How Ward delivers Shrinkage daily cards: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
How the connection works. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
The short list
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
- Cause-level shrinkage attribution
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 Shrinkage matters for Home retail
Small hardware has the highest per-unit theft rates but lowest dollar impact; power tools have lower frequency but massive loss per incident. Ward segments shrinkage by value tier and department so loss prevention allocates resources where the dollar impact is highest, not just where the unit count is.
What Ward has eyes on.
Ward tracks 50,000+ SKUs throughout your stores, at the store-category level rather than the chain roll-up. The metrics under watch include project basket value, seasonal accuracy, long-tail turn. A roll-up hides a single-store problem inside a healthy average, which is how project basket identification stays invisible for a quarter.
The connection to Oracle Retail is read-only and runs on your schedule. Ward pulls from sales audit, inventory positions, and the rest of the feed, then combines it with external context your ERP does not carry: weather, local events, competitor pricing.
The shrinkage model runs daily, not on a reporting calendar. It spots the pattern, accounts for the cause, and attaches a recommended move before the number reaches a review deck.
At the metric level. Ward segments by value tier, tracks geographic clustering for ORC detection, monitors receiving accuracy on bulk/pallet deliveries, and measures POS velocity-to-inventory count gaps.
Why this combination
is its own problem.
A generic shrinkage model applied to a home store base 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 High-volume small hardware shrink dominates incident counts and pulls LP attention toward unit-level fixes; the bigger dollar exposure is in low-frequency power tool theft.
- 02 ORC patterns concentrate geographically; chain-wide LP allocation misses the cluster signal that directs investigative focus.
Benchmarks. Home improvement shrink runs 1.4-2.6% of sales, with power tools, copper, and high-value building materials accounting for 30-50% of total dollar shrink despite small unit volume. ORC events typically affect a tight geographic cluster of 5-15 stores.
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 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: 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
Cards arrive on a daily cycle 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
Home KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. 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 compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
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.
Ward segments by value tier, tracks geographic clustering for ORC detection, monitors receiving accuracy on bulk/pallet deliveries, and measures POS velocity-to-inventory count gaps.
Ward flags elevated power tool shrinkage at a geographic cluster of stores, concentrated during weekday afternoons, a pattern consistent with organized retail crime. Ward recommends immediate spider-wrap enforcement and receipt-checking at affected locations. LP investigation confirms a theft ring, and targeted intervention brings shrinkage back toward estate averages within weeks.
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 shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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