Home shrinkage, read from Epicor
Ward reads sales orders, inventory, purchase orders from Epicor and hands back home shrinkage cards daily. Read-only, no config changes.
How Ward turns Epicor data into home shrinkage
What shrinkage detection does: Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
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.
The mechanism. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Getting the data in. Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
What it does
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
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.
Coverage is store by store, category by category. Ward monitors 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 store base is fine and knowing which seven stores are not.
Ward reads straight from Epicor rather than replacing it. Sales orders, inventory, purchase orders come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your ERP is untouched.
The shrinkage model runs on a daily cycle, not on a reporting calendar. It catches the pattern, traces what caused it, and attaches the next step 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.
Shrinkage Detection needs sales orders and inventory at minimum. Epicor carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
- 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 Epicor. Ward pulls from sales orders, inventory, purchase orders 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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands you insight cards daily, each with the cause and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.
How Ward connects to Epicor
Ward integrates with Epicor for home improvement, furniture, and building supply retailers. Inventory, purchasing, production, and sales data power insight cards.
Setup: Ward connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse.
Data Ward reads from Epicor
Impact metrics with Epicor
Data lake enrichment
Ward enriches Epicor data with: Sales orders, Weather & events, Contractor/Pro data, Competitor pricing, Vendor 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 connects via Epicor REST API. Compatible with Epicor Prophet 21 and Epicor Eclipse. Data points include: Sales orders, Inventory, Purchase orders, Customer accounts, Pricing tiers, Vendor performance.
Yes. Ward reads Epicor 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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