Shrinkage Detection for Home merchandising
Your category managers are drowning in spreadsheets. Ward watches shrinkage across 50,000+ home SKUs and sends the merchandising read daily.
Shrinkage Detection on a home estate, scoped to merchandising
Your category managers are drowning in spreadsheets. Ward raises the readings that change a merchandising decision.
Here is shrinkage detection in plain terms. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
A Home Improvement operator is monitoring 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.
What Ward does with that: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
What you get
- 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.
Why this combination
is its own problem.
Shrinkage Detection produces a lot of output that is technically correct and operationally useless to merchandising. Ward filters on whether the finding changes a decision a VP Merchandising can actually make.
- 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 Pallet receiving discrepancies on building materials (lumber, drywall, roofing) often don't surface until the next physical because random sampling rarely catches the missing units.
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 Ward has eyes on.
The shrinkage model runs each day, not on a reporting calendar. It spots the pattern, traces the driver, and attaches a recommended action before the number reaches a review deck.
Ward watches 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.
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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to whatever holds your transaction and inventory data. Ward starts building baselines the same day. First findings land within 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 every morning 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.
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
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.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Project basket value, Seasonal accuracy, Long-tail turn, tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.
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.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.