Retail Shrinkage Detection Software
Find the leak before it drains you. Most retailers find it in a post-mortem. Ward surfaces it while there is still time to act.
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
How Ward catches what your reports miss
Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
Dashboards
Pinned views built from saved data-lake queries.
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
What changes for your team
Sample insight card
Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.
Where this lands hardest
The roles that feel shrinkage first, and what changes for them.
How Ward delivers this
The platform pieces doing the work behind the insight.
Shrinkage by role
Available for every vertical
Shrinkage by integration
Metrics this moves
The KPIs Ward improves when this insight runs.
See it. Or compare it.
Operator stories and the alternatives buyers benchmark against.
Questions about shrinkage.
Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline. Each card explains what changed, the root cause, and the recommended action, at the store-category level, not estate aggregates.
Ward delivers shrinkage insight cards within 48 hours of data connection, with baselines tightening over the first two weeks. Most issues are surfaced before they show up in weekly or quarterly reports.
Ward runs classical statistical and time-series models your planners can audit, backtested against your last 24 months. The model card, MAPE, and confidence interval ride on every answer. The LLM frames the result; the number comes from the math.
Two ways to start.
Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.
Stop finding Shrinkage problems in the post-mortem.
See what Ward catches, and when.
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.