Shrinkage Detection for Fashion & Apparel
No dashboards. No queries. Shrinkage findings delivered every morning.
Why shrinkage matters
in fashion retail.
The biggest hidden source of fashion shrinkage isn't theft, it's administrative error in transfer-heavy operations where every handoff between stores, e-commerce, and returns is a reconciliation risk. Ward tracks inventory movements across all channels and distinguishes transfer discrepancies from return fraud and genuine theft.
Industry benchmarks
Fashion shrink runs 1.4-2.5% of sales, with returns/wardrobing in premium tiers contributing 0.4-0.8% of that. High-value categories (handbags, outerwear, premium denim) account for 40-60% of total dollar shrink despite being 10-20% of unit volume.
Return fraud pattern detection, premium retailer
Ward flags a cluster of stores where high-value item returns run well above estate average, most without original tags, with the same payment cards appearing across multiple locations. The pattern matches a wardrobing ring. LP adjusts the return policy for flagged categories and sees a significant drop in high-value returns within weeks.
What Ward actually tracks
Ward tracks transfer accuracy rates, return-to-sale ratios, inter-store reconciliation gaps, and high-value item movement patterns. Separating operational shrinkage from intentional loss is essential because the interventions are completely different.
Data signals
POS transactions including returns, OMS transfer logs, WMS receiving scans, customer return history, payment card patterns, and tag-status fields where captured.
Three pitfalls Ward catches
in fashion shrinkage.
- 01 Return fraud and wardrobing get logged as legitimate returns because store associates lack the data to challenge them in real time.
- 02 Inter-store transfers get scanned as "received" without case opening, so vendor short-shipments only surface at the next physical inventory.
- 03 BOPIS and ship-from-store create double-counted inventory in the OMS that disappears on the next reconciliation as "shrink".
How Ward runs shrinkage
for fashion retailers.
-
01
Reconcile inventory across channels weekly, not at physical
Ward builds a continuous reconciliation between POS, OMS, WMS, and e-commerce; gaps surface as cards within days, not at the annual count.
-
02
Score returns by fraud risk
Time-to-return, missing tags, repeat customer patterns, and payment card overlap produce a per-return risk score; the top 5% trigger associate prompts.
-
03
Tag high-value items for movement audit
For categories above a value threshold, Ward enforces an audit trail check at every channel handoff and flags gaps in real time.
What a Ward card looks like.
Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.
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.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
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 |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
Light and dark themes are available. Your choice is remembered per browser.
Fashion shrinkage:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Cause-level shrinkage attribution
- ✓Store-vs-estate benchmarking
- ✓Receiving dock anomaly detection
Fashion KPI impact.
Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
Questions about fashion shrinkage.
The biggest hidden source of fashion shrinkage isn't theft, it's administrative error in transfer-heavy operations where every handoff between stores, e-commerce, and returns is a reconciliation risk. Ward tracks inventory movements across all channels and distinguishes transfer discrepancies from return fraud and genuine theft.
Ward flags a cluster of stores where high-value item returns run well above estate average, most without original tags, with the same payment cards appearing across multiple locations. The pattern matches a wardrobing ring. LP adjusts the return policy for flagged categories and sees a significant drop in high-value returns within weeks.
Ward tracks transfer accuracy rates, return-to-sale ratios, inter-store reconciliation gaps, and high-value item movement patterns. Separating operational shrinkage from intentional loss is essential because the interventions are completely different.
First shrinkage insight cards arrive within 48 hours. Robust fashion baselines form within two weeks. Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
Fashion shrinkage
by data source.
More Fashion insight cards.
Fashion retailers: see what shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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.