Shrinkage Detection + BigQuery: Built for VP Supply Chain
Most retailers discover Shrinkage problems too late. Ward delivers automated insight cards — what changed, why, and what to do — while there's still time to act. Your Supply Chain team has the data. What they don't have is bandwidth to find what's buried in it.
Shrinkage Detection powered by Google BigQuery
Shrinkage Detection is the process of ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
When connected to Google BigQuery, Ward reads any bigquery dataset, ga4 event exports, ads data transfers and enriches them with contextual signals to generate shrinkage insight cards. Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
How Ward delivers Shrinkage insight cards: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
Key capabilities
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
How Ward connects to Google BigQuery
Ward queries BigQuery using your existing datasets. GA4 exports, POS data, CRM exports. Ward reads it where it lives.
Setup: Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
Data Ward reads from BigQuery
Impact metrics with BigQuery
Data lake enrichment
Ward enriches BigQuery data with: Any BigQuery dataset, GA4 event exports, Weather & events, Demographics, Custom feeds
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate issues are discovered at receiving, not predicted
- ×Safety stock levels are set annually, not dynamically
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. — IHL Group
What a Ward insight card looks like
Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.
Frequently asked questions
Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule. Data points include: Any BigQuery dataset, GA4 event exports, Ads data transfers, Custom ETL outputs.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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
Insights surface
Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.
Insights become actions
Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.
Your team responds
Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.
Outcomes measured
Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.
Agents get sharper
Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.
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.