Shrinkage Detection
Find the leak before it drains you. Most retailers discover this too late — in a post-mortem, a quarterly review, or a customer complaint. Ward surfaces it while there’s 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.
What changes for your team
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
Every finding shows its work. Confidence scores, forecast blockers, goal relevance — pin it, ask about it, or investigate deeper.
Ward builds charts on the fly from your question. Revenue vs. margin, category comparisons — generated in seconds, pinnable to any dashboard.
Sample insight card
Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.
Shrinkage by role
Available for every vertical
Shrinkage by integration
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.
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.