WorkGlobal E-Commerce · Bag Tracking
Global E-Commerce Leader AIOperations

Reducing bag loss
by 95% — in
24 weeks.

Client
Global e-commerce leader — Fulfilment Operations
Capability
AI · Real-time tracking · Anomaly detection
Timeline
24 weeks end-to-end
Team
4 engineers · 1 product lead
95%
Reduction in bag loss
92%
Recovery rate on flagged incidents
24
Weeks from kickoff to production

Losses were invisible. Accountability was zero.

The client’s fulfilment operations involve thousands of bags moving across handoff points daily. The problem was not that bags were disappearing — it was that nobody could prove it, quantify it, or assign accountability for it. Manual logging created gaps at every handoff. Reconciliation happened after the fact, by which point the chain of custody was broken.

Before
12%
of bag losses traced to a cause
After
95%
reduction in total bag loss

Four constraints that shaped every decision.

Operational
System had to work with existing floor staff. Zero training overhead. Scan in under 3 seconds or adoption would fail.
Scale
Thousands of bags per shift, multiple facilities. The system had to handle peak volumes without degradation.
Latency
Anomaly alerts had to fire within 90 seconds of a loss event — after that, recovery probability drops sharply.
Adoption
Floor managers needed to trust the data before they would act on it. Credibility had to be earned before authority was assigned.

Two weeks embedded. Then we designed.

The Flipr team spent the first two weeks on the fulfilment floor — observing every handoff, mapping process gaps. The root cause was not a technology gap. It was a process gap that technology could close.

We designed a real-time tracking system on barcode scanning at every handoff, feeding a central event log with automated anomaly detection. The AI layer flagged deviation patterns and escalated unresolved anomalies to supervisors within 90 seconds.

Event-driven architecture. Sub-second anomaly detection.

Every scan generates an event flowing through a real-time processing layer before being committed to the audit log. Anomaly detection operates on live data, not end-of-shift batch logs.

LAYER 01Scan LayerMobile app + fixed barcode readers at every handoff zoneReact Native · BLE + QR scanning · Offline-first syncEvent streamLAYER 02Apache Kafka — Event StreamLAYER 03AEvent ProcessorNode.js · Real-time enrichmentLAYER 03BAnomaly DetectionStatistical baseline · Dwell timeLAYER 04AStoragePostgreSQL audit log · Redis stateLAYER 04BAlert Engine90s escalation · Supervisor SMSSupervisor Dashboard + Mobile Alerts
Frontend
React Native (scanning app)
Next.js (supervisor dashboard)
WebSocket real-time updates
Backend
Node.js event processor
Apache Kafka event stream
PostgreSQL + Redis
AI Layer
Statistical baseline modeling
Dwell-time anomaly detection
90-second alert escalation

What the supervisor sees — live.

The dashboard became a standard part of floor management. Shift handover briefings now include a live bag count. Anomaly alerts are reviewed in the daily ops standup.

app.flipr.io/amazon/bag-tracking/live
Fulfilment Operations · Live View
Bag Tracking Dashboard
LIVE
Bags Tracked Today
10,247
↑ 3.2% vs yesterday
Anomalies Detected
3
2 resolved · 1 open
Recovery Rate
95%
↑ from 12% baseline
Bag Loss Rate — Last 6 Months
Recent Events
Bag #BT-4821 — Zone D → Dispatch
CLEARED
Bag #BT-4819 — Dwell alert Zone B
OPEN
Bag #BT-4817 — Recovered at Zone A
RECOVERED

95% loss reduction. 92% recovery rate.

Within 90 days of go-live, bag loss dropped by 95%. The recovery rate on flagged incidents was 92%. The client rolled the system to additional facilities ahead of schedule.

The less visible outcome was accountability. The system created an unambiguous chain of custody at every handoff. The act of being measured was itself a reduction in loss.

24 weeks. Five phases.

Wks 1–2
Discovery
Embedded on the fulfilment floor. Mapped every handoff. Documented process gaps.
Wks 3–4
Architecture
Designed event architecture, data model, anomaly detection. Validated before build.
Wks 5–14
Build
Scanning app, event processor, anomaly detection, supervisor dashboard. Weekly demos throughout.
Wks 15–20
Pilot
Two-facility deployment. Iterated on alert thresholds and UX based on floor feedback.
Wks 21–24
Rollout & Handoff
Full facility expansion. Documentation and knowledge transfer to the client’s engineering team team.