Computer Vision Warehouse Management
TrackIQ operates 12 large-scale warehouses across India. Manual barcode scanning, paper pick-lists, and spreadsheet inventory were causing 8% error rates and 4-hour processing cycles. We deployed a computer vision-powered WMS that cut processing time by 60% and errors by 90% — with no forklift driver retraining required.
12 warehouses processing 50,000 SKUs each. 8% error rate on picks meant 4,000 wrong shipments per day. Average order processing time was 4.2 hours. Manual cycle counts took 3 days and shut down shipping. RFID retrofitting quotes came in at $4M — way over budget. Staff turnover meant constant retraining.
We deployed overhead fixed cameras and forklift-mounted cameras paired with a custom YOLO-v8 object detection model trained on 200K+ warehouse images. Items are identified and counted automatically. Pickers receive audio instructions via earpiece connected to a React Native app, guided by the optimal pick path generated by our routing algorithm.
Overhead cameras identify and count items as forklifts move them — no scanning required.
TSP-based routing algorithm generates the shortest pick path per order, displayed in the app.
Audio instructions via earpiece with voice confirmation — pickers never look at a screen.
Real-time stock levels, location heatmaps, and low-stock alerts across all 12 sites.
NVIDIA Jetson modules run inference on-site for <50ms latency with no cloud round-trip.
Bi-directional SAP and Oracle NetSuite sync with sub-minute data freshness.
On-site visits, camera placement planning, 200K+ image dataset collection for model training.
YOLOv8 training, TensorRT optimisation for Jetson, accuracy benchmarking across SKU types.
WMS core, Kafka event streams, SAP/Oracle integration, real-time inventory engine.
React Native app, BLE device pairing, audio TTS instructions, offline resilience.
Camera installation across 3 pilot warehouses, Jetson edge deployment, network hardening.
9 remaining warehouses, staff training (minimal — system is intuitive), full SLA handoff.
The ROI calculation was simple — we were losing $2M a year in errors and reprocessing. The system paid for itself in 11 months. What impressed me most was how little we had to change for the warehouse workers — they just put on the earpiece and started.
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