Logistics Firm Gains Real-Time Visibility Across 12 Warehouses with Data Platform

We started by cataloguing every data source: 4 different WMS vendors, a TMS, a fleet tracking system, and HR data for labor. The biggest challenge was schema normalization — each WMS used different field names, date formats, and status codes for the same concepts. We built a dbt-based transformation layer that standardizes all incoming data into a unified warehouse model. Apache Airflow orchestrates nightly full loads and 15-minute incremental syncs for high-priority metrics like dock-door utilization and pick rate. The executive dashboard refreshes every 15 minutes. Site managers get their own dashboards with drill-down to individual shift and SKU level. What used to take 3 weeks to compile now updates automatically — and anomalies like unusual shrinkage or throughput drops trigger automated Slack alerts to the relevant site manager within minutes.