Reporting cut from 15 hours a week to under 1.
Forecast accuracy up to 88%.

By the time the report landed, the problem had already happened.

The operations team built weekly logistics performance reports by hand, pulling data from multiple systems – a job that ate roughly 15 hours a week. Warehouse managers had no real-time view of delivery delays or bottlenecks, so issues were usually caught only after they’d already escalated.

Demand forecasting leaned entirely on manual spreadsheet analysis, which left little room to factor in historical patterns or seasonality and made it hard to get ahead of stock levels. On top of that, operations managers were losing significant time just coordinating updates between teams – time that should have gone to strategic work.

Three constraints were non-negotiable:

• Weekly reports compiled by hand from multiple systems: ~15 hours a week
• Delivery delays spotted only after they’d already escalated
• Demand forecasts built in spreadsheets, blind to seasonal patterns

One pipeline, three systems, a live view instead of a weekly guess.

The team built an automated reporting pipeline that integrates the warehouse management system, delivery tracking feeds and finance systems into consolidated operational dashboards – no manual compilation required. A real-time monitoring dashboard flags delivery disruptions and operational anomalies as they emerge, with AI-driven anomaly detection catching unusual delays or inventory discrepancies early.

An AI forecasting model, trained on historical order data and seasonal patterns, now predicts demand and helps optimise stock levels across regional warehouses. Automated weekly executive reports summarise KPIs and forecast risks without anyone compiling them by hand, and automated alerts push concise summaries straight into the internal messaging tools frontline managers and dispatch teams already use.

Underneath it all: ETL pipelines, model training and evaluation, and a lightweight orchestration layer keeping reports and alerts running on schedule. Training sessions and playbooks were built alongside the system so managers knew how to read the AI-driven flags and act on them fast.

Fifteen hours of reporting became one.

The combination of live dashboards and message-based alerts also cut manual coordination time across operations teams by about 10 hours a week. Executive stakeholders now get regular, automated KPI reports flagging emerging risks and forecast deviations, while anomaly detection surfaces patterns – a sudden jump in transit times on a particular lane, unexpected inventory shrinkage – early enough to act on them. Reporting has gone from a labour-intensive weekly exercise to a near real-time intelligence capability that supports proactive logistics management.