The problem
A GCC logistics company was making route planning and inventory decisions based on two-week-old reports. By the time the data reached decision-makers, it was already wrong. Operational inefficiency was costing millions annually.
What we did
Built real-time data pipelines pulling from all operational systems into a unified data lake. Deployed predictive ML models for demand forecasting and route optimisation. Set up automated MLOps pipelines to retrain models continuously as new data arrives.
The outcome
Decision-making moved from two-week lag to real-time. Logistics costs reduced by 22%. Inventory waste cut by 31% in the first six months.
