[Client Info]Freight Logistics Operator
A transportation and logistics firm managing end-to-end freight movement across rail, truck, and intermodal delivery routes.
#1. summary.
This project focused on improving asset utilization, delay prediction, and route-level cost accuracy across the client’s freight network. Operations teams struggled to respond to unpredictable delivery slowdowns, underutilized vehicles, and reactive planning. Xuno delivered predictive delay models, capacity forecasts, and cost-per-route intelligence integrated directly into their dispatch and planning workflows.
Technologies used:
#2. challenges.
Route planning was reactive, relying on historical data and driver reports. Forecasting models failed to account for weather, dwell times, or terminal congestion. Cost-per-route estimates were inaccurate, making profitability hard to track in real time. Dispatch teams lacked the insight to rebalance loads or predict late arrivals until after they occurred—impacting delivery SLAs and resource allocation.
#3. solution.
Xuno implemented a predictive system that analyzed historical trip data, terminal bottlenecks, and external risk factors like weather or road restrictions. We built dynamic forecasts for ETA, idle time, and cost per mile—delivered directly to dispatch dashboards. Resource managers were also given visibility into underutilized assets with proactive reallocation alerts.
#4. outcome.
Forecast accuracy improved by 22%, with a 31% reduction in missed SLAs due to unexpected delays. Idle time dropped by 18%, and cost-per-trip estimation improved by 27%. Planners reported higher confidence in asset assignments, and dispatchers used the new system to prevent reroutes before they impacted delivery windows.
