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The Opportunity
High time waste
Workers spent several hours travelling inefficiently between locations, reducing daily coverage of points of sale.
Complex constraints
Routes had to respect limits such as maximum time per route, vehicle constraints and different frequency of visits.
No optimisation engine
There was no system capable of generating fast, cost-efficient routes or adapting to changes.
The Solution
We built a routing engine designed to optimize daily field operations. Using an OSRM-based server, we automatically calculate travel durations between every pair of locations and generate a complete duration matrix. With this foundation, our optimisation algorithms create routes that minimise travel time while respecting real constraints such as maximum route length, vehicle limits and repeated visits. The system then produces clear, ready-to-use schedules for one or multiple workers.
The approach is flexible: it adapts to real-time changes, tests multiple routing strategies, and selects the most efficient one. By combining classic machine learning with optimisation methods, we help operations teams reduce wasted kilometres, expand coverage, and increase predictability in the field.
The Impact
The routing engine has changed the way field teams plan their day. Instead of relying on manual planning or habit-based routes, teams now receive optimised schedules that help them cover more ground with less effort. With shorter travel times and smarter ordering of visits, workers can reach more locations per shift, respond faster, and operate with greater predictability.
This efficiency also translates into tangible savings. Fewer wasted kilometres mean lower fuel consumption and reduced operational costs, while improved coverage ensures that teams maximise the value of every hour on the road. What began as a technical optimisation quickly became a strategic advantage, giving the client a faster, clearer, and more scalable way to manage daily operations.
