The Challenge
Manual route planning was costing the company 3 hours each morning and still producing suboptimal routes. Planners used static maps and couldn't account for real-time traffic, weather disruptions, vehicle capacity constraints, or time-window commitments. As order volume grew and the fleet expanded, the planning process became a daily crisis — often delaying dispatch and cascading into late deliveries.
Our Approach
Tequity built an AI routing engine using Vehicle Routing Problem (VRP) solvers enhanced with real-time signals. The system ingests daily order manifests, vehicle availability and capacity, driver schedules, and time-window constraints — then solves for optimal route assignments using a combination of constraint programming and heuristic optimization.
Routes are updated dynamically during the day as new orders arrive or disruptions occur — traffic incidents, vehicle breakdowns, or customer rescheduling. The system integrates with real-time traffic APIs and a delay prediction model trained on the company's historical delivery data to produce realistic ETAs rather than optimistic ones.
The Results
Planning time collapsed from 3 hours to 15 minutes from day one. Delivery cost per order fell 22% through better vehicle utilization and shorter routes. On-time delivery rates improved 18%, reducing customer complaints and redelivery costs. The fleet now handles 30% more daily orders with the same number of vehicles.
