Our research explores how a dynamic routing engine could replace static daily routes with real-time AI-optimised schedules — projecting $3.1M in annual savings and lifting on-time delivery from 77% to 95% for a large-scale last-mile logistics operator.
Scenario: Large-scale last-mile logistics operator · Industry: Logistics & Distribution · Est. Timeline: 12 weeks
Large logistics operators typically build routes each morning based on that day's delivery manifest. Once vehicles leave the depot, there is no mechanism to adapt as conditions change throughout the day — a structural weakness with major cost and service implications.
1,200 vehicles followed routes generated manually each morning with no intra-day re-optimisation. New orders, traffic incidents, customer cancellations, and failed deliveries accumulated throughout the day with no systematic response — just driver improvisation.
Rising fuel costs combined with routing inefficiencies had pushed annual fuel spend to $14.2M. Driver feedback repeatedly identified high-traffic corridors that were avoidable with alternative routes, but the static routing process had no mechanism to respond.
Estimated delivery windows were 4-hour blocks, and actual on-time performance had fallen to 77%. 23% of inbound call centre volume was related to delivery status queries or missed windows — a growing reputational and operational cost.
The proposed solution is a constraint-aware Vehicle Routing Problem solver combined with real-time traffic integration — giving dispatchers dynamic, always-optimal routes while preserving full human override capability.
Analysed 18 months of GPS telemetry, delivery logs, and traffic API data to build a statistical model of corridor congestion by time-of-day and day-of-week for all FastShip delivery zones. This baseline informed the initial routing cost matrix.
A Vehicle Routing Problem solver using Google OR-Tools incorporates vehicle weight/volume capacity, customer time windows, driver hours-of-service constraints, and priority tiers for same-day and premium delivery SLAs.
Integrated with HERE Maps Traffic API to detect live incident and congestion events. Each replanning cycle completes in under 3 seconds for zones of up to 300 active stops — fast enough for immediate dispatcher action when a route blockage occurs.
A React dispatcher dashboard with live vehicle tracking, route performance metrics, and drag-and-drop manual overrides for exceptional cases connects to the operator's WMS for order data feeds and CRM for customer notification triggers.
Estimated impact based on logistics optimisation benchmarks, VRP modeling, and comparable dynamic routing deployments in last-mile delivery.
Projected reduction in total fuel expenditure by ~22%, with distance per delivery falling ~18% through route consolidation and more efficient stop sequencing across the fleet.
Projected improvement in on-time delivery performance from a typical 77% baseline to 95% — reducing complaint call volumes and driving an increase in repeat order rates.
Projected 18% reduction in average time from depot dispatch to delivery, driven by fewer backtracking movements, better traffic avoidance, and tighter stop sequencing across all zones.
Every engagement starts with a thorough understanding of your data, workflows, and objectives. Let's talk about what's possible for your organisation.