8 Technologies Enhancing Real-Time Supplier Collaboration
14 December 2025
How to reduce lead times for your EU customers without extra cost
14 December 2025
FLEX. Logistics
We provide logistics services to online retailers in Europe: Amazon FBA prep, processing FBA removal orders, forwarding to Fulfillment Centers - both FBA and Vendor shipments.
Introduction
The road freight industry forms the backbone of the global logistics network, facilitating the movement of the vast majority of consumer and industrial goods. However, this critical sector faces a persistent, costly, and environmentally damaging challenge: empty miles. Defined as the distance a commercial vehicle travels without revenue-generating cargo, empty miles contribute significantly to elevated operating costs, unnecessary carbon emissions, and chronic resource inefficiency. Industry estimates frequently place the empty running rate for heavy goods vehicles (HGVs) in the range of 15% to 30%, depending on the region and specialization. Addressing this inefficiency has become a paramount strategic objective for transportation organizations worldwide.
The solution lies in the adoption of sophisticated, AI-Driven Routing systems. Moving beyond traditional static or rule-based optimization software, these machine learning models ingest and analyze massive datasets—including real-time traffic, historical load patterns, weather data, driver availability, and regulatory constraints—to generate predictive and adaptive routing decisions. AI transforms routing from a complex, human-led scheduling puzzle into a dynamic, self-optimizing process. For logistics managers, leveraging these systems is no longer a matter of technological advantage but a fundamental requirement for sustainable and competitive operation. The following seven principles illustrate how AI-driven routing is fundamentally reshaping the road transport sector by systematically eliminating empty miles.
1. Dynamic Backhaul Matching and Predictive Load Sourcing
The most significant contributor to empty miles is the failure to secure return loads, or backhauls, for vehicles after completing a one-way delivery. Traditional systems rely on manual brokerage, load boards, or pre-negotiated, fixed lanes, which often leaves trucks running empty when fixed agreements fall through. AI-driven routing solves this by enabling Dynamic Backhaul Matching and Predictive Load Sourcing.
AI systems constantly analyze the real-time global availability of freight loads against the predicted end-of-trip location and time of every vehicle in the fleet. The model doesn't just look for an existing load; it uses predictive analytics to calculate the probability of a high-value backhaul load becoming available in the vehicle’s immediate vicinity within a specified time window. For example, if a truck is scheduled to drop off goods in a specific industrial park on Friday morning, the AI identifies and ranks potential loads originating from nearby warehouses or manufacturing plants that align with the truck’s capacity and the driver’s hours-of-service regulations. It then integrates the optimal backhaul load—often sourced from a real-time exchange or even predicted based on historical manufacturing output data—into the primary routing plan before the first journey even begins. This predictive, automated matching drastically reduces the dead time and distance spent searching for a return trip.
2. Eliminating Street Turns and Optimizing Inter-Stop Sequencing
Even in multi-drop routes, empty miles can accumulate through inefficient sequencing of delivery stops, often forcing drivers to perform unnecessary street turns (driving past a destination, turning around, and driving back) or traversing congested areas multiple times. AI-driven routing achieves superior efficiency by optimizing inter-stop sequencing on a non-linear, adaptive basis.
Traditional routing algorithms solve the Traveling Salesperson Problem (TSP) using static road network data. AI takes this a step further by integrating real-time traffic flow, time-of-day restrictions, and granular delivery window requirements. The model simulates thousands of route permutations per second, not just seeking the shortest distance, but the lowest cost-per-minute of travel while minimizing complex, inefficient maneuvers. For a dense metropolitan route with twenty stops, the AI determines the precise sequence that reduces cumulative turning, avoids known bottleneck intersections during peak hours, and maximizes the use of one-way streets, effectively reducing the non-revenue-generating maneuvers and maximizing time spent driving toward the next destination. The marginal reduction in distance per trip translates into substantial overall empty mile savings across thousands of daily routes.
3. Dynamic Consolidation and Multi-Stop Route Blending
Many logistics operations utilize dedicated vehicles for specific customers or regions, which frequently leads to underutilized space and unnecessary partial empty miles. AI routing allows for Dynamic Consolidation and Multi-Stop Route Blending, treating the entire available freight pool as a single optimization problem.
The AI system doesn't limit its search to loads originating from a single depot or client. Instead, it aggregates disparate, smaller consignments from multiple shippers that share a general geographic or temporal delivery cluster. The algorithm dynamically builds highly dense, multi-stop routes that ensure the vehicle is operating near its full capacity (cube or weight) for the maximum duration of the trip. For example, a vehicle that typically delivers only high-value medical supplies in a specific zone might be blended with a small, urgent consignment of industrial parts headed to the same region. The AI handles the complexity of merging different service level agreements (SLAs), temperature requirements, and delivery windows into a cohesive, optimized route that minimizes the need for separate, partially-loaded trips. This advanced blending capability is mathematically impossible to achieve efficiently without machine learning.
4. Predictive Capacity Utilization and Trailer Swapping Optimization
Empty miles are often generated by vehicles running with excess capacity—an empty cube within the trailer. AI-driven systems enhance Predictive Capacity Utilization by optimizing not just the route, but the required asset type and configuration, often involving dynamic Trailer Swapping Optimization.
The AI analyzes the dimensional characteristics (cube and weight) of the aggregated load, comparing it against the available fleet assets. It can recommend dispatching a smaller, more fuel-efficient box truck instead of a full-sized tractor-trailer if the load characteristics dictate, thereby minimizing the relative empty space and associated fuel burn. More complexly, in hub-and-spoke networks, the AI can plan strategic trailer swaps. For instance, a long-haul truck may drop off a partial load at a central cross-dock facility and immediately couple with a pre-loaded backhaul trailer waiting at the same facility, eliminating the empty miles associated with driving to a separate yard or terminal to pick up the return load. The system predicts the precise time the trailer will be empty and schedules the inbound backhaul to arrive simultaneously, creating a seamless, near-zero dead time exchange.
5. Integrating Driver Hours-of-Service (HOS) Constraints into Real-Time Planning
Driver Hours-of-Service (HOS) and fatigue regulations are critical operational constraints, and failure to account for them accurately often leads to unplanned stops, deviations, and wasted mileage. AI routing systems integrate HOS constraints into real-time planning with predictive accuracy.
Traditional systems rely on simple hard cut-offs. AI models, using telematics data and driver history, predict the most efficient timing for mandatory rest breaks, factoring in current traffic and potential delays. The system does not just find a route; it finds a compliant, efficient route that leverages mandatory rest stops in locations that minimize the deviation from the main route, or, ideally, strategically places rest breaks near the next pick-up point for the backhaul. If a severe traffic incident occurs, the AI instantly recalculates the HOS compliance window and reroutes the vehicle along a faster, though potentially slightly longer, path to ensure the driver arrives at the next major stop before the HOS limit is hit, avoiding costly, non-revenue-generating stops and regulatory fines that add indirect empty miles.
6. Dynamic Re-Sequencing Based on Live Operational Disruptions
The best-planned route can be instantly compromised by real-time disruptions such as traffic accidents, sudden road closures, or unexpected changes in a customer's availability. AI-driven routing enables Dynamic Re-Sequencing Based on Live Operational Disruptions, ensuring that the remaining route section is re-optimized for maximum efficiency.
The system utilizes continuous inputs from GPS, traffic APIs, and driver inputs. When a disruption occurs, the AI does not simply find an alternative route to the next stop; it instantaneously re-solves the entire remaining sequence of stops for that truck. It checks if it would now be more efficient to skip the next scheduled stop and jump to a later one that is now more accessible, or if a nearby truck in the fleet can absorb a portion of the route. This is a true dynamic optimization that constantly minimizes the aggregate distance and time required to complete all outstanding tasks, radically mitigating the empty mileage that accumulates when drivers are forced to deviate manually and inefficiently due to unexpected events.
7. Geo-Fencing and Predictive Yard Management for Terminal Efficiency
Empty miles are not exclusively accrued on public roads; they are also generated within logistics yards and terminals due to inefficient internal vehicle movement and wait times. AI-driven routing and Predictive Yard Management reduce this localized empty mileage through Geo-Fencing and real-time guidance.
By applying Geo-Fencing technology around terminals and depots, the AI monitors vehicle movement and dwell time. The system uses predictive models, informed by current inbound and outbound load volumes, to guide drivers efficiently. For example, upon arrival, the AI can instantly direct the driver to the exact open bay or loading dock that is scheduled to accept their freight, bypassing manual check-in queues and eliminating time spent aimlessly searching the yard. It can even pre-emptively stage the backhaul trailer near the unloading bay based on the vehicle’s predicted arrival time. By optimizing these terminal movements and significantly reducing waiting times, the AI eliminates the internal, non-revenue-generating mileage and engine idling that contributes to overall operational inefficiency.
Conclusion
The pervasive problem of empty miles in road transport is an economic, environmental, and operational crisis that can no longer be managed effectively with traditional tools. The seven breakthroughs in AI-driven routing—from predictive backhaul matching and dynamic consolidation to real-time HOS integration and terminal efficiency—collectively demonstrate a comprehensive solution. By leveraging vast amounts of real-time data and sophisticated machine learning models, logistics organizations can transition from simply filling trucks to dynamically orchestrating the movement of goods with near-perfect efficiency. This technological shift is paramount for achieving the required environmental sustainability targets and securing a profitable, resilient future for the road freight sector. The elimination of unnecessary empty miles is, therefore, the most immediate and profound impact of AI on the movement of global commerce.
