3. Using AI and analytics for better routes and greener deliveries
Manually planning delivery routes is time-consuming and often inefficient, leading to wasted fuel, increased operational costs, and longer delivery times. On top of that, crowdsourcing solution is still not ideal for transporting more expensive, bulkier packages, which is when you will probably want to use a more established shipper.
However, such deliveries present their own issues, with trucks having to find a suitable unloading spot or navigate smaller inner-city roads. As out-of-route miles account for 3-10% of a driver’s total mileage, inefficient route planning can add a hidden cost to already expensive services.3
Artificial Intelligence (AI) and data analytics offer powerful last-mile delivery solutions for e-commerce businesses looking to optimize this critical stage. These technologies automatically analyze vast amounts of data—including traffic patterns, delivery addresses, specified time windows, vehicle capacity, and driver availability—to calculate and assign the most efficient routes dynamically.
Implementing last-mile delivery route optimization through AI can directly reduce fuel consumption and costs, cut down on unnecessary mileage, shorten overall delivery times, improve customer satisfaction, and lower the carbon footprint of your delivery fleet, sometimes by as much as 25%.
Some sources suggest4 that couriers and courier route planning staff could be spending 3-4 hours a day manually planning their routes.
Taking a data-driven approach is vital to provide industry-leading services today. Data analytics provides clear insights into delivery performance, so logistics professionals can easily identify operational bottlenecks, predict potential delays more accurately, and make informed decisions to continuously improve both the customer experience and bottom-line efficiency.
Here are the types of AI-powered tools e-commerce businesses can leverage:
- Route Optimization Software: Specialized platforms that automatically calculate the most efficient multi-stop routes based on various constraints. Examples include dedicated tools or modules often found within comprehensive last-mile delivery management systems.
- Predictive Analytics Tools: Software designed to forecast delivery demand geographically, predict traffic congestion hotspots, or estimate delivery times (ETAs) with greater accuracy for better customer communication.
- Telematics Systems: Modern vehicle tracking systems (GPS) integrated with analytics platforms provide real-time data on driver location, behavior (like speed or idling), and vehicle performance, enabling ongoing monitoring and optimization opportunities.
- Warehouse Automation (AI-powered): Within fulfillment centers, AI enhances robotics used for sorting, picking, and packing orders. This accelerates the preparation stage, impacting how quickly parcels are ready for dispatch into the last-mile network.
In the distribution centers, AI and robotics are coming together to automate repetitive tasks, But does that mean humans are no longer needed? Tim Tetzlaff, Global Head of Accelerated Digitalization, DHL Supply Chain: “The more we can use robots to complete repetitive or distant tasks in highly predictable, structured environments, the more we free up our employees to leverage their unique human capabilities.” AI and robots are another tool to help humans—not to replace humans.
Perhaps most importantly, route optimization AI can reduce carbon emissions from a last-mile road fleet by as much as 25%. Software company Descartes’ route optimization tools have been shown to reduce CO2 emissions by over 552,000 tons and decrease fuel usage by 5% to 25%5. That’s a huge impact, all achieved by simply finding the best route.