Fleet Performance Management
AI-Powered Routing for Delivery Fleets
Enhance on-time delivery, strengthen service level compliance and reduce cost per delivery by learning from everyday execution data.
Continuous Improvement for Accurate, Cost-Effective Routing
Up to 30% increase in route density
97%+ on-time delivery compliance
Up to 15% reduction in cost per delivery
Accelerate decision making
Continually Improve Routes
Learn from real behavior to improve delivery performance over time.
- Increase route density by up to 30% with machine-learned service times.
- Improve on-time delivery through dynamic ETA predictions.
- Build achievable routes that maximize capacity utilization.
Reduce Planning Time
Accelerate decision making with AI agents for planners and dispatchers.
- Surface insights and recommendations without specialized analytics expertise.
- Investigate issues and test hypotheses by asking natural language questions.
- Identify patterns by connecting and analysing large volumes of execution data.
Get More Out of Your Data
Unify and standardize data to continuously improve fleet operations.
- Connect planning and execution data.
- Increase visibility with standard dashboards and KPI reporting.
- Start from an AI-ready data foundation.
AI Route Planning Principles
OUTCOMES
No AI for AI's sake
We invest where AI measurably improves customer outcomes.
VISIBILITY
You'll always know
You know where AI is used, and if we change how it behaves.
CONTROL
You decide
You choose when you're ready to bring AI into your operations.
AI in the Last Mile
In this interview, James Wee, General Manager of Fleet Solutions at Descartes, explains how AI and machine learning can help uncover hidden signals within fleet execution data, enabling better decision-making and improved operational outcomes.
Ready to improve delivery performance?
Connect with our team to get started and see how Descartes can help you streamline routes, reduce costs, and consistently deliver on time and in full.
Featured Resources
AI Route Planning FAQ's
AI routing software creates and/or improves delivery routes based on historical data including traffic, driver behavior and service times. Common applications include:
- Machine-learned service times: The software learns from past deliveries to estimate how long each stop will take.
- Machine-learned geocodes: Learns from driver behavior for more accurate drop-off locations.
- Predictive Estimated Time of Arrival (ETAs): ETAs update dynamically as the delivery approaches, based on real-world data
- AI agents: Surface insights and make suggestions to improve route performance.
Machine learning is used for route optimization, real-time re-sequencing, service time learning and ETA predictions.
Generative AI is used to surface route planning insights and suggestions using natural language questions.
Agentic AI can observe, plan and act to improve routes, with human oversight.
Machine learning generates more accurate service time predictions by learning from actual delivery performance. Models can identify how variables such as customer type, product characteristics, delivery volume and vehicles affect service time.
With more realistic service times, planners can reduce excess buffer time, increase on-time delivery and improve asset utilization, allowing drivers to complete more stops per route. Early deployments by Descartes customers have increased route density by up to 30%.
AI agents can surface real-time insights and long-term improvement opportunities without labor-intensive data extraction. Planners, dispatchers leaders can quickly investigate issues, test hypotheses and get immediate answers by asking natural language questions.
The ideal route planning vendor won't add AI features simply for AI's sake. Features will focus on making incremental improvements with measurable ROI.
For wholesale distribution, Descartes has a few advantages because its routing is built around the realities of B2B delivery.
- Machine-learned service times can significantly improve route density for operators with recurring customers, multiple delivery and service types, high-volume deliveries and different customer types.
- Machine-learned geocodes improve route accuracy over time by learning from recurring deliveries.
- Data from planning and execution is tightly integrated.