6 ways Machine Learning can enhance fleet operations: A Comprehensive Guide
Machine learning (ML) technology has many practical applications when it comes to delivery management.
From route optimisation to driver performance, ML can increase efficiency and reduce delivery costs.
How does machine learning apply to delivery operations?
Machine learning algorithms use GPS data from your fleet to make predictions or decisions that improve delivery performance.
Here are some specific use cases for machine learning in last-mile delivery.
1. Refining Delivery Locations
Accurate delivery locations are always a challenge for companies delivering to newly developed areas or construction sites. Traditional address data may not accurately pinpoint the required delivery location, often leading to delays and confusion.
Machine learning assists planners by leveraging real-time data from vehicle sensors and GPS tracking in order to refine delivery locations for the future, ensuring drivers reach the precise destination promptly in subsequent visits.
2. Predicting Service Times
Service times can vary significantly depending on various factors, such as the complexity of the delivery and driver proficiency.
Machine learning algorithms can analyse historical data and real-time information to more accurately predict service times, enabling route planners to optimise delivery schedules and improve overall efficiency. This translates into a more reliable plan in the future with potentially more deliveries on a route as any overestimate of service time can be reduced through machine learning.
3. Enhancing Stop Times
Stop times at customers can be influenced by a range of factors, including location, parking availability, and vehicle size or type.
Machine learning can analyse historical stop data and incorporate real-time conditions to better predict future stop times for each delivery. This information helps dispatchers optimise routes and minimise idle time, leading to increased productivity and cost savings.
4. Understanding Travel Times
Travel times can be affected by changing factors such as traffic congestion, road closures, time of day, and weather conditions.
Machine learning algorithms will continuously monitor delivery times and travel times, adjusting future planned travel speeds should actual travel speeds in certain locations vary wildly from expected modelled speeds to ensure accurate delivery times for future route plans.
5. Evaluating Driver Performance
Driver performance can be assessed based on a variety of metrics, including driving speed, fuel efficiency, adherence to schedules, and accident avoidance.
Machine learning can analyse this data and identify patterns and trends, helping to provide fleet managers with insights that can help them improve individual driver performance and thus overall fleet efficiency.
6. Providing Accurate ETAs
Accurate ETAs are crucial for maintaining customer satisfaction and ensuring on-time deliveries.
Machine learning algorithms can incorporate real-time traffic data, historical delivery times, predicted service durations, travel times and driver behaviour, while including information about the products being delivered, size and type of vehicle helping to provide ETAs that are more accurate than ever before. Accurate ETA’s generate trust with customers and enhance their overall experience.
Conclusion
Machine learning is already here and already revolutionising fleet operations today.
By harnessing Machine learning's power, businesses can better optimise their delivery routes, enhance driver performance, improve ETAs, and overall improve their fleet’s efficiency. As Machine learning continues to evolve, so its impact on the logistics industry will increase resulting in even greater levels of productivity, cost savings, and customer satisfaction.
Contact us to understand how Machine Learning is incorporated into Descartes Delivery Scheduling and Route Optimisation software.
