Machine Learning Can Help Improve Fleet Performance

Information generated by GPS-tracked fleets is an excellent data source for machine learning to improve delivery accuracy. Here are six ways machine learning improves fleet management.
By Chris Jones
October 24, 2021

Artificial intelligence is one of those technology terms that has captured the imagination of the press and is talked about in almost every aspect of life. In particular, a branch of artificial intelligence called machine learning can help building and construction fleet operators maximize the performance of their fleets. Machine learning algorithms build a mathematical model based on sample data to make predictions or decisions without being explicitly programmed to do so. The information generated by GPS-tracked fleets is an excellent data source for machine learning to improve delivery accuracy. Here are six ways machine learning helps fleets.

1. Delivery location

A significant challenge for construction companies is determining the delivery location at green field construction sites. There may or may not be a road system in place, the address doesn’t correctly reflect the actual delivery point and companies may not have the digital map data in their databases because the location is so new. Machine learning can use the actual delivery location data to refine the address or geocoordinates used by the route planning system. The result is more accurate and feasible routes, more accurate planned delivery times and less confusion for the driver as they know exactly where to make the delivery.

2. Service times

Services times can vary based on a number of factors such as resources involved, vehicle type, products being delivered and preparation work. Many route planning systems have the ability to model service time based on the factors above using concepts like engineered standards. While these can be highly developed models, they’re still representations of the real world and may not consider all the factors that drive service times. Machine learning can take actual service time data to determine the most representative service time. The result is more accurate service times. This improves delivery reliability and potentially improves delivery productivity, as any contingency time that was originally built into the stop could be reduced, allowing for more stops on the route.

3. Stop times

Stop times can vary based on the customer location, parking restrictions and other physical considerations (including vehicle type and driver skills). Again, while many route planning systems can model stop times in detail, not all the information and real-time physical conditions about each individual stop is available. This makes it nearly impossible to provide a reliable and accurate calculation. Machine learning can take the stop time data to determine the most representative stop time. The result is more accurate stop times. Again, this enhances delivery reliability and potentially enhances delivery productivity, as any contingency time originally built into the stop could be reduced, allowing for a greater number of stops on the route.

4. Travel times

Travel times can vary based on vehicle type, road class and network, time of day, weather and other factors. Most of these can modeled, particularly in the case of road data, in very detailed fashion. However, local conditions mean road speeds can vary greatly. Machine learning can use delivery data to determine if modeled road speeds are slower or faster than what is actually experienced. This information can be used in route planning to create more accurate and reliable routes.

5. Individual driver performance

Driver performance varies for a wide range of reasons from experience to driving speed to individual attitude. Understanding driver performance allows for a better definition of what constitutes ‘good’ performance as well as correctly incorporating it into route planning and execution processes. Machine learning can evaluate driver performance to determine a performance factor for each driver to help maximize delivery productivity and correctly reflect driver capability.

6. Estimated-time-of-arrival

Providing accurate ETAs is an important part of the customer experience and helps contractors be more productive with their crews. ETAs can be dynamically calculated using several factors, including road network speeds and restrictions, vehicle types, predictive road speeds, and traditionally calculated stop, service and travel times as well as current status in route. Considering all of the ways machine learning can better calculate the times and driver performance mentioned above, ETAs can be much more accurate, providing customers with a better delivery experience and allowing contractors to detect delivery problems before they occur.

The extent that machine learning can help building supply and construction companies improve fleet operations is considerable and tangible—from more accurate delivery plans and customer ETAs to greater productivity. Probably the best news for fleet operators is that these benefits are in reach today as ML is already being incorporated into modern route planning and dispatch systems.

by Chris Jones
As Executive Vice President, Marketing and Services, Chris is primarily responsible for Descartes’ marketing and professional services organizations. With over 30 years of experience in the supply chain market, Chris has held a variety of senior management positions including Senior Vice President at The Aberdeen Group’s Value Chain Research practice, Executive Vice President of Marketing and Corporate Development for SynQuest, Vice President and Research Director for Enterprise Resource Planning Solutions at The Gartner Group, and Associate Director Operations & Technology at Kraft General Foods.

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