Machine learning and Artificial Intelligence technologies are transformational. They’re also heavily used buzzwords in our industry. Let’s find out how machine learning impacts last-mile delivery.
The delivery day starts with a set of perfectly planned routes. Stops are sequenced so that each delivery is made during each customer’s expected delivery window. Now fast forward a few hours when drivers are out on their routes. One driver, after being delayed by a bit of traffic, has made it to the first stop, and is trying to navigate the last few feet to the customer’s location. Now they hit another small delay – finding parking. After they’ve parked and have walked the block to the customer’s kiosk, they find that – just like the last three weeks – the customer isn’t ready, delaying the driver from getting back on the road and delaying deliveries downstream.
This is a fairly typical scenario, but it doesn’t have to be this way.
Learning from experience:
When we make a decision for the first time, we base that decision on some set of information. Then, we store the result of that decision in our memory. When we need to make that decision again, we can base it off of the information at hand and take into account the success of the initial decision. This is how we apply experience to decision making.
For decades, software has performed prescribed actions based on predetermined sets of information. Today, with access to larger data sets, and the ability to save the success rates of its actions, software can make decisions, and learn from them.
Using machine learning, Wise Systems can evaluate historical data, including weather, traffic and driver patterns to do things like:
- Determine the best performing drivers for the assignment of high-value accounts.
- Better anticipate delays during route planning for improved stop sequencing or more accurate delivery expectations.
- Find the best parking spots for hard-to-reach delivery locations and reroute drivers to that spot.
Watch the webinar, Machine Learning and Last Mile-Deliveries to see how else machine learning is finding efficiencies for last-mile fleets.