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6 Minute Read

What Are the Top Machine Learning Use Cases in Logistics?


According to a recent report from IDC, the top 2 business objectives for using AI are operational efficiency (39%) and improved customer experience (38%). Given how trendy AI and machine learning are at the moment, it should come as little surprise that these kinds of technologies are being used to address such critical business areas. But it’s hard not to wonder what actual use cases for AI and machine learning are being leveraged to try and achieve those goals.

top machine learning use cases in logisticsIn this post, we’ll go over some of the ways that delivery organizations are using machine learning-powered technology to actually address concerns like operational efficiency and improved customer experience. 

What Role Does Machine Learning Have to Play in Logistics?

First things first, what exactly are we talking about when we talk about machine learning? AI as a concept is so broad that it includes a lot of technologies that are barely related to one another. Machine learning, on the other hand, is a specific subset of AI that involves training algorithms on large datasets so that they “learn” how to find connections and correlations that a human data analyst wouldn’t necessarily be able to uncover. 

Logistics processes in general, and last mile deliveries in particular, generate a ton of data. So it stands to reason that machine learning would have a role to play in logistics—i.e. turning some of the operational data being produced by daily operations into valuable insights that can lead to efficiency improvements. 

Before that can happen, however, delivery organizations need to lay the foundation for getting value out of their data. That means ensuring that all of their processes and technology solutions are fully connected and can easily share data between themselves. You should be able to pinpoint a single source of truth for, say, last mile logistics data, and that should function as a real repository of all the data you’re generating in that area. 

To make this happen, most businesses turn to cloud technology, since it’s fundamentally more connected and interoperable than on-prem technology deployments. When you have this kind of foundation for your tech stack, you put yourself in a position to easily enable new technologies without having to reinvent the wheel. 

Top Machine Learning Use Cases in Logistics

When you have a flexible, connected technology stack that can incorporate machine learning-powered technology, there are a number of different machine learning use cases that you can focus on. 

Route optimization

Like we said, machine learning is all about data—and  route optimization is an extremely data-intensive process. Why? For one thing, you have routing parameters like customer time window requests, driver skill level requirements, vehicle requirements, drive time, service time, and much more. The more parameters you add, the harder it is for a human planner to create an efficient plan. 

This is where machine learning comes in. By analyzing these parameters—along with comprehensive data about past deliveries, including traffic patterns, service times, and more—machine learning-powered technology can rapidly generate routes that actually meet your parameters. 

Crucially, machine learning also has the power to leverage all that data into extremely accurate delivery ETAs. This might not seem like a game-changer, but it’s hard to overstate the importance it can have on efficiency and customer experience. When you know precisely when the truck is going to arrive at the delivery site, you can avoid over-scheduling and under-scheduling, meaning you use your capacity much more efficiently. At the same time, customers hugely prefer to receive their orders on-time, and they’re much more likely to be at the delivery site to accept the delivery if it shows up as promised.  

Territory optimization

The same sorts of machine learning use cases we described above for route optimization can also be applied to territory planning. On some level, this makes sense: Planning territories is like planning routes with the complexity ratcheted up to 11. You need to account for customer needs and other parameters and balance revenue potential between sales personnel—all in such a way that it will actually translate into daily routes that make sense. 

Here, the big benefit that machine learning can offer is speed. Traditionally, territory optimization has been a slow, cumbersome, and painful process. The sort of process that a food distributor or beer wholesaler might be loath to do more than once or twice a year at most. This can lead to inefficiencies, since the territories you’re running often don’t match the actual daily and weekly realities of your business. 

With machine learning, the territory planning process suddenly moves at the speed of business. By leveraging the same routing techniques we discussed above as part of the territory planning process to automatically generate routes as you’re generating territories, you can cut out the guesswork that comes from feeding territories into your routing solution, making adjustments, and then taking those adjustments back to your territory planner. In short, you can shave the process down from a matter or days or weeks to a matter or minutes. As a result, you can update plans as often as you like. Thinking about taking on a new large client account, or opening up a new distribution center? You can test out potential new territories to accommodate those changes. Wondering how you’re going to cope with increased order volumes from a particular client? Spend a few minutes running new territory plans and find out. 

Predictive analytics

So far, the machine learning use cases in logistics that we’ve presented have been fairly specific. But if we zoom out a little bit, we can see that one of the most important applications for AI in logistics is powering better predictive insights across the supply chain. Delivery ETAs can certainly fall into this category, but the potential uses are much broader. For instance, further upstream in the supply chain we’re starting to see applications that can predict changes in freight rates so that shippers can lock in favorable prices more frequently. By the same token, machine learning can be used to predict customer ordering probabilities. 

These are just a few examples, but as AI and machine learning technology becomes more advanced, the potential applications will only increase. That’s why it’s so important for delivery organizations to lay the groundwork now to take advantage of future technological advancements. 

How to Future-Proof Your Supply Chain

So how, exactly, do you put yourself in a position to make sure that you’re able to take advantage of new machine learning use cases in logistics as they crop up? The simplest way to do so might be to simply prioritize utilizing the machine learning-powered applications that already exist. For instance, you might make a point of adopting AI-powered route optimization software the next time you’re upgrading your technology. In doing so, you obviously get all the benefits of the software itself (say, increased efficiency and improved customer satisfaction), but you’d also be laying the foundation for future AI and machine learning-powered technology deployments. 

As we go forward, the potential machine learning use cases in logistics are only going to increase—within a few years, technologies that we haven’t even thought of yet might be commonplace. When that happens, it may be those who are already leveraging AI who are able to benefit the most quickly. That’s why the technology decisions you make now are so important. 


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