5 Applications of AI in Logistics

ChatGPT hasn’t taken over the world yet, and human delivery drivers still haven’t been phased out in favor of autonomous vehicles with sophisticated computer vision capabilities—but AI has been making an impact in the world of logistics for longer than most people imagine. In fact, many solutions that have been on the market for years have been leveraging AI and machine learning (a subfield of AI) to improve performance and boost users’ capabilities for many years.

As AI research progresses and the technology becomes more and more sophisticated, its potential application in logistics in general (and last mile logistics in particular) is only going to grow. To lay the groundwork for future applications, businesses that are interested in the power of these kinds of advances already need to be thinking about undergoing digital transformations and future-proofing their technology stacks. 

Make no mistake: the uses of AI that are already available to delivery organizations are already powerful enough to impact efficiency and boost profitability. But these solutions also have the advantage of positioning their users to more easily take advantage of new applications of AI in logistics as they crop up. 

1. Route Optimization

At heart, AI and machine learning are all about data. To “teach” programs to do certain things as well as—or better than—their human counterparts, they usually need to be trained on large datasets. When it comes to route optimization, some of the most crucial data out there is about travel and service times for traveling from point to point along a route. Given huge amounts of data on previous deliveries, previous traffic patterns in a given region, and more, machine learning algorithms can predict ETAs with greater accuracy than a human planner could ever hope to achieve. 

This might not sound like an earth-shattering use of AI—but in point of fact it can underpin radically smarter, faster, and more cost-effective route optimization processes. How long it’ll take to complete a given delivery after you’ve wrapped up at the previous stop determines how many deliveries you’ll be able to make in a day, and it also determines whether you’ll be able to meet time requests made by your customers. In other words, having accurate ETA predictions is the foundation of any route plan. Without them, you don’t just risk late deliveries, you also risk potentially scheduling more deliveries than your capacity allows for, which can have huge downstream impacts. 

In this way, AI-powered route optimization enables you to generate more accurate routes much more quickly and easily. And this technology isn’t a hypothetical—it’s an application of AI in logistics that’s making an impact on businesses' last mile operations right now.

2. Territory Management

In much the same way that you can leverage AI-powered technology to make your route optimization smarter and easier, you can also utilize AI to streamline sales and delivery territory planning. Historically, this process has been slow, cumbersome, and frustrating for all involved. Often, the software that generates territories is so cumbersome that managing it requires poring over binders of instructions or even calling in outside specialists—and even then the process of generating new territories can take months. 

When you have AI built into your territory planning solution, you can speed the process up significantly. AI enables you to run optimizations on existing territory plans within a matter of minutes, resulting in more efficient territories right off the bat. Often, one of the big challenges that comes with territory planning is bridging the gap between territory planning and route planning, since sales and delivery territories that look efficient on paper often fall apart when you try to build real-world routes with them. 

But with the kinds of AI-powered routing capabilities we described above, you can effectively combine routing and territory planning into a single, cohesive process; as you generate territories, the AI-powered system can automatically build hypothetical routes in the background as a check on the feasibility of the plan. When a plan is set, routes can then be automatically generated and tweaked as need be. Instead of months of tedious effort, the whole process is done in a matter of minutes. 

Like with route optimization, smarter and faster processes aren’t just nice-to-haves. Rather, they can transform the way you administer your last mile logistics operations. When planning new territories takes less than a day, you can update your plans much more frequently to account for changes in your customer roster, distribution network, or anything else that feels like it might warrant a shift. Instead of carrying on with the old plan even after it’s out of date, you can ensure you’re implementing the most efficient possible plans and routes at any given moment.   

3. Predictive Insights

One thing that’s probably clear by now is that AI and machine learning have practical applications in predicting outcomes based on large caches of logistics data. And those applications go beyond predicting ETAs and efficient territory organizations. For instance, some businesses might use AI to predict the likelihood of customer order quantities in advance. Elsewhere in the supply chain, you’re starting to see technology for predicting favorable freight prices (basically the supply chain equivalent of the Google Flights functionality that tells you when prices are low).  

This is another area where data is king. When your operations are fully digitized, logistics processes—including last mile logistics—can generate a lot of operational data, and AI can help you make the most of it. By using past performance to inform your expectations for the future across a number of different functions, you can create smarter, more efficient plans while effectively working to stave off disruptions. 

4. Address Resolution

Though it’s absolutely not the sexiest problem being worked on, normalizing addresses can actually be a serious challenge for some delivery organizations. To generate delivery routes and actually execute on them, you need addresses that are accurate and formatted in a consistent way—something you can’t necessarily count on when buyers are inputting their own delivery information. Without a means of resolving addresses, it’s difficult to ensure that your drivers are actually delivering to the right place at the right time. 

Here, AI is already quietly making an impact. By leveraging machine learning trained on innumerable addresses and delivery locations, last mile logistics operators can speed up and simplify address resolution, resulting in improved routes, more empowered drivers, and happier customers. 

5. Future Proofing for New Applications of AI in Logistics

We’ve discussed just a handful of the applications of AI in logistics that are already making life easier for dispatchers, managers, and drivers across the delivery sector—but there’s no doubt that more innovations are coming down the pike in the future. At least for the time being, new uses of AI in the last mile are likely to involve incremental advances, but they still have the power to impact logistics businesses in ways that we probably haven’t even considered yet. 

But which businesses are actually going to reap the rewards as new AI-powered technologies make themselves available? It’s going to be the ones that laid the groundwork now by prioritizing digital transformation and adopting existing AI-powered solutions. Simply put, by integrating AI and machine learning into existing processes now, you can pave the way for much easier implementation of newer technologies. AI, in other words, can act as a way to future proof your supply chain technology stack—in addition to conferring all the immediate benefits we discussed above—for businesses that are willing to prioritize it now.