Artificial intelligence to optimize the last mile

One of the great transformations brought about by the pandemic has been the shift in consumer habits. The way people purchase products has undergone a radical change, shifting in large part toward digital shopping, which has gone from being a complement to becoming a genuine alternative to shopping in physical spaces.

This disruptive transformation has created an enormous logistical challenge with a strong impact on the last mile. It is estimated that between 35% and 40% of all traffic in cities is related to shipment distribution, with 80% of those deliveries corresponding to e-commerce. And these figures are only set to increase in the coming years. The push toward digitalization of commercial activity has created a new buying-selling framework that is here to stay.

Traditionally, transportation, and especially the last mile, has not played a particularly prominent role among the links in the supply chain, although it’s true that perception of its importance has grown in recent years. Consumers are increasingly demanding when it comes to the quality of the products they purchase, delivery times, and the level of service provided. On top of that, delivery volumes have multiplied and traffic through city centers has become increasingly complex, putting logistics front and center—something that can determine whether a company retains or loses customers.

Digital transformation: greater efficiency and profitability

As a result, one of companies’ major concerns today is distributing their products in the shortest possible timeframes, treating every delivery as if it were unique, and doing so at the lowest possible cost. This poses real challenges for logistics departments, particularly when it comes to the last mile. As noted, the elements of this equation are difficult to balance, although the development of Big Data, Artificial Intelligence, and Internet of Things (IoT) systems makes it possible to improve the efficiency of every process in the supply chain, and of course to move toward Transportation 4.0 and Logistics 4.0.

In the new context we’re living in, it’s essential to begin digital transformation processes in order to be efficient and profitable. The last mile is heavily dependent on external factors that make service costs highly variable, such as weather, traffic, or fuel prices. It is therefore the most difficult process in the supply chain to plan and quantify, with a high degree of uncertainty that prevents optimal performance.

To address this challenge, the application of new technologies and advanced data analytics makes it possible to build environments that enable work based on objective facts and certainties. The shift toward Logistics 4.0 and Transportation 4.0 makes it possible to plan the best routes, make the most of load capacity, monitor ongoing operations in real time, and estimate product demand—and therefore future transportation needs. Let’s look at some examples of how this is applied:

Demand forecasting: based on artificial intelligence and advanced analytics, demand forecasting systems compile and analyze a company’s internal and external data using algorithms specifically designed for these tasks, generating product demand predictions.

In this way, by knowing in advance the warehouse output requirements—and even the zoning of that future demand—transportation operations can be optimized. When demand forecasting is combined with the other tools discussed here, transportation efficiency and profitability ratios multiply.

Load management: Load management systems use artificial intelligence to analyze the packages that need to be transported and the capacity of the fleet, in order to get the most out of every inch of the vehicles and every ounce they can carry.

These tools examine loads by size, weight, content, and destination, organizing them in a way that ensures the integrity of shipments and maximizes the use of the fleet’s load capacity.

There are also new ways of operating, such as the one used by Amazon, based on pre-sorting packages, which classifies parcels in stages prior to loading them for transport.

Route planners: through the analysis of fleets’ internal and external data, and the use of Artificial Intelligence, these systems make it possible to optimize delivery routes, which are very costly in the last mile due to multiple drop-off points. Taking into account variables such as available vehicles, delivery schedules, traffic, regulations, weather, and more, they are able to design the most efficient routes for each service.

With less time on the road, fewer kilometers traveled, and lower fuel consumption, deliveries become more competitive, cost-effective, and sustainable, reducing CO2 emissions and leaving a smaller environmental footprint.

Fleet management systems: fleet management systems make it possible to track vehicles in real time thanks to onboard sensors. With Internet of Things (IoT – IIoT) devices, it’s possible to know at all times the geolocation of each unit, the route taken, the speed, and more—making decision-making in logistics processes much easier.

These systems can go a step further by using machine learning techniques to learn from every action taken, enabling them to issue alerts and recommendations—based on predefined algorithms—to the teams responsible for fleet management.

360° visibility: The use of the disruptive new technologies we’ve been discussing in the transportation sector in recent years also provides a full 360° view of operations. In this case, visibility over the last mile is complete for the company in terms of vehicle location and delivery management—information that can also be shared with the recipient, allowing them, for example, to narrow down the delivery window for their shipment.

Conclusion

As we can see, transportation operations—especially those involving the last mile—have multiplied in volume, demands, and complexity, creating real headaches for businesses. Faced with this new and abrupt landscape, digital transformation opens the door to achieving more efficient, sustainable, and profitable transportation.

Data analytics makes it possible to obtain objective information on which to base decisions, design routes, make full use of available vehicle space, and optimize operations—new ways of managing logistics that ultimately result in more competitive transportation, with lower economic and environmental costs.