Is artificial intelligence useful in logistics?

The logistics sector has been one of the most discussed and written about in recent times. Our ways of life and our societies as we knew them are undergoing a significant transformation reflected in consumer habits. It is a fact that the way we buy has changed due to technological development, the outsourcing of production, and an endemic lack of time among consumers. To these factors, we must also add an unexpected pandemic that restricted mobility for months and, even today, limits gatherings in enclosed spaces. The consequence of the concatenation of all these circumstances, both structural and unforeseen, has been the exponential growth of logistics activity.

And the ceiling has not yet been reached. Logic and estimates suggest that digital commerce will continue to grow in a globalized and hyper-connected world, where logistics plays a primary role as a vehicle for raw materials and finished products between destinations worldwide. The business sector as a whole expects an increase in turnover in 2022 of more than 12%, with forecasts for logistics companies exceeding 26.5%; in fact, according to a study by the Adecco Group, it will be one of the five labor fields with the highest job creation until 2025. Furthermore, based on consolidated data, the evolution is more than palpable. In Spain, the confluence of the aforementioned circumstances has led to a 36% increase in logistics activity in 2020, according to Astound Commerce.

Benefits of technology in the logistics process

To cope with the estimated growth in transactions, it is not enough to put more means of transport on the road, regardless of their type. As we have already mentioned in other articles, it is necessary to make processes more efficient, on the one hand, to maximize the profitability of each operation carried out, and on the other, to increase sustainability ratios on every journey undertaken.

And this can only be achieved through technology and, more specifically, through Artificial Intelligence and Data Science, whose application to transport has given rise to what is known as logistics intelligence. It is now possible to work at every link in the supply chain with the support of new disruptive technologies, imbuing the process with a data-driven philosophy. Let’s look at the specific advantages for logistics, the subject of this post.

Big Data applications in the logistics sector

Predictive systems:

Demand Forecasting: Tools such as Demand Forecasting allow us to have highly reliable estimates of product output over a certain period. By knowing what we are going to sell, we can efficiently manage warehouse resources, handling the entry and exit of references based on objective data rather than the experience of past campaigns.

Predictive maintenance: By applying prediction to maintenance processes, it is also possible to optimize the working time of warehouse machinery and the fleet to guarantee performance, avoiding unexpected downtime resulting from sudden breakdowns. Predictive maintenance monitors machinery performance based on multiple parameters to determine when a deviation from predetermined values indicates the need for preventive or reactive actions. In this way, fixed intervention schedules and unnecessary downtime or parts changes are eliminated, increasing equipment performance.

Digital Twins: Also known as mirror systems, these allow us to create a digital copy of a real environment on which to work and experiment without affecting ongoing processes. Digital twins are extremely useful for testing the effectiveness of new procedures (routes, facilities, personnel, etc.) without taking any risks.

Location systems:

Goods localization: Warehouse product exit or reception processes were among the first where technology was used as a means to simplify and streamline procedures. Working with barcodes and RFID coding allows for knowing the exact location of the product and digitizing inventory control.

Smart warehouses: As we are seeing, warehouse management is much simpler with the support of technology, to the point that large distribution centers like Amazon have automated the internal distribution of references with smart conveyor belts.

Product and fleet traceability: The development of the Internet of Things (IoT) has allowed for the evolution of tracking systems for items and vehicles, which can now be monitored in real-time to update delivery times, record incidents, and resolve issues arising en route, for example.

Warehouse identification systems:

Code reading: We have already mentioned the utility of barcodes and RFID coding in warehouse management tasks, but these systems, and the evolution of computer vision, as we will see later, also make it possible to identify products as they pass, for example, through a conveyor belt equipped with readers, or to recognize batches of packaged goods.

Automatic counting systems: With the development of computer vision, a significant leap has been made in some of the processes associated with transport; a clear example is the automatic counting of parcels, products, or unit materials arranged together.

Knowing, for example, the number of rebar units loaded onto a truck for reception is infinitely faster, more precise, and accurate. This load, consisting of identical small-diameter elements, is counted in tenths of a second without error, which streamlines and optimizes the work. Another clear example with the same benefits is the counting of products as they pass through conveyor belts, such as freshly unloaded pieces of fruit.

AI-based planning systems:

Load planners: These tools maximize the use of available transport space by arranging the load optimally based on parameters such as size, weight, fragility, or unloading time.

Route planners: Big Data and Artificial Intelligence also come into play in route planning processes, through which logistics operations can reach their maximum levels of efficiency. They can be used in all types of transport operations (maritime, air, and land) to design the most efficient routes by exploiting the data associated with these operations (type of operation, intermediate loads, weather, vehicles to be used, schedule, etc.). Furthermore, as noted in the case of traceability, fleets are monitored in real-time, making it possible to resolve unexpected events by recalculating routes.

Management systems:

Robotic Process Automation: or RPA (Robotic Process Automation) simplifies and streamlines administrative tasks of any kind, including those associated with logistics management, which are highly repetitive and of low added value (delivery notes, invoicing, e-CMR, etc.). Thanks, once again, to artificial intelligence and machine learning, large volumes of desktop tasks can be resolved, while also avoiding errors in the process.

Artificial intelligence in the supply chain

As we can see, new technologies have been progressively incorporated into the entire supply chain, with a significant impact on logistics. This evolution began with the benefits provided by identification code reading and has evolved as technology itself has advanced. Predictive systems, planners, and computer vision allow logistics operations to reach their highest levels of efficiency.

There is no doubt that we are in the era of Artificial Intelligence and Big Data, and it is mandatory, not optional, to move towards Logistics 4.0.