AI-based systems can analyze very large amounts of data about vehicles, drivers, and routes. This makes it possible to adjust schedules and routes, make better use of transportation resources, and reduce fuel consumption by up to 10-15%.
Intelligent systems equipped with machine learning capabilities can predict potential breakdowns months in advance based on data from sensors installed in vehicles and other equipment. This makes it possible to schedule repairs and maintenance at convenient times, minimize downtime, and avoid unplanned stops on the road.
One example of the use of AI in fleet management is DB Schenker, a global leader in the logistics industry. The company uses advanced AI algorithms to optimize transportation planning, demand forecasting, and offer management. In Bulgaria, for example, the company used the Transmetrics AI solution to improve vehicle utilization and reduce transit times for bulk shipments.
In air transportation, the company is using a hybrid simulation and forecasting tool that allows for customization of simulations and is based on historical data. By using AI, DB Schenker is not only accelerating its digital transformation but also securing a long-term competitive advantage in the logistics market.
Source: DB Schenker (https://shippingwatch.com/logistics/article14448745.ece)
Modern AI-powered mapping systems can analyze traffic congestion in real time, search for detours, and suggest optimal routes for drivers based on current conditions. What’s more, machine-learning algorithms can help better plan the distribution of loads so that they are transported over the shortest possible distances. This translates directly into lower operating costs.
One example of a company specializing in AI solutions for route optimization is the American firm FourKites. They’ve developed a real-time supply chain monitoring platform that leverages data and machine learning to enhance transportation visibility and efficiency.
One of their clients, Henkel, benefits from using the FourKites solution by having access to real-time data on the location and estimated time of arrival (ETA) of shipments. This allows them to better plan their tasks and respond to any potential delays.
FourKites has also brought additional benefits to Henkel, such as time and cost savings, improvement in the quality and accountability of LSP (Logistics Service Providers), fair dispute resolution, and avoiding penalties for delays. In 2024, Henkel plans to track almost a million shipments using FourKites.
Source: Four Kites (https://www.fourkites.com/platform/)
Artificial intelligence is adept at analyzing massive amounts of data to accurately predict demand for specific goods and raw materials. As a result, inventory can be managed more efficiently, warehouses can be replenished more accurately, and out-of-stocks can be reduced.
Two popular tools that use AI and machine learning for supply chain optimization are:
Autonomous robots equipped with artificial intelligence modules are already at work in many modern warehouses and logistics centers. They are capable of picking orders, packing products, and transporting pallets of goods. Machine learning algorithms enable these robots to recognize individual goods and packages, plan their own paths around the warehouse, and even communicate with employees.
What happens when a product, packed and prepared by a robot, is ready to hit the road? This opens the door to the implementation of AI in autonomous vehicles. One example is the T-Pod autonomous truck, which is currently being tested in DB Schenker distribution centers. It can be controlled by an operator while driving on the road or, thanks to the implementation of AI, it can autonomously transport pallets of products, avoiding obstacles along the way. Navigation is facilitated through the use of cameras, radar and depth sensors.
The DB Schenker T-Pod is the first vehicle of its kind to be approved for public roads in Sweden. It can carry up to 20 tons of cargo and has a range of around 200 km on a single charge.
Source: DB Schenker (https://www.dbschenker.com/)
Data from in-vehicle sensors, warehouse automation systems, and shipment locators can be analyzed in real time by artificial intelligence algorithms. This allows for making accurate business decisions instantly and improves the efficiency of the entire organization. For example, a system equipped with an AI module can help respond immediately to delivery delays and notify customers or take preventative measures.
The OLX team used machine learning to build a predictive ETA model, which in transportation and logistics stands for Estimated Time of Arrival. The model takes into account such factors as:
The model was trained on data from over two million transactions and tested with data from six countries. The ETA model achieved very high accuracy and precision, and it demonstrated the ability to adapt to changes in market and operational conditions. The ETA model has helped increase customer trust and satisfaction, as well as enhance the efficiency and profitability of the delivery process.
Intelligent monitoring systems equipped with AI modules not only protect the assets of transportation companies. By analyzing images from cameras and data from sensors, they can assess driver behavior and detect signs of fatigue, suggesting breaks during the journey. Moreover, machine learning algorithms, continuously analyzing incoming telemetry data from vehicles, can predict potential faults well in advance.
And so, the Israeli start-up Cortica applied neural networks to analyze engine sounds for early detection of impending malfunctions. Companies like Continental and ZF Friedrichshafen AG offer similar solutions for predictive vehicle diagnostics for carriers.
Experts agree that due to artificial intelligence, the TSL industry will undergo a complete transformation within the next ten years. Autonomous trucks will become the standard on roads in the United States and will start appearing more frequently in other parts of the world. Meanwhile, in warehouses, the majority of operations—from order picking to loading—will be handled by robots.
Thanks to AI, transportation and logistics costs will decrease by as much as 30-40%. Delivery times will also be shortened through route and loading optimization, as well as the implementation of intelligent city systems that facilitate vehicle movement during the final kilometers of the route. The integration of AI in logistics will enhance customer service quality, and the risk of human errors will be nearly eliminated.
Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)
In conclusion, systems using machine learning and AI algorithms in transportation have great potential in the TSL industry that is just beginning to be tapped. Their implementation is an opportunity to significantly reduce costs, shorten delivery times, improve transportation safety, and better serve customers. To be successful, however, the implementation of these technologies must be approached strategically.
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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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