Artificial intelligence (AI) has been gaining ground in road freight transport in Rio Grande do Sul, providing advances in operational efficiency and cost reduction. With sophisticated algorithms, technology allows for route optimization, anticipate demands and make fleet management more strategic. However, despite the transformative potential, its application in the sector can still be expanded, especially in the intelligent use of already available data.
For Junior Cavalca, member of COMJOVEM Porto Alegre, the issue is not just adopting AI, but to structure and use correctly the information that the sector already has.
Before everything, we need to demystify artificial intelligence. Many times, we put different technologies under the same label and forget that data management, machine learning and other tools are complementary. In transport and fleet management, this becomes even more evident. How many carriers actually monitor loading times, unloading and the unproductivity of vehicles? The curious thing is that this information already exists. About 90% of trucks have trackers, but companies still do not use this data strategically. The Internet of Things and big data already provide solutions to collect and analyze this information, but the great differential lies in how to apply AI to transform this data into intelligent decisions, explain.
How it works in practice
In a carrier that strategically uses artificial intelligence, the operational routine is significantly optimized. Even before a truck leaves for delivery, algorithms analyze traffic data, climate and historical routes to define the most efficient path, reducing fuel costs and travel time. During the journey, sensors monitor the vehicle's performance, alerting about the need for preventive maintenance, avoiding mechanical failures and delays. In the warehouse, AI systems cross-reference stock and demand information to enhance loading, ensuring better use of space and shorter waiting time. Furthermore, artificial intelligence identifies patterns of driver behavior, suggesting adjustments in driving to reduce the risk of accidents and improve operational efficiency. With these solutions, the company not only reduces costs, but also improves the predictability and quality of the service provided.
