When Routes Become Smart: The Role of AI in the Modern Supply Chain
With the rapid advancement of technology and the growing demands of the transport and logistics sector, increasing attention is being paid to the implementation of advanced digital solutions. One of the areas where innovation is making a significant impact is route planning and management, which today faces extremely high demands for efficiency, cost-effectiveness, and sustainability.
Dr Rūta Juozaitienė, an associate professor at Vytautas Magnus University (VMU) and a member of the project SustAInLivWork, which is establishing an Artificial Intelligence Competence Centre, states that route planning is one of the most complex tasks in logistics due to the numerous interrelated variables involved. The main objective of this process is to allocate cargoes to vehicles based on their location and to plan the service sequence in such a way that as many cargoes as possible are handled, with the route being as short and economically efficient as possible.
“To achieve this, numerous factors must be considered, such as the pick-up and delivery locations of cargoes, the dimensions of the cargoes and vehicles, possible restrictions (e.g., refrigerated products or hazardous materials requiring specific transport conditions), and deadlines. It is also necessary to comply with driving and rest time regulations, working hours of companies to avoid delays, as well as plan for stopovers and rest areas,” she notes.
Moreover, route planning does not occur merely at the level of individual trucks but encompasses the entire fleet. This means that solutions must be optimised for the whole transport system to ensure the highest possible overall efficiency. The situation is further complicated by the integration of additional options, such as freight resell or the use of terminal services. These options provide flexibility but also increase the complexity of planning.
“Given the vast spectrum of possible choices, it becomes almost impossible for a human to process such a volume of information and make an optimal decision in real time. This is where AI systems come into play, capable of processing enormous amounts of data in a very short time, ensuring that all constraints are met and that the solution is genuinely suboptimal,” says the VMU researcher.
Enabling Dynamic Route Decisions
According to Dr Juozaitienė, smart systems analysing historical traffic trends, weather forecasts, and current road conditions are used to predict where traffic congestion is most likely to occur and to propose alternative routes in advance. GPS and telematics data allow real-time monitoring of traffic conditions and the making of dynamic decisions regarding route changes.
“For instance, if a vehicle is approaching an area where traffic congestion has suddenly increased, AI can suggest a bypass, thus optimising the route and reducing the likelihood of delays. Real-time data can also improve delivery time predictions, enabling more accurate delivery time estimates for customers,” she explains.
In addition, telematics systems can monitor various operational aspects of the vehicle, including fuel consumption, speed, engine condition, and even tyre pressure. These data help identify mechanical issues before they become critical.
“If a vehicle exhibits signs of a fault, the system notifies the dispatcher, who can send assistance or reroute other vehicles to the affected route, thereby reducing downtime and ensuring smooth operations,” the VMU researcher explains.
AI also plays a crucial role in decisions regarding cargo selection from freight exchanges – choosing not only the most profitable but also the logistically most suitable cargoes that match the current vehicle locations and integrate smoothly into the cargo handling chain.
“Such solutions help reduce the number of empty kilometres travelled and ensure that a truck does not end up in economically disadvantageous locations without cargo collection points. Moreover, the time factor is extremely important in this process: decisions regarding cargo collection must be made quickly because the most profitable and attractive cargoes receive the attention of many carriers and are quickly snapped up, making speed a key competitive advantage,” she notes.
Forecasting Market Changes
Dr Juozaitienė notes that by analysing historical order data and forecasting market trends, AI can significantly contribute to setting optimal transport prices. AI methods allow for more accurate identification and assessment of seasonal price fluctuations and regional pricing differences, integrating this information efficiently into pricing decisions.
“By using advanced AI algorithms, it is possible to develop dynamic pricing strategies that adjust in real time to market conditions and demand fluctuations. These models can consider fleet utilisation rates, cargo flows, and the possibility of connecting cargoes to already planned routes, ensuring that prices are not only competitive but also economically justified,” she highlights.
According to the VMU researcher, while AI methods are also used to optimise pricing, they provide the ability to analyse historical transport exchange data and identify recurring patterns crucial for predicting future trends. This enables monitoring of supply and demand trends across different periods and regions.
“Using forecasting algorithms, AI solutions can assess the impact of seasonal fluctuations, festive periods, as well as economic and geopolitical factors on the logistics market. Furthermore, AI allows for the analysis of the profitability of different routes and regions, taking into account not only actual price changes but also anticipated demand intensity,” she observes.
Such insights give logistics companies the ability to plan routes in advance that optimise vehicle utilisation and better meet market needs. They also allow for data-driven decisions regarding the selection of priority routes, contract negotiations, or even expansion into new markets.
The Future – Autonomous Vehicles
According to Dr Juozaitienė, although AI innovations are profoundly transforming the logistics industry, the last mile delivery stage still remains one of the most complex and costly elements of the logistics chain. For this reason, she believes that more attention will soon be focused on improving the efficiency of this stage.
One of the key directions for development is the creation of a personalised delivery experience, using historical data analysis, assessment of individual client needs, and the integration of smart technologies.
“In this context, the integration of unmanned aerial vehicles (drones) could play a particularly significant role, with breakthroughs expected in the coming years. This technology is already being tested in various countries, and forecasts suggest that in the near future, drones will become one of the main solutions for goods delivery – particularly in densely populated urban areas and hard-to-reach locations,” she says.
Autonomous vehicles could offer a fast, flexible, and environmentally friendly alternative to traditional land transport: they are capable of bypassing traffic jams, taking the most direct routes, and significantly shortening delivery times.
“Nevertheless, in my view, the most important aspect of AI integration will remain sustainability. The application of AI solutions in the logistics sector creates the conditions for building a much more sustainable, efficient, and environmentally friendly supply chain – optimising resource use, reducing emissions, and contributing to the achievement of sustainable development goals,” Dr Juozaitienė observes.