Article Artificial intelligence in logistics

Choosing the optimal route, planning rest stops and avoiding the most congested and dangerous routes are among the many challenges that are part of the daily work of logistics.
from 11 questions
Cancel filter

Until recently, tasks related to the organization of transport were performed manually or with the help of a spreadsheet. Nowadays, forwarding usually uses special route planning software that allows the coordination of many variables at once. Artificial intelligence also supports the transport industry.

Software for dispatchers – saving time and money

With a fleet of tens of vehicles, hundreds of serviced addresses and tens of thousands of kilometers, the number of possible combinations reaches hundreds of millions. The right dispatching software must take them all into account and analyze them in order to choose the optimal solution in terms of costs, deadlines and unexpected events on the road, which require an immediate decision of the dispatcher.

Benefits? Acceleration of order execution by up to several tens of percent and reduction of transport costs by up to 15% thanks to the current profitability analysis. Such programs undoubtedly include Tasha, software developed by the fast-growing Czech company Solvertech. This program uses advanced heuristic models and allows you to reduce costs from day one after implementation. Such software is becoming standard today.

AI in logistics – it’s not a fantasy

In a recent report by the consulting agency McKinsey [1] we can read that artificial intelligence in logistics is currently applied in four areas: customer service, service and product development, marketing and sales and of course in supply chain optimization (its role is evident especially in software for route planning, planning, demand and supply forecasting, warehouse automation, delivery control for defects and damage, price dynamics control, intelligent road systems such as Valerann).

In a few decades, we will see further automation of the supply chain. Chatbots in purchasing departments already help process orders, track shipments, and provide service information. Machine learning technology helps anticipate demand for specific products, continuously reduce fuel costs and, thanks to supply chain price stimulators, are more likely to predict cash flows in the coming months. Support for robotics and automated controlled vehicles (AGVs) is of great importance for inventory management.

AI means save on costs

Artificial intelligence brings huge savings. DHL has implemented a system that is highly likely to predict air delivery delays due to cyclical congestion or extreme weather conditions – although air transport accounts for only 1% of total world freight traffic in terms of tonnage, in terms of value it is up to 35%. Another courier giant, UPS, has set aside a significant portion of its $ 20 billion budget over the next few years to invest in an artificial intelligence implementation that would process and analyze the tens of billions of data records the company generates each month.

An example of the involvement of big data in logistics, which has already gone down in history, was when UPS virtually eliminated the delivery vehicle turning to the left. After analyzing the routes and results of fuel combustion, it was found that the cars are standing on the engine when turning left and are waiting for priority. Today, deliveries with the UPS logo turn right or go straight in 90% of cases, saving the company 37 million gallons of fuel a year.

Intensive work is also being done on autonomous vehicles. 75% of transport companies expect to get on the road in the next ten years [2]. And while these are very optimistic predictions, sooner or later it will certainly come.

[1] https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain

[2] https://www.iru.org/resources/newsroom/technology-and-automation-will-define-future-road-transport