Artificial intelligence (AI) is often discussed in abstract terms: Which processes could be improved? In which areas could it be used? However, there are now plenty of logistics companies that are using AI productively.

Compared to other companies in the German economy, the logistics industry is playing a pioneering role in AI. This is confirmed by figures from the Ifo Institute from August 2023: 13.3 percent of logistics companies in Germany already use AI. 9.2 percent are planning to use it and a further 36.7 percent are discussing possible application scenarios. The Ifo survey also corresponds with self-assessment: according to Bitkom, the majority of logistics companies (56 percent) see themselves as pioneers in digitalisation – in comparison, only 32 percent of the economy as a whole do so. And in which areas are the heroes of transport now relying on AI? A look at the companies shows that there are plenty of live scenarios.

Real-time information in land transport

At Kuehne + Nagel, a key area of application for AI is real-time information in land transport: AI-supported systems are used to monitor shipments, identify deviations at an early stage and proactively inform customers. In concrete terms, this takes the form of a cloud-based platform. “It allows us to track the location of trucks via a dashboard and call up automated status reports as well as temperature and location graphics,” says Tobias Jerschke, CEO of Kühne + Nagel Germany.

Users can set up real-time alerts to respond quickly to deviations. The use of intelligent sensor labels enables monitoring down to the item level. This solution integrates data from various sources into a central dashboard, improving the efficiency and security of the supply chain. The transparent preparation of the database makes it possible to respond more quickly to disruptions and make supply chains more resilient.

Faster booking of freight shipments

Lufthansa Cargo uses AI in various business areas. A specially developed software solution enables faster booking of freight shipments and supports sales staff. “Booking requests are automatically entered into the booking system via email, and customers then receive a fully automated booking confirmation,” explains Jasmin Kaiser, Head of Information Management & Chief Data Officer at Lufthansa Cargo.

With the help of AI and RPA, the relevant shipment data is extracted from continuous text or other file formats and transferred. This means that an average of 8,000 emails are processed each week. The project is the result of the company’s own “AI & Automation Community,” in which experts evaluate and implement potential automation projects. Last year alone, this resulted in around ten new projects, which are now in the pilot phase or already successfully in regular operation.

Completion of address data

At UPS, our Address Classification Enrichment System helps on the last mile by completing incorrect address data and classifying it as a business or residential address. This creates dynamic routes, saves thousands of kilometres every day and has significantly accelerated the expansion of Saturday deliveries. The company is currently testing additional machine learning models that check every address in real time, including geocoordinates, so that every delivery stop can be found even if there are typing errors. At the same time, digital twins of the global network, a domain-specific logistics LLM (Large Language Model) and the NextGen Brokerage platform are being developed. The latter validates customs data in advance using GenAI, weighs risks and automatically transmits approvals to authorities. According to the company, this results in more reliable delivery windows, fewer detours and lower CO₂ emissions.

Platform for knowledge management

HGK Shipping is working on its own AI platform, which will not only be used for knowledge management and dispatching assistance, but will also support predictive maintenance and the HR and administrative environment. The goal is learning logistics that goes beyond traditional transport.

Tracking the carbon footprint

The start-up Gryn sees AI as a tool that will enable more sustainable logistics. The platform uses AI to analyse emissions data across supply chains in real time. It helps companies track their carbon footprint per shipment and optimise routes and transport modes accordingly. In a new version, customers will be able to communicate with their data through AI-generated reports. “They can use the data in various formats and charts to understand and optimise their supply chains,” promises Oliver Ritzmann, CEO and founder of Gryn. By converting raw data into actionable sustainability and logistics insights, AI helps partners comply with regulations.

Monitoring load security

Schnellecke Logistics uses various AI solutions. At its site in Wolfsburg-Warmenau, an AI-supported camera system automatically monitors load securing – faulty securing is detected and reported before the truck leaves the yard. At the Hanover site, several cameras protect people and machines through intelligent environment analysis: if a forklift truck approaches an employee dangerously, it brakes automatically.

The company also relies on AI internationally. In the USA, a chatbot enables employees to report sick easily – around the clock, without waiting times. At the same time, processes are being optimised with Agentic Automation: “In combination with AI, RPA robots are increasingly acting independently, making decisions in context, responding flexibly to changes and working proactively with people and systems,” says Marten Niebuhr, Head of Group Competence Centre “RPA” at Schnellecke Logistics.

Real-time recording of vehicle load status

LUIS Technology, a provider of driver assistance systems, uses camera-based systems that record the load status of vehicles in real time. “This allows free loading space to be used more efficiently, empty runs to be avoided and CO₂ emissions to be reduced,” says Aileen Sommer, Team Lead Product Management. In the area of safety, AI can be used to prevent serious personal injury even more effectively.

AI can also be used to identify potential danger zones by detecting and analysing critical situations – such as people in danger zones – and visualising them as heat maps. The aim is to prevent accidents before they happen.