AI for businesses: efficiency and innovation in logistics

Artificial intelligence (AI) is increasingly becoming a crucial tool in logistics, especially when it comes to making complex decisions and increasing efficiency along the entire value chain. The answer to logistical challenges always lies in the data. The more and more accurate data is available, the better the quality of the answers. Thanks to data, artificial intelligence closes the gaps in experiential knowledge. In times of a shortage of specialists, artificial intelligence enables the efficient transfer of knowledge and helps to secure expertise that would otherwise be lost. AI solutions for logistics use cases are now available to companies of all sizes, especially those that have not yet been able to build up their own resources and expertise in the field of AI or do not wish to do so.

When it comes to optimizing logistics processes, artificial intelligence is particularly useful for finding answers to the following questions:

Which solution is likely to be the right one?

Analysis and forecasting with AI

One of the main advantages of AI lies in its ability to recognize patterns in large amounts of data in order to predict future developments. With predictive analytics based on historical and real-time data, logistics can react more precisely to possible bottlenecks, fluctuations in demand or supply chain interruptions. The aim is not only to react proactively to problems, but even to prevent them.

What is the next and best step to optimize the supply chain?

Process control through AI

AI-based systems provide precise recommendations for action that can be integrated into process control. This not only makes decisions faster, but also allows them to be made on a sound database that optimizes the entire process flow and avoids bottlenecks.

How can decisions be made?

AI support systems

AI can not only collect and store data, but also present it in an understandable, action-oriented way: chatbots or AI agents explain the collected information and prompt people to take action.

In any case, AI solutions must be sustainably and efficiently implemented in a way that is tailored to the challenges, needs and goals of companies. Not every AI application is equally suitable for every company. Needs assessments provide the basis for understanding in which sections AI can offer a company the greatest added value. On this basis, specific use cases can be developed. This involves identifying potential processes that can be optimized through the use of AI – whether it's to improve efficiency, increase transparency or reduce costs. 

Companies that want to optimize and automate their processes and products using AI are now faced with the challenge of identifying the right tools or steps. It is recommended that you start with small use cases to get to know and understand the advantages and limitations of the processes and methods. Developing a solution for a specific part of the problem and then expanding it to larger use cases is often easier than trying to develop a comprehensive solution from scratch.

Numerous research and industry projects are now also providing AI solutions as AI-as-a-Service. AIaaS enables companies to access advanced AI technologies without having to maintain their own IT infrastructure.

AI-as-a-Service: AI apps for forwarding agencies

One example of this is the platform “Omnistics” from Fraunhofer IML. “Omnistics” – a blend word of the Latin “Omni” (all) and “logistics” – currently combines a total of four apps that transport companies can use to optimize their processes in a targeted and decisive way: Capcast, a comprehensive capacity planning solution with advanced forecasting technology and an assistant function for action planning; Pretime, a multimodal arrival prediction; LoOmni-Chat, an intelligent chat and voice assistant; and Frostimate, a strategic and tactical cost estimation tool for freight rates. The apps were developed as part of industry-funded contract research and publicly funded research projects.

Omnistics was developed specifically for moving companies without extensive IT infrastructure. While large logistics service providers have often developed their own solutions or purchased them, neither of these options is generally available to small and medium-sized companies.

“Artificial intelligence is a key to achieving even better ecological and economic goals in logistics, paving the way for greater sustainability.”

- Prof. Dr. Uwe Clausen, Institute Director

Companies looking for specific AI solutions will also find what they are looking for in open source components that can be used free of charge. With the hardware and software components in the three sections “Transport Information”, “Computer Vision” and “Automation & IoT”, developed by Fraunhofer IML in the Silicon Economy research project and published as open source, companies now have new perspectives for getting started – ideally in a group with other companies. The open-source components are predestined for further joint development. Time, costs and resources can be saved through cross-company collaboration, moderated by Fraunhofer IML researchers.

The open-source components of the Silicon Economy address various challenges in logistics and supply chain management. They are freely available to companies in the Open Logistics Repository of the Open Logistics Foundation. The institute's researchers support companies in adapting and integrating the components into their respective IT infrastructures – here are a few examples: