Intelligent Logistics with AI
No other technology has changed our society and working environment as fast as artificial intelligence (AI). In addition to well-engineered products, efficient and flexible production and logistics processes are key for manufacturing companies to be economically successful on global markets. That’s why Konstantin Mühlbauer and Lukas Rissmann immediately took the opportunity to join the research team of the new project “KIProLog – AI in production logistics” at the Technology Centre for Production and Logistics Systems (TZ PULS) at Landshut University of Applied Science.
Over the next four years, the two research associates will develop concepts, methods, and algorithms to optimize internal logistics processes. The aim of their research activities is to successfully apply and validate the results in collaboration with local industry. Using artificial intelligence methods to identify the root cause of problems and provide optimization solutions has attracted young scientists to the KIProLog project.
"I am fascinated by the possibility to solve a wide range of relevant problems in the field of internal logistics. State-of-the-art technologies that require complex programming such as rule-based expert systems have reached their limits. AI offers new opportunities to support workers and managers with crucial information and proposals for action, and hence provides solutions to problems that have not yet been properly solved," Mühlbauer states.
Preventing Errors and Increasing Working Efficiency
Due to the increasing range of product variants and the complexity of today’s logistics and production processes, modern manufacturing increasingly depends on planning and control systems. The focus of the project lies on data analysis and machine learning in order to make these systems more intelligent and robust.
Our goal is to increase working efficiency and to reduce the frequency of errors and bottleneck situations by supporting workers and managers with crucial, situation-related information.Prof. Dr. Sebastian Meissner, Production Management and Logistics, Landshut University of Applied Sciences
Strong Support from Industry
“To ensure the applicability of our results and the overall practical impact of such research projects, the integration of industrial partners, especially in conception and validation is of enormous importance,” Meissner states. “This is one of the great strengths of Landshut University of Applied Sciences,” adds Dr. Stephan Schnabel, postdoc at TZ PULS, based on his many years of international research experience.
Germany has an international reputation for being a leader in innovation, especially with regard to so-called German engineering. This is mainly because of medium-sized companies that are often world market leaders in their niches. “In addition to the great practical relevance of our research fields, cooperation with companies in Bavaria is a big plus,” says Schnabel.
Landshut University of Applied Science is therefore particularly proud of its close contact with many companies in the region. For example, KIProLog is supported by different internationally successful industrial partners: MANN+HUMMEL, Kuehne+Nagel, NeoLog, and Agrotel. These companies will help to evaluate the research results of Landshut University of Applied Sciences.
"We are very pleased that we were able to win four diverse renowned and regionally anchored industrial partners for our project. Thus, our project can prove its practicability in different branches," Meissner emphasizes.
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Knowledge Transfer at TZ PULS
At the end of the project, the research results will be implemented within the learning and model factory at TZ PULS to provide crucial knowledge transfer. Interested companies in the region will therefore have clear access to the project results.
"This allows us to discuss our innovations with operational experts in an application-oriented way in order to take on board their requirements, and at the same time, to show state-of-the-art of science and technology to many different companies. The aim of this is also to secure knowledge transfer beyond the project," says Mühlbauer.
“In this project, AI is not only implemented prototypically or just shown as a proof of concept, or only developed as a theoretical alternative to classical methods; rather, we will actually use AI to solve real problems within the industry,” Rissmann adds.
"With this project in particular, we not only want to make an important scientific contribution, but also to demonstrate the practical applicability to industrial companies and, thus, contribute to the dissemination of the results in order to strengthen the competitiveness of Bavarian industry," Meissner concludes.
