Our NextBase research project is revolutionizing intralogistics with artificial intelligence and machine learning!
Our customers benefit from the initial results of our AI research project. As part of the three-year NextBase research project, we are working with RISC Software GmbH on the topic of artificial intelligence and machine learning in Manufacturing and intralogistics. This research activity will soon open the door to prescreptive analytics and next-level intralogistics intelligence, especially for our customers at OneBase®WORLD, as the focus is primarily on simple integration and upgrade options for our existing and future customer solutions.
Two AI use cases in the field of intralogistics were presented at the workshop for the interim report on July 17. The implementation work is already in full swing and the first tests have been successfully completed.
Maximizing the transport throughput
In use case number 1, the focus is on optimizing the collaboration of multiple means of transport in a shared work area using artificial intelligence. In intralogistics, these are often cranes on a shared crane runway, or storage and retrieval machines and shuttles in an overlapping travel area.
In contrast to the calculation with a static set of rules, the optimization of transport movements is carried out dynamically using AI algorithms. A meta-heuristic search algorithm optimizes the transport throughput, taking into account predicted processing and means of transport movement times.
Intelligent avoidance of stock transfers
In the second use case, machine learning is used to take into account the inbound forecast of the production facilities and the predicted outbound when reserving storage capacities. This allows usable storage capacity to be maximized and avoidable stock transfers to be reduced. Hyperparameter Optimization’s AI approach is used as an automated search tool for machine learning models. Results such as fill level, number of stock transfers, target times or downtimes are evaluated in the search cycle.
In addition to real-time information on current warehouse occupancy and upcoming transport order data, which is provided by OneBase®MFT in the digital twin, historical data from the knowledge base is used by machine learning methods to incorporate future storage and retrieval operations into the warehouse strategy. This pioneering use of AI can also significantly increase warehouse efficiency in this use case.
The two use cases impressively demonstrate the potential that lies in intralogistics optimization. Numerous other use cases have already been identified.
In the following year, the research project will focus on the use of AI in production process and system control.
Look forward to the future of digital manufacturing and intralogistics with AI at OneBase®.
