The project aims to develop an AI-based analysis tool for ERP systems that combines sales data with internal and external factors. This will not only enable general sales trends to be recognised, but also individual correlations, such as discounts or external influences such as weather and political events. This helps to create more precise purchasing forecasts, avoid bottlenecks and reduce excess stock and reduce excess stock, which saves storage costs. In addition, a large language model is also used to explain the AI forecasts in order to ensure the traceability decisions, supported by explainable AI approaches.

This research project is a collaboration between the Bremer Institut für Produktion und Logistik (BIBA) at the University of Bremen, Germany, the Federal University of Rio Grande do Sul (UFRGS), Brazil, the Federal University of Santa Catarina (UFSC), Brazil, and the Federal University of Amazonas (UFAM), Brazil.

The goal of this project is to develop a self-adaptive model selection method for predictive maintenance that is fully integrated into production and maintenance planning. To achieve this, a machine learning-based approach will be developed, enabling the automated selection of suitable prognostic models for different system configurations and conditions. A key aspect is the incorporation of reinforcement learning for the dynamic optimization of machine availability and utilization in real time. This is based on a digital representation of the production system, which allows for the evaluation of decision impacts using production logistics KPIs. This performance assessment enables targeted feedback between meta-learning and reinforcement learning, contributing to the continuous improvement of the system.

A key aspect of the project is the integration of reinforcement learning to dynamically optimize machine availability and utilization in real time. This is based on a digital representation of the production system, allowing the assessment of decision impacts using production logistics KPIs. The continuous feedback loop between meta-learning and reinforcement learning facilitates the ongoing improvement of the system. To validate the developed methods, a simulation-based environment will be created, which replicates the relevant production and maintenance processes with the required level of abstraction. Finally, the developed system will be tested in two industrial use cases in Germany and Brazil.

The 'Smartport Living Lab' project is aimed at overcoming the current challenges facing Bremen's ports in competition with the North Range ports. The objective is to develop innovative systems that will enable a connected and sustainable port industry in Bremen/Bremerhaven. The focus is on the use of flying drones and autonomous mobile robots for reactive and intelligent condition monitoring of the port superstructure. These technologies offer a detailed recording of problem areas through dynamic and individualised monitoring from different perspectives. The systems are developed and tested in decentralised living labs that are connected both virtually and physically. A central component of the project is the Smart Cooperation Platform, which optimises cooperation between the research institutions and partners and creates synergies. This initiative not only enhances the sustainable competitiveness of port locations, but also expands the growth potential in maritime logistics. The project demonstrates Bremen's innovative strength and offers the opportunity to implement research results profitably in the port world.

The PiQASO project addresses the urgent need for quantum-resistant cryptographic solutions to secure critical infrastructures and data in the face of advancing quantum computing threats. As quantum capabilities mature, existing cryptographic methods risk becoming obsolete, jeopardizing the confidentiality and integrity of sensitive information. PiQASO’s primary goal is to deliver agile, scalable, and practical Post-Quantum Cryptography (PQC) solutions that seamlessly integrate into legacy systems without requiring additional specialized hardware.

The project introduces “PQC as a Service” (PQaaS), providing operational implementations of NIST-standardized algorithms like Dilithium, FALCON, and SPHINCS+. PiQASO enables robust encryption, authentication, and identity management across diverse industries, offering flexibility through crypto agility—adaptation to evolving cryptographic needs. The project emphasizes the secure execution of PQC, incorporating programmable accelerators to optimize performance while maintaining resistance to physical and side-channel attacks.

BIBA will provide a comprehensive demonstration scenario - a safeguarding aviation testing infrastructure. It will show how a Distributed Hardware-in-the-Loop (HIL) testing in aerospace will benefit from secure communication channels fortified by PQC, ensuring real-time responsiveness (<100ms) and protecting intellectual property. Through these advancements, PiQASO aims to establish a sustainable path toward quantum-secure critical systems, fostering trust and resilience in a rapidly evolving technological landscape.