Development of future-proof solutions for industrial environments
fortiss shapes the future of industrial technology by developing flexible manufacturing and intelligent production solutions to ensure sustainable competitiveness for companies in a dynamic market environment. Our interdisciplinary approach combines cutting-edge research with practical engineering to realize innovative and future-proof systems for industrial applications.
By addressing current challenges in the manufacturing industry, such as resource scarcity and changing market demands, we enable companies to increase efficiency, flexibility, and resilience. Whether through advanced automation, intelligent system integration, or collaborative robotics – fortiss is a driving force for the next generation of industrial innovations.
fortiss provides scientifically developed potential analyses and industry-specific studies in the areas of AI, Industry 4.0, digital manufacturing, and cyber-physical production systems, tailored to the specific requirements of industrial production. In company-specific workshops, individual challenges and needs are addressed, production processes are analyzed, potential bottlenecks and optimization opportunities are identified, and innovative solutions are developed to enhance efficiency and flexibility in manufacturing. The results can serve as a basis for developing concrete prototypes or initiating collaborative projects.
With the fast fortiss Quick-Checks, fortiss offers small and medium-sized enterprises in industrial production practical online tools for the initial analysis and optimization of digital production processes. These include feasibility studies and the adaptation of specific methods to the respective tasks.
For manufacturing companies that wish to implement innovative digital processes, production methods, or services in pilot projects, fortiss is a reliable, technological, and vendor-independent partner. Depending on the project scope, the institute develops initial concepts up to pre-competitive software solutions (Technology Readiness Level TRL 6), which are evaluated in industrial environments.
The fortiss experts design and implement projects together with industry partners and within public funding programs – whether in bilateral collaborations or larger consortia. fortiss supports companies competently on their path to collaborative research projects, analyzing individual questions and challenges.
In introductory workshops, suitable cooperation and funding opportunities are identified, and companies are supported both technically and administratively in submitting joint project proposals, while offering state-of-the-art research in the fields of software and AI.
fortiss takes on software engineering based on specific simulation environments and offers various opportunities to explore, test, and evaluate new solutions for software-intensive systems. Companies can leverage the expertise of experienced professionals to collaboratively develop solutions for challenges in industrial production.
fortiss’ offering includes:
The fortiss Labs offer companies dedicated spaces where partners from research and industry can interact through the use of existing or newly developed demonstrators. The fortiss Robotics Lab provides modern robotics platforms, simulation environments, and software tools to develop, test, and optimize innovative robotic systems.
As a central research facility, the Robotics Lab combines interdisciplinary methods from the fields of Artificial Intelligence, industrial robotics, and autonomous systems. Using simulations and real-world test environments, new technologies are tested, improvement potential is identified, and the practical implementation of scientific findings is made possible.
The Robotics Lab is located on the 15th floor of the Highlight Towers in Munich and is part of fortiss’ infrastructure for applied research. Here, key questions of modern robotics are explored, innovative control and interaction methods are tested, and prototypes for industrial applications are developed.
By using collaborative robotic systems and AI-based control algorithms, the lab enables practical experiments and the validation of new technologies for flexible and adaptive automation. Companies benefit from a top-tier testing environment where they can experiment with and further develop robotics solutions under realistic conditions.
The tailored training and continuing education offerings from fortiss are aimed at manufacturing companies looking to specifically expand their competencies in software development, automation, and digital production processes. The hands-on training sessions, workshops, and lectures provide both theoretical knowledge and practical skills – a crucial combination for successfully implementing innovative software and automation solutions.
Our training programs cover specific methods and tools, highlight the potential of new software technologies, and demonstrate their practical application in the manufacturing industry. Additionally, we offer customized training programs to equip teams with the necessary knowledge to address specific challenges – from the basics of model-based systems engineering to advanced topics in industrial software development.
The following qualification offerings are available specifically for the manufacturing industry:
These and many other training sessions are available both at our facilities and directly at your location. Feel free to contact us to discuss a tailored training program for your company!
Do you have questions about our offers, are you looking for innovative solutions in the field of industrial software, or do you already have specific ideas for your production processes?
fortiss provides comprehensive software solutions for smart manufacturing, addressing the needs of both production equipment manufacturers and product manufacturers alike. Spanning the entire product lifecycle – from product development to production and quality assurance – our solutions ensure efficient processes, seamless collaboration, and the adaptability required to thrive in dynamic markets.
Machine learning and semantic knowledge representation are transforming production by enabling the development of advanced automation systems – from robot-assisted manufacturing processes to cyber-physical production systems (CPPS). This approach combines deterministic production knowledge with data-driven insights and automated data analysis, such as anomaly detection and the assessment of Key Performance Indicators (KPIs) that are specifically tailored to dynamic environments.
Reliable Architectures for Smart Manufacturing Systems
Comprehensive coverage of all development phases – from requirements and modeling to implementation, analysis, and testing – ensuring adherence to stringent industry standards, supported by structured approaches such as Advanced Product Quality Planning (APQP) to ensure quality and efficiency criteria are met.
Centralized Design and Engineering Integration
Technical assistance systems integrate design, engineering, and production, leveraging semantic interoperability for efficient data exchange, streamlined workflows, and real-time decision-making across the product lifecycle.
Digital Twin Technology
Digital twin technology enables simulations and the exchange of relevant production data. It supports the optimization of manufacturing processes, shortens iteration cycles, and enhances precision in planning, control, and development within production.
Intelligent Control and Automation
Building on the optimization capabilities of Digital Twin technology, which enables simulations and data exchange for manufacturing processes, research combining semantic knowledge and machine learning enhances automated production. This integration allows for flexible task deployment, autonomous operation, and KPI evaluation, ensuring reduced downtime and optimized workflows.
Edge AI and Real-Time Connectivity
Edge computing and AI minimize latency, deliver real-time insights, and enable adaptive, data-driven decisions. Context-aware systems enhance transparency and responsiveness to changes in dynamic production environments.
Predictive Maintenance
Advanced ML and semantic knowledge is combined to analyze process data in real time to identify patterns and predict failures, enabling proactive maintenance and optimizing workflows for efficient, uninterrupted production.
Optimizing Manufacturing with Data Integration and Analytics
Advanced data integration and real-time analytics unlock enterprise data’s potential, enabling predictive decisions, system connectivity, and adaptive production workflows to enhance robustness and responsiveness.
Automated test generation
Creation of complex scenario-based test cases using advanced algorithms, machine learning and model-based techniques to dynamically generate comprehensive production test cases.
AI-Powered Post-Production Testing for Manufacturing Optimization
By combining simulated tests with real production data, we create a realistic testing environment. This technology enables precise fault detection, optimization of production workflows, and maximization of system efficiency.
Centralized data infrastructures enable uninterrupted, automated workflows by integrating data from diverse domains and systems. This integration supports production planners in making requirements-driven design decisions, enhancing both agility and efficiency. From automating production orders to programming robots, these solutions minimize delays, maximize productivity, and foster smarter, more connected, and disruption-free production environments.
Integrated Data Infrastructure Management
Centralizing data across heterogeneous systems ensures cross-system interoperability and decision-making, eliminating process interruptions. Data integration frameworks support dynamic optimization, predictive analysis, and efficient resource allocation for timely, informed decisions.
Automated Compliance
Automated tools support agile development while ensuring compliance with security and regulatory standards, enabling secure, reliable software systems in fast-paced innovation cycles. This approach balances flexibility, speed, and robust security for dynamic manufacturing environments.
Real-Time Insights for flexible Production
Providing production planners with the tools to monitor, analyze, and optimize workflows in real time. These insights enable rapid adjustments to changing conditions, support agile decision-making, and ensure sustained operational efficiency in dynamic manufacturing environments.
Autonomous Manufacturing Systems
Autonomous manufacturing systems use real-time analytics and AI to monitor, adapt, and optimize production. They ensure seamless operations by responding to market demands, minimizing disruptions, and driving continuous improvement for future-ready environments.
Resilient and Connected Automated Manufacturing
Advanced data integration and real-time analytics unlock production data’s potential, enabling smarter decision-making, enhanced connectivity, and adaptable, resilient systems for efficient, future-ready manufacturing.
Retrieval Augmented Generation (RAG) for Production Systems
Modular, scalable systems integrate RAG for intuitive interaction with heterogeneous data sources on historical and real-time process data, allowing operators to query system states, identify and resolve errors, and aid innovation and decision-making processes.
Centralized data infrastructures enable uninterrupted, automated workflows by integrating data from various domains and systems. This integration helps production planners make requirements-driven design decisions, enhancing both agility and efficiency. From automating production orders to programming robots, these solutions minimize delays, maximize productivity, and promote smarter, connected, and disruption-free production environments.
Integrating Legacy Systems with Advanced Technologies
Integrating legacy systems with IoT, AI, and real-time monitoring enhances efficiency and decision-making. Using semantic interoperability, OPC UA, and Mendix, legacy infrastructures connect with modern systems, unlocking the potential of existing assets.
Smart Sensor Integration
Development of standardized interfaces and flexible architectures for the dynamic integration of new sensors. Creating information models for seamless sensor data exchange ensures efficient, adaptable integration into existing systems, enhancing real-time monitoring and operational efficiency.
AI-driven Perception
Leveraging AI for advanced object detection and pose estimation, recognition of human collaborators and their activities, and gesture recognition. These capabilities improve safety and interaction in manufacturing environments by enabling intelligent responses to dynamic conditions and human-machine cooperation.
Monitoring and Quality Assurance
Real-time monitoring enhances precision and operational insights. By focusing on semantic interoperability, these solutions ensure seamless data integration, automatic data segmentation and interpretation, and support cross-functional collaboration. As a result, data-driven decision-making is enabled, process accuracy is improved, and errors are minimized.
Adaptive Error Response and Increased Reliability
Utilizing formal knowledge models combined with data-driven anomaly detection and error classification models to enable production systems to respond flexibly to faults. This approach minimizes downtime and enhances production security by addressing external influences and compensating for unforeseen events.
Smart Variant Management
Enabling autonomous production of multi-variant configurations, with the capability to automatically analyse and adjust production systems for customized single-unit orders. This approach enhances flexibility in production lines, allowing for efficient management of diverse product variants while ensuring high-quality, individualized production.
The development of collaborative systems for industrial production focuses on a human-centered approach to create solutions that improve both productivity and occupational safety and user-friendliness. Scalable infrastructures enable seamless human-machine integration, ensuring resilience and adaptability simultaneously. Modular architectures and innovation-driven methods promote operational stability and agility for next-generation manufacturing.
Intuitive Robot Programming
Research on industrial robot systems using semantic process descriptions enables financially viable automation of small-lot production. Advanced semantic technologies enhance usability, accuracy, and flexibility, enabling efficient, adaptable, and innovative human-robot collaboration for agile manufacturing.
Teaching by Demonstration
This technology enables robots to learn from human actions through observing demonstrations of tasks, simplifying programming and enhancing worker involvement. It streamlines automation integration in flexible manufacturing, fostering collaboration and upskilling.
Collaborative robot systems (Cobot)
Research on collaborative robots (Cobots) enhances human-robot cooperation, improving quality and efficiency. By integrating Cobots into workflows, manufacturers foster flexibility in the design of production lines and innovation, driving advancements in smart, adaptive manufacturing.
Mixed-Skills Factories
Research explores models for integrating human and robotic capabilities in production, focusing on aligning skills, optimizing integration, and enhancing cooperative flexibility. The goal is to boost productivity, empower workers, and improve adaptability while maintaining control.
With the successful completion of the ELEANOR project, fortiss is advancing neuromorphic computing for real-time, energy-efficient robot control. By combining an event-based camera for sensory input with a neuromorphic chip for computation, the project demonstrates how biologically inspired hardware can improve precision, adaptability, and efficiency in industrial robotics.
Prof. Dr. Michael Beetz, expert in plan-based control and robot learning, will join fortiss as a Research Fellow in "Platform Engineering." In this interview, he discusses his past projects at the University of Bremen and his collaboration with fortiss.
in the European Connected Factory Platform for Agile Manufacturing
![[Translate to English:] Bildschirm mit Anwendung aus der Industrieproduktion](/fileadmin/_processed_/6/7/csm_fortiss-produktion-use-case-1_7f1a4e095b.jpg)
![[Translate to English:] Bildschirme in der Industriefertigung](/fileadmin/_processed_/f/c/csm_fortiss-produktion-use-case-2_89e4084ffa.jpg)
![[Translate to English:] Produktionshalle](/fileadmin/_processed_/1/c/csm_fortiss-produktion-use-case-3_a72de9a5dc.jpg)
![[Translate to English:] Roboterarm in Produktionsumgebung](/fileadmin/_processed_/0/7/csm_fortiss-produktion-use-case-4_6d579a8601.jpg)
![[Translate to English:] Prof. Dr. Beetz](/fileadmin/_processed_/1/b/csm_fortiss-interview-prof-beetz-header_6bff301de2.jpg)
![[Translate to English:] Roboterarm und Hand](/fileadmin/_processed_/a/7/csm_fortiss-projekt-corinne-header_4b0d4e4b95.jpg)
![[Translate to English:] fortiss Whitepaper Platform Engineering - Towards pervasive, robust and trustworthy platforms](/fileadmin/user_upload/06_Ergebnisse/Whitepaper/fortiss-Whitepaper-PlatformEngineering-mini.png)
![[Translate to English:] fortiss Whitepaper Knowledge as Invariance – History and Perspectives of Knowledge-augmented Machine Learning](/fileadmin/user_upload/06_Ergebnisse/Whitepaper/fortiss_whitepaper_knowledge_as_invariance_mini.png "[Translate to English:] fortiss Whitepaper Knowledge as Invariance – History and Perspectives of Knowledge-augmented Machine Learning")
![[Translate to English:] fortiss Whitepaper Praxisnahe Einführung von Model-based Systems Engineering – Vorgehen und Lessons Learned](/fileadmin/user_upload/Dateien_MIT_Referenzen_-_Zuordnen/Bilder/fortiss_MBSE_Whitepaper_mini.png "[Translate to English:] fortiss Whitepaper Praxisnahe Einführung von Model-based Systems Engineering – Vorgehen und Lessons Learned")
