Qunevo: Start-up from OWL ensures greater efficiency and flexibility with AI-supported production planning
Efficient and flexible detailed planning is crucial in modern production, especially for companies with a high number of variants, small batch sizes and complex material flows. But how can detailed planning be simplified for companies? Qunevo, a spin-off from Bielefeld University of Applied Sciences, provides the answers. The it’s OWL project ‘SUPPORT’ has already shown how production planning can be improved with the help of artificial intelligence (reinforcement learning). Parts of the current founding team were significantly involved in the development and implementation of the project and were able to gain valuable experience and expertise in this area. The start-up from OstWestfalenLippe is building on this and has developed a cloud-based solution that simplifies production planning with a declarative approach: instead of planning processes manually, production managers can simply enter the desired result – the algorithm takes care of the planning details itself.
Until now, production planning has often had one disadvantage: it is complex, expensive and not very flexible. The introduction of conventional planning systems often fails due to high costs, long implementation times and the inability to take into account the complex requirements of modern production processes. The result: inefficient processes, a high susceptibility to errors and inflexible planning. “Order deadlines and resource allocations are often determined by idealized heuristics that do not adequately capture the complexity of modern production environments. This leads to inconsistencies and conflicts that then have to be resolved manually in the planning board or in Excel. The consequences are stress, chasing deadlines and inefficient processes,” says Stefan Görlitz, one of the founders of Qunevo. To avoid this, Qunevo has developed a new approach. In the SUPPORT project, initial practical tests in collaboration with Miele, Isringhausen and Fraunhofer IOSB-INA have shown how AI agents have a positive impact on efficiency and flexibility in production planning. Qunevo uses this promising idea of automating production planning using artificial intelligence and has developed an innovative approach: declarative production planning.
Declarative production planning: algorithm supports planners
“With our declarative approach, we relieve production planning of routine tasks and automate the often tricky determination of order sequences and machine assignments,” explains Dr. Felix Grumbach, one of the founders of Qunevo. This so-called declarative production planning is one of the key innovations of the start-up Qunevo. It means that planners only specify the goals, while the algorithm independently finds the best way to achieve them. This reduces the risk of human error and increases planning accuracy – a clear advantage in modern manufacturing, where rapid adaptation to unexpected events is crucial. Nevertheless, the selection of the final production plan is left to the planners themselves to ensure that special requirements and real-time conditions can be met at all times.
The cloud-based solution integrates seamlessly into existing IT systems as an add-on and picks up where conventional systems often reach their limits. The tool takes into account detailed requirements and framework conditions in the production process. At the same time, it can address specific secondary conditions that are crucial in day-to-day production, for example: Order 4711 is to be produced on machine X on Tuesday morning because the customer is in the factory for an inspection.
Flexibility and efficiency through artificial intelligence
Unlike traditional methods, Qunevo’s solution does not require complicated mathematical modeling – much can be data-driven or implemented using natural language. “Our algorithm uses an advanced optimization model that is tailored to the requirements of a wide range of manufacturing processes, especially the challenges of flexible and discrete production. With the help of machine learning, we can create high-quality and realistic schedules very quickly and also teach the algorithm specific constraints,” says Grumbach.
Advantages for the industry at a glance
Declarative production planning promises manufacturing companies a number of advantages:
- Maximum flexibility: plans can be adapted quickly and easily in the event of unexpected changes.
- Increased efficiency: Automated processes save time and resources and relieve staff of routine tasks.
- Minimization of errors: Target-oriented planning reduces the susceptibility to errors and ensures greater process accuracy.
- High scalability: The system can be easily adapted to changes and growing production complexity.
Pilot companies wanted – the next step towards market maturity
Since October 2024, Qunevo has been receiving support from the ERDF startup.transfer program, which aims to promote the path to market maturity within 18 months. Companies that want to optimize their production processes with an innovative approach are invited to apply as pilot users and actively participate in the further development of the solution. The Center for Entrepreneurship (HSBI) and Prof. Dr. Pascal Reusch will support the founders and accompany the further development of the business model. The involvement of new pilot partners should help the start-up to further refine the technology and adapt it to the needs of a wide range of companies.
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