Simulation with Artificial Intelligence
Sustainable and efficient warehouse and production processes through precise, digital simulations.
- Efficient and safe processes
- Early risk detection
- Time and cost savings
- Reduced downtimes
- Long-term future security of the WMS and MES
OneBase®Intelligence for Manufacturing
Automatic fine planning and machine learning
The method portfolio for order scheduling has been specially developed for the optimization of multi-stage production processes.
- Up to 25% performance optimization
- Reduced planning times
- Combined simulation and optimization
- Transparent, data-driven decisions
OneBase®Intelligence in Intralogistics
Higher storage capacity and efficient crane control
OneBase®Intelligence offers numerous tools for simulation and testing:
- Integrated AI methods
- AnyLogic integration with OneBase®MFT
- OBPortal for live data visualization
- OBFormula for the configuration of simulation cases
Key Benefits
- Evaluation of changes in plant, crane, and transport system configurations
- Analysis of warehouse layout, topology, and material flow (input and output)
- Consideration of production plans and relevant influencing factors
- Detection of bottlenecks and inefficient processes
- Development and validation of optimization concepts
- Optimized planning and execution of transport and production orders
- Assessment of planned measures regarding their effectiveness
- Support in the implementation and commissioning of improvements
Showcases of Artificial Intelligence
These practical showcases demonstrate how simulation and AI work together to significantly increase efficiency, predictability, and process reliability.
Crane Scheduling
This use case has a tactical component: How can the next 10–20 crane movements be planned in such a way that the material flow is as efficient as possible?
The solution is achieved through numerical simulations and heuristic optimizations – in principle, through “intelligent trial and error.”
In numerous simulation runs, different movement sequences are evaluated and optimized with regard to throughput and waiting time.
Result:
Up to 25% increase in efficiency in processing
Significantly reduced downtimes and waiting times
Adaptive decision logic for changing process situations
Location Assignment Problem (Space Allocation)
This use case deals with the strategic level of warehouse logistics: Where should a coil or piece of material be stored so that later retrievals are possible without complex re-stacking?
The system works with rule-based parameters that are tested and optimized using offline simulation.
This involves continuous “Learning through Data” – the system learns from simulation results which strategies are the best in the long term.
Result:
Reduction of re-stacking and blockages
Increase in storage density and access efficiency
Sustainable improvement of overall logistics
Automatic fine planning and machine learning for production data
Our method portfolio for order scheduling is specifically designed to continuously optimize multi-stage production processes.
This results in a performance optimization of up to 25%
Current situation
- Planning functions strongly dependent on project-specific technologies
- Standard control station functions often insufficient for individual requirements
- High dependence on experienced dispatchers
Challenge
- Complex dependencies between machines, plants, and processes
- Initial decisions significantly influence later work steps
- Search for a flexible, learning planning system
Goal and benefit
- Combine simulation and optimization
- Generic solution, adaptable for various production processes
- Reduced planning times and higher system efficiency