Typical challenges:
- limited tank and silo capacities
- complex product and material constraints
- high cleaning and changeover effort
- inefficient use of intermediate storage
- bottlenecks between production and packaging
- high manual coordination effort
This becomes particularly critical in the case of:
- continuous production
- Batch and campaign manufacturing
- multiple intermediate storage units
- complex material flows
- high hygiene and contamination requirements
Your benefits through Tank / Silo Optimisation with MCP AI-as-a-Service:
Better utilisation of existing storage capacities
Reduced downtime and waiting times
More stable production campaigns
reduced cleaning effort
higher production stability
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tank and silo allocation
Optimised utilisation of limited storage and buffer volumes.
intermediate storage optimisation
Synchronization of production, storage, and downstream processing.
cleaning and changeover constraints
Minimisation of product-related cleaning and changeover processes.
material flow optimisation
Stabilisation of complex material flows between production stages.
Relevant AI-as-a-Service solutions
Process Industry Scheduling
Optimised production planning taking into account tank, silo, and cleaning constraints.
Process Industry Scheduling
Optimised production planning taking into account tank, silo, and cleaning constraints.
Core Resource Assignment
Automatic allocation of limited resources and storage capacities.
Who is Tank / Silo Optimisation particularly relevant for?
- Food Industry
- Beverage Industry
- Chemical industry
- Pharmaceutical industry
- Compound feed industry
- Cosmetics industry
Easy Integration
Optimise production planning: Tank / Silo Optimisation with AI-as-a-Service
- APS systems
- MES systems
- ERP systems
- custom planning solutions
The existing system landscape remains in place and is specifically enhanced with additional optimisation capabilities.
Where do tank and silo constraints currently reach their limits in your organisation today?
In a joint discussion, we analyse the underlying planning logic and demonstrate how AI-as-a-Service can extend it in a targeted way.