AI optimizes brine bath or drying cabinet

On paper, cheese and licorice are very different products. In the factory, however, it turns out that both processes revolve around precisely timed intermediate stages, process dependencies and tight boundary conditions. Planwisely precisely replicates this reality in a Virtual Factory: a digital replica of your factory in which every production step, capacity and dependency is modeled. This creates a powerful basis for simulating and thus optimizing planning in a way that operators and planners immediately recognize.

The critical point: duration and nuance

• Cheese – the brine bath as the defining phase
  In cheese production, brining is not a secondary task but a critical intermediate stage: salt absorption, rind formation and micro-conditions in the brine determine taste, shelf life and weight. Proper duration is essential; small deviations can lead to non-conforming batches or unnecessary weight loss.
• Licorice – drying time and air management
  With drop, the drying phase is similarly critical: drying and curing determine consistency, texture and storability. Air circulation, temperature and relative humidity in the drying cabinet are control points that directly affect drying time and failure. Balancing throughput and product quality is a central planning issue here.

Why AI and the Virtual Factory really help here

Both the dryer and the brine bath are great examples where AI can really make a difference in planning. That’s where Planwisely’s Virtual Factory comes in. In this virtual environment, a structured digital representation of your production logic is recreated. In this virtual environment, all scenarios can be automatically simulated. On this basis, our AI can generate the most optimal planning in no time. We give some advantages of this approach:

• Digital modeling of critical intermediate stages: Each step is modeled as a virtual workstation with exactly the boundary conditions you use. It is thus a unique configuration for your production process.
• Scenario Exploration: A real plant with multiple lines, batches and constraints quickly produces thousands of possible scheduling variants. AI can systematically calculate many scenarios (e.g., what if a dryer is available later, or a brine bath has a maintenance shutdown) and quantify the effects on delivery times, lead time and quality risks.
• Multi-goal optimization: Planning goals often conflict: think maximum output versus minimum deviations in quality or weight. The Virtual Factory lets you set goals explicitly (e.g., priority on customer deadlines, minimum weight loss, and maximum efficient use of drying capacity). The AI looks for schedules that deliver a balanced optimum.
• Robustness against variability: Food production is not always deterministic, think material variation or unexpected disruptions. By simulating variations, Planwisely can propose schedules that are less vulnerable to disruptions – meaning fewer ad hoc interventions on the shop floor.
• Cross-line and resource-aware scheduling: Because the entire plant has been recreated, the AI does not look at one line in isolation, but optimizes across all lines: who uses which dryer when, which batches can go together or have to go separately, and how does that affect your inventory position and order fulfillment.
• What-if and human control: Planners and process experts remain decision makers. The Virtual Factory offers quick insight into what-if analyses (e.g., “if we extend X hours in the dryer, do we take 7% more downtime for granted or do we gain delivery time?”). Trade-offs become visible. Planners can adjust constraints and immediately run new scenarios.

The virtual parallels between cheese and licorice

With our experience in the production process and Planwisely’s Virtual Factory, we show the similarities between AI-based production planning for cheese and licorice

Practical added value to the operation

The Virtual Factory therefore offers supply chain professionals in the food industry many value-added features, such as:

• Less firefighting: clearer, executable schedules that take critical phases into account.
• Reduced product rejection and quality fluctuations: because critical dwell times are explicitly monitored and scheduled.
• Higher delivery reliability: orders and lead times are realistically and robustly scheduled.
• Calculate adjustments faster: planners can change policies (e.g. additional drying time allowed) and see the impact immediately.

Conclusion

Whether it’s a brine bath or a liquorice dryer, the core problem is the same: timing, constraints and dependencies determine whether a batch conforms to the recipe and whether the schedule holds up. By combining a fully digital replica of the factory with AI-driven scenario simulation – exactly as in Planwisely’s Virtual Factory – you can not only make that complexity understandable, but also solve it practically. That’s where food processes really benefit.