Your process data reveals more than your models can explain
We identify additional opportunities for optimization based on real-time operational data—and validate them directly during ongoing operations.
Untapped potential often remains hidden—because it isn't visible in existing models.
In many plants, the relevant factors are known—but their actual interactions are not. This is precisely where uncertainties arise during operation:
Which factors actually influence energy consumption—and how do they interact?
Where are the untapped opportunities for optimization in the process?
What is possible without compromising quality or operations?
Models and empirical data provide guidance—but they are not sufficient to reliably predict the actual behavior of the system. Especially in complex process environments, critical interactions remain hidden.
There is untapped potential between the model and reality
Simulation models and traditional calculations show ideal conditions. However, actual operating conditions differ from these. It is precisely this discrepancy that reveals measurable opportunities for optimization:
real-world interactions between parameters
previously unrecognized connections within the process
Deviations from the "optimal" model state
Without access to actual system performance, these opportunities remain untapped—or are not implemented due to risk considerations.
Step 1: Identify relevant data and make it usable
Not all available data is relevant to optimization. We identify which process data actually influences your specific issue, assess its quality, and prepare it so that it can be used directly for analysis.
Result: A robust database instead of an unstructured collection of data
Step 2: Identify connections and identify opportunities
Based on real-world operational data, models are developed that replicate the actual behavior of the system. This process also reveals interactions between parameters that often remain hidden in traditional approaches. This leads to specific recommendations for optimized parameter sets.
Result: Clear correlations and concrete optimization strategies
Step 3: Test in operation and refine as needed
The derived parameter sets are not evaluated theoretically, but are tested during actual operation. New insights are directly incorporated into the models, iteratively improving the recommendations—in alignment with your process knowledge and operational requirements.
Result: Validated optimizations that work in real-world operations
The Data Science Framework does not follow a rigid analytical process; instead, it is consistently geared toward operational implementation. The goal is to derive concrete, verifiable optimizations from existing data—and to validate them under real-world conditions.
