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Model is the cause and effect behind the map. It shows how each decision moves each outcome, against its objective and its constraints. These relationships are nonlinear and dynamic. They change over time, and plant data is never complete, so data alone is not enough.
That is why first principles are built into every model, and nothing sits in a black box. Every relationship is visible, causal rather than correlated, and deterministic. A Deep Learning Predictor learns each unit's dynamics from years of its own data. And with DLPC Builder, your process engineers build models of their own units, without Python or a machine-learning background.
Your engineers can make their own models and create their own competitive advantage.