Statistics and mathematical optimization are not generative AI, but they often deliver the fastest operational impact. They help teams detect equipment degradation earlier, spot process deviations, and plan production and logistics more effectively.

Data from thousands of sensors flows through OPC-UA into the historian. Statistical models continuously analyze trends and detect deviations from normal behavior. For optimization, the problem is formalized mathematically - constraints, resources, and objective - and the solver searches feasible combinations to find the best plan.

These methods work best in familiar conditions: if the data pattern changes, the model can fail. Optimization also depends on precise problem formulation - a wrong constraint produces a wrong optimum.