In real-time industrial process applications, there is often a need to predict the effects of adjustments before actually effectuated. Multivariate prediction models based on historical data works fine for predictions as long as the new X data have the correlation structure preserved. This paper shows that such can anyway be used for �What if �� decision support in presence of correlated X variables, whenever it is possible to create a help model that predicts the influence of the manipulated X variables on the rest of the X variables.