The industry scale production of heavy steel plates is performed in plate mills using reversing mill stands and roll schedules with 30 or more passes. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. To obtain these forces process models based on the slab theory together with semi-empirical material models are most frequently used. The material parameters necessary to calibrate the models for a specific steel grade are conventionally generated on a lab scale via time-consuming and expensive compression tests. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by an inverse modeling technique. In this context, the parameters of the material model are adjusted according to the current offset between measured and calculated roll forces. The adjustment is enabled by a non-linear optimization that aims for a minimum deviation between measured and calculated roll forces for each pass. Several results gathered in an optimization run with industrial data of about 2650 produced plates are presented. The concurrence between measurement and prediction using the optimal parameters is in detail shown for three different schedules. In summary, inverse modeling seems to allow utilizing data of past rolling processes to determine material model parameters with high accuracy.