Decision Support Systems (DSS) can help factory workers in the decision-making step of multiple tasks. In digital factories, these systems make use of data towards a human-centered manufacturing. Rolling of large and thick plates into cans is a common practice in the metal forming industry to fabricate pipes or tanks. The process is adjusted by trial and error with a high level of operator intervention. Furthermore, only a small number of cans are identical. The objective of this work is to prescribe, by means of a DSS, the process parameters to be applied by the operator in the machine to optimize the can fabrication. The development of the DSS involved several steps, including firstly signal preprocessing and classification and then data extraction, aggregation, and regression in a multi-stage prediction framework. A significant use of domain knowledge for a data-centric solution contributes to the quality of the recommendations and the ability to organize and transfer know-how among operators.