Gaining knowledge from a given data basis is a complex challenge. One of the frequently used methods in the context of Supply Chains is Knowledge Discovery in Databases. For a purposeful and successful knowledge discovery, valid and preprocessed input data are necessary. Besides preprocessing collected observational data, simulation can be used to generate a data basis as an input for the knowledge discovery process. The process of using a simulation model as a data generator is called Data Farming. This paper investigates the link between Data Farming and Data Mining. We developed a Farming-for-Mining-Framework, where we highlight requirements of knowledge discovery techniques and derive how the simulation model for data generation can be configured accordingly, e.g., to meet the required data accuracy. We suggest that this is a promising approach and is worth to gain further research attention.