Hunker, J.: Farming for Mining: Combining Data Farming and Data Mining to Gain Knowledge in Supply Chains. In: Kim, S.; Feng, B.; Smith, K.; Masoud, S.; Zheng, Z.; Szabo, C.; Loper, M.: (eds,): Proceedings of the 2021 Winter Simulation Conference. Piscataway: IEEE 2021.

Knowledge discovery in databases (KDD) is a frequently used method in the context of supply chains (SC). The core phase of KDD is known under the term data mining. To gain knowledge, e.g., to support decisions in supply chain management (SCM), input data for KDD are necessary. Typically, such data consist of observational data, which have to be preprocessed before the data mining phase. Besides relying on observational data, simulation can be used to generate data as an input for the process of knowledge discovery. The process of using a simulation model as a data generator is called data farming. To link both data farming and KDD, a Farming-for-Mining-Framework has been developed, where the data farming process generates data as an input for the KDD process to support decisions in SCM.