Hunker, J.; Scheidler, A.-A.; Rabe, M.: A new data farming procedure model for a farming for mining method in logistics networks. In: Feng, B.; Pedrielli, G.; Peng Y.; Shashaani, S.; Song, E.; Corlu, C.G.; Lee, L.H.; Chew, E.P.; Roeder, T.; Lendermann, P. (eds.): Proceedings of the 2022 Winter Simulation Conference. IEEE: Piscataway 2022, pp. 1461-1472.

A key factor in maintaining a logistics network in a competitive state is gaining and visualizing knowledge. The process of gaining knowledge from a given data basis is called knowledge discovery in databases. Besides gathering observational data, simulation can be used to generate a suitable data basis for the knowledge discovery known as data farming, which is typically implemented as a study. Conducting such a study requires a suitable procedure model, describing and structuring the tasks of the process. However, existing procedure models focus on defense applications, while considerably less work was put into transferring the approaches to logistics networks. Therefore, the authors developed a procedure model for conducting a data farming study in logistics networks. In this work, we systematically introduce the necessary background and discuss existing approaches in the literature. Furthermore, we present a software framework that is used to support the process in a practical application context.