Wuttke, A.; Hunker, J.; Scheidler, A. A.; Rabe, M.: Synthetic Demand Generation with Seasonality for Data Mining on a Data-Farmed Data Basis of a Two-Echelon Supply Chain. International Conference on Industry Sciences and Computer Sciences Innovation (ISCSI), Porto, 09.-11. März 2022. Procedia Computer Science 204 (2022) , S. 226-234

A widely used method in the context of supply chain analytics and management is data mining. It is used to discover patterns in a supply chains data basis. To obtain a data basis, besides preprocessing observational real-world data, simulation can be used. This process is referred to as data farming and involves using a simulation model as a data generator. A common input to a simulation model of a supply chain is demand of stock keeping units that is likely to project to the model's behavior. When testing novel approaches or in planning stage, demand of real-world supply chains is not always available or viable to adept. Then, synthetically created demand can be used. In this paper, a general approach of realistic demand generation with seasonality by a demand generator in the context of data farming is presented. The approach is further exemplified on a data farming and data mining framework of a two-echelon network.