Data model to enable multidimensional process mining for data farming based value stream planning in production networks

Abstract International industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.