Time-related Differentiation of Complexity Costs Using Process Data Mining

In the recent years, manufacturing companies extended their product portfolio with numerous product variants, accompanied by an increase in process variants and indirect costs. Many companies are not aware of the negative financial effects of a large product portfolio. Existing methods, such as activity-based costing or process costing, focus on a cause-related allocation of costs to products. Additionally, approaches use the separation of complexity costs into initial and running costs in order to determine a more accurate differentiation of cost effects. However, these approaches are based on primarily manually gathered data and are thereby time and cost intensive. In the course of digitalization and the use of business information systems, new possibilities arise to obtain cost relevant data efficiently. Nevertheless, current research does not provide a holistic data-based approach separating initial and running costs. This paper presents a methodology focusing on this differentiation using data from information systems to increase the transparency in cost accounting. The result is a methodology validated within an automated tool using process data mining for a targeted analysis of complexity costs for product portfolio evaluation.