Dominik Hammann obtained his Bachelor's and Master's degrees in Industrial Engineering and Management (Wirtschaftsingenieurwesen) at the Karlsruhe Institute of Technology (KIT). After graduation, he joined the Chair of Management Accounting as a research assistant and PhD student. He successfully obtained his PhD in 2022 for his thesis “The Management of Direct Material Cost During New Product Development: A Case Study on the Application of Big Data, Machine Learning, and Target Costing”. Dominik Hammann is currently working at the controlling department of AUDI AG.


My research investigates the application of big data, machine learning, and the target costing approach for managing costs during new product development in the context of high product complexity and uncertainty. A longitudinal case study at AUDI AG was conducted to examine this topic.
Specifically, a case study on the applicability of machine learning and big data technology for product cost estimation, focusing on the material costs of passenger cars was conducted. Further, an experimental study to investigate the trade-off between accuracy (predictive performance) and explainability (transparency and interpretability) of machine learning models in the context of product cost estimation was undertaken. Finally, a proprietary archival study was carried out to investigate the target costing approach in a complex product development context, which is characterized by product design interdependence and uncertainty about target cost difficulty.


D. Hammann (2024). Big data and machine learning in cost estimation: An automotive case study. International Journal of Production Economics, 269, 109137.