Bodendorf F, Merbele S, Franke J (2019)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2019
Publisher: Association for Information Systems (AIS)
Conference Proceedings Title: Proceedings of the 2019 Pre-ICIS SIGDSA Symposium
URI: https://aisel.aisnet.org/sigdsa2019/25
Due to the high pace of development in the automotive industry there is a need for innovating cost engineering. A methodology for intelligent cost estimation in the early stages of the product life cycle is introduced. In a first step it is shown how significant economic and technical parameters for cost prediction can be prepared and filtered from historical calculation data. Subsequently, it is shown how cost prediction models can be developed using machine learning algorithms. Learning data and practical use cases come from a large automotive manufacturer in Germany. The models predict costs of car parts and assemblies of increasing complexity. Seven different machine learning models are trained and optimized. Based on the test data of the use cases these models are assessed and compared. Finally, the prediction results obtained are evaluated from different perspectives, demonstrating the practical applicability of the most suitable methods explored.
APA:
Bodendorf, F., Merbele, S., & Franke, J. (2019). Predictive Cost Analytics of Vehicle Assemblies Based on Machine Learning in the Automotive Industry. In AIS eLibrary (Eds.), Proceedings of the 2019 Pre-ICIS SIGDSA Symposium. München, DE: Association for Information Systems (AIS).
MLA:
Bodendorf, Frank, Stefan Merbele, and Jörg Franke. "Predictive Cost Analytics of Vehicle Assemblies Based on Machine Learning in the Automotive Industry." Proceedings of the 2019 Pre-ICIS SIGDSA Symposium, München Ed. AIS eLibrary, Association for Information Systems (AIS), 2019.
BibTeX: Download