Abstract
Advanced capabilities in artificial intelligence pave the way for improving the prediction of material requirements in automotive industry applications. Due to uncertainty of demand, it is essential to understand how historical data on customer orders can effectively be used to predict the quantities of parts with long lead times. For determining the accuracy of these predications, we propose a novel data mining technique. Our experimental evaluation uses a unique, real-world data set. Throughout the experiments, the proposed technique achieves high accuracy of up to 98%. Our research contributes to the emerging field of data-driven decision support in the automotive industry.
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Acknowledgements
This work has been partially supported by the Federal Ministry of Economic Affairs and Energy under grant ZF4541001ED8. We would like to thank Hansjörg Tutsch for his valuable comments on earlier versions of this paper. We also thank Lyubomir Kirilov for helping to develop and improve parts of this paper.
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Widmer, T., Klein, A., Wachter, P., Meyl, S. (2019). Predicting Material Requirements in the Automotive Industry Using Data Mining. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-20482-2_13
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DOI: https://doi.org/10.1007/978-3-030-20482-2_13
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