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Predicting Material Requirements in the Automotive Industry Using Data Mining

  • Tobias WidmerEmail author
  • Achim Klein
  • Philipp Wachter
  • Sebastian Meyl
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)

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.

Keywords

Predictive manufacturing Material requirements planning Data mining Artificial intelligence Automotive industry 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tobias Widmer
    • 1
    Email author
  • Achim Klein
    • 1
  • Philipp Wachter
    • 1
  • Sebastian Meyl
    • 1
  1. 1.University of HohenheimStuttgartGermany

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