Nachfragemuster in der Lieferkette erkennen

Zwei Clustering-Algorithmen für die Optimierung der Halbleiterproduktion

  • Thomas Ponsignon
  • Pramod Govindaraju Hamburg University of Technology
  • Sebastian Achter Institute of Management Accounting and Simulation (MACCS)
  • Hans Ehm Infineon Technologies AG
  • Matthias Meyer Institute of Management Accounting and Simulation (MACCS) Universität Hamburg


Advancements in semiconductor industry have resulted in the need for extracting vital information from vast amount of data. In the operational process of supply chain, understanding customer demand data provides important insights for demand planning. Clustering analysis offers potential to identify latent information from multitudinous customer demand data and supports enhanced decision- making. In this paper, two clustering algorithms to identify customer demand patterns are presented, namely K-means and DBSCAN. The implementation of both algorithms on the prepared data sets is discussed and their performance is compared. The paper outlines the importance of deciphering valuable insights from customer demand data in the betterment of a distributed cognitive process of demand planning.


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PONSIGNON, Thomas et al. Nachfragemuster in der Lieferkette erkennen. atp magazin, [S.l.], v. 60, n. 08, p. 54-61, aug. 2018. ISSN 2364-3137. Verfügbar unter: <>. Date accessed: 20 apr. 2019. doi:
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