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Item type:Publication, Access status: without files , Comparison of two clustering approaches to find demand patterns in semiconductor supply chain planning(2018-12-04); ; ; ; 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.Publicationtype: Conference Paper227 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Finding demand patterns in supply chain planning [Nachfragemuster in der Lieferkette erkennen](2018-08-20); ; ; ; 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.Publicationtype: Journal Article105 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On agent-based modeling in semiconductor supply chain planning(IEEE, 2018-01-04); ; ; ; Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on the demand side as well as a complex manufacturing process with non-deterministic failure modes on the production side. Understanding the complex interdependencies and processes of a SC is essential to realize opportunities and mitigate risks. However, this understanding is not easy to achieve due to the complexity of the processes and the non-deterministic human behavior determining SC planning performance. Our paper argues for an agent-based approach to understand and improve SC planning processes using an industry example. We give an overview of current work and elaborate on the need for integrating human behavior into the models. Overall, we conclude that agent-based simulation is a valuable method to identify favorable and unfavorable conditions for successful planning.Publicationtype: Conference Paper196