Data Mining Definitions and Applications for the Management of Production Complexity

Production complexity has increased considerably in recent years due to increasing customer requirements for individual products. At the same time, continuous digitization has led to the recording of extensive, granular production data. Research claims that using production data in data mining metho...

Full description

Saved in:
Bibliographic Details
Published inProcedia CIRP Vol. 81; pp. 874 - 879
Main Authors Schuh, Günther, Reinhart, Gunther, Prote, Jan-Philipp, Sauermann, Frederick, Horsthofer, Julia, Oppolzer, Florian, Knoll, Dino
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2019
Subjects
artificial intelligence
data mining
machine learning
production complexity
machine learning
data mining
production complexity
artificial intelligence
Online AccessGet full text

Cover

More Information
Summary:Production complexity has increased considerably in recent years due to increasing customer requirements for individual products. At the same time, continuous digitization has led to the recording of extensive, granular production data. Research claims that using production data in data mining methods can lead to managing production complexity effectively. However, manufacturing companies widely do not use such data mining methods. In order to support manufacturing companies in utilizing data mining, this paper presents both a literature review on definitions of data mining, artificial intelligence and machine learning as well as a categorization of existing approaches of applying data mining to manage production complexity.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2019.03.217