Discovering actionable knowledge for industry 4.0: from data mining to predictive and prescriptive analytics
Date
2023
Authors
Schuetz, C.G.
Schrefl, M.
Selway, M.
Thalmann, S.
Editors
Vogel-Heuser, B.
Wimmer, M.
Wimmer, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Book chapter
Citation
Source details - Title: Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective, 2023 / Vogel-Heuser, B., Wimmer, M. (ed./s), Ch.14, pp.337-362
Statement of Responsibility
Conference Name
Abstract
Making sense of the vast amounts of data generated by modern production operations—and thus realizing the full potential of digitization—requires adequate means of data analysis. In this regard, data mining represents the employment of statistical methods to look for patterns in data. Predictive analytics then puts the thus gathered knowledge to good use by making predictions about future events, e.g., equipment failure in process industries and manufacturing or animal illness in farming operations. Finally, prescriptive analytics derives from the predicted events suggestions for action, e.g., optimized production plans or ideal animal feed composition. In this chapter, we provide an over view of common techniques for data mining as well as predictive and prescriptive analytics, with a specific focus on applications in production. In particular, we focus on association and correlation, classification, cluster analysis and outlier detection. We illustrate selected methods of data analysis using examples inspired from real-world settings in process industries, manufacturing, and precision farming.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2022
Access Condition Notes: The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature