Data-driven process optimization in the beverage industry

Wöstmann, R; Reckelkamm, T; Deuse, J; Kimberger, J; Temme, F; Schlunder, P; Klinkenberg, R

Data-driven process optimization in the beverage industry

Wöstmann, R Reckelkamm, T Deuse, J

Kimberger, J Temme, F Schlunder, P Klinkenberg, R

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Publication Type:: Journal Article
Citation:: Fabriksoftware, 2019, 24, (3), pp. 21-24
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Wöstmann, R
dc.contributor.author	Reckelkamm, T
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.contributor.author	Kimberger, J
dc.contributor.author	Temme, F
dc.contributor.author	Schlunder, P
dc.contributor.author	Klinkenberg, R
dc.date.accessioned	2022-10-06T02:58:39Z
dc.date.available	2022-10-06T02:58:39Z
dc.date.issued	2019-01-01
dc.identifier.citation	Fabriksoftware, 2019, 24, (3), pp. 21-24
dc.identifier.issn	2569-7692
dc.identifier.uri	http://hdl.handle.net/10453/162345
dc.description.abstract	Increased pressure on prices and competition is presenting the beverage industry with major challenges in terms of rationalization. Existing Lean and Six Sigma approaches are reaching their limits in biochemical processes with complex multivariate influences. The paper presents an approach for data-driven process optimization in the beverage industry based on machine learning.
dc.language	en
dc.relation.ispartof	Fabriksoftware
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Data-driven process optimization in the beverage industry
dc.type	Journal Article
utslib.citation.volume	24
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-06T02:58:38Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	3

Abstract:

Increased pressure on prices and competition is presenting the beverage industry with major challenges in terms of rationalization. Existing Lean and Six Sigma approaches are reaching their limits in biochemical processes with complex multivariate influences. The paper presents an approach for data-driven process optimization in the beverage industry based on machine learning.

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http://hdl.handle.net/10453/162345