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  5. A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study
 
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A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study

Author(s)
Fahey, Will  
Jeffers, Paul  
Carroll, Paula  
Uri
http://hdl.handle.net/10197/11580
Date Issued
2020-04-24
Date Available
2020-09-22T14:13:27Z
Embargo end date
2022-01-24
Abstract
Biopharmaceutical manufacturers are required to collect extensive observational data sets in order to meet regulatory and process quality monitoring requirements. These datasets contain information that may improve the performance of the production process. Analytics provides a means of extracting this information while Six Sigma provides a means for the insights to be incorporated into production practices. We present a novel framework which combines Six Sigma and Business Analytics. This approach mines large volumes of inline and offline biopharmaceutical production data, allowing the entire production process to be analysed and modeled. The recommendations of the model are represented as manufacturing rules which give actionable insights to improve the performance of the process. The integrated approach delivers promising results from synthetic experiments as well as being applied in practice to a cell culture process.
Sponsorship
Irish Research Council
Type of Material
Journal Article
Publisher
Elsevier
Journal
Computers in Industry
Volume
116
Copyright (Published Version)
2019 Elsevier
Subjects

Biopharmaceutical

Manufacturing

Analytics

Six sigma

Time series

DOI
10.1016/j.compind.2019.103153
Language
English
Status of Item
Peer reviewed
ISSN
0166-3615
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

A Business Analytics Approach to augment Six Sigma Problem Solving V1.pdf

Size

884.47 KB

Format

Adobe PDF

Checksum (MD5)

3c6f968c686960e7c7cb2c7d92a22e23

Owning collection
Business Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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