A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study
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|Title:||A business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case study||Authors:||Fahey, Will; Jeffers, Paul; Carroll, Paula||Permanent link:||http://hdl.handle.net/10197/11580||Date:||24-Apr-2020||Online since:||2020-09-22T14:13:27Z||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.||Funding Details:||Irish Research Council||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Computers in Industry||Volume:||116||Copyright (published version):||2019 Elsevier||Keywords:||Biopharmaceutical; Manufacturing; Analytics; Six sigma; Time series||DOI:||10.1016/j.compind.2019.103153||Language:||en||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/|
|Appears in Collections:||Business Research Collection|
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