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- PublicationImproving Biopharmaceutical Manufacturing Yield Using Neural Network ClassificationTraditionally, the Six Sigma framework has underpinned quality improvement and assurance in biopharmaceutical manufacturing process management. This paper proposes a neural network (NN) approach to vaccine yield classification and compares it to an existing multiple linear regression approach. As part of the Six Sigma process, this paper shows how a data mining framework can be used to extract further value and insight from the data gathered during the manufacturing process, and how insights into yield classification can be used in the quality improvement process.
- PublicationA business analytics approach to augment six sigma problem solving: A biopharmaceutical manufacturing case studyBiopharmaceutical 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.
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