New video: Boehringer Ingelheim introduces 'Lifecycle DOE'COMPANY CONFIDENTIAL. DO NOT DISCLOSE.
(Note: This video is currently under review by the customer.)
TAKEAWAYS:
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The whole analytics pipeline – from reading in the data to modeling to analysis and decision making to implementation – can all be contained within one single software. “This is, of course, the reason why we use JMP,” says Principal Statistician Marco Kunzelmann.
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Subject matter experts need to be able to understand and trust the analytics workflows developed for them by the statisticians who support their work. Having consistent, transparent and standardized methods in JMP helps cement a relationship of trust between statistical experts and subject matter experts.
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After identifying some inefficiencies in the DOE process, Kunzelmann worked to develop what he now calls Lifecycle DOE. “What people did in the past is they performed a bunch of experiments to characterize their process. We have done all these experiments already during development, however, in such a fragmented way that it was not possible to combine them anymore,” he explains. “What we do now is we create at first, a single model, a single DOE, and then we augment this DOE several times within each of these individual work packages. And by doing that, we can create all the data we create within process development as a whole. And this of course is increasing efficiency and in the end we can reduce the number of experiments for the process characterization study at the very end dramatically.”
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Kunzelmann estimates that with this method, at least 20% of experiments can be eliminated. At scale, he says, this saves “a bunch of resources” – constitutes a step change in the cost of experimentation.
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Even more important, he says, is that “you have stored all the development information… in one single model.”
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Interactive analytics workflows in JMP have contributed to a culture where bench scientists, statisticians, and Boehringer Ingelheim upper-level management have a “close collaboration.”
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