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Bayer Healthcare Planning Engine
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Between Jun 2004 and December 2006, a full-scale planning and scheduling model was developed for Bayer Healthcare. This model was based on work by Prof. Leachman in the semiconductor field, whose model is now used in dozens of semiconductor plants around the world.
The Planning Engine uses enterprise-class database software and is developed with a completely customized front-end, written in the advanced .NET framework. The software pulls production data from a disparate range of sources and uses leading-edge optimization technologies. This Engine manages the entire production process, from fermentation to packaging, including allowance for all regulatory and production constraints, equipment information and cycle times / yields. All work on this project, from conception to execution and final implementation, was undertaken by Bio G.
The Planning Engine allows Bayer to:
Quickly assess “what-if” scenarios to model the overall supply chain’s reaction to problems or opportunities. Allows planners to perform much more detailed and sophisticated analysis in a shorter time period:
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“Can we meet current demands if all material in XXX facility was contaminated? How would we change batch allocations and testing schedules?”
“What is the effect on QC work centers if the US approved product YYY two months ahead of schedule?”
“How do we optimally produce new product A, B, and D in existing facility XXX, so that as soon as regulatory restrictions are removed we have sufficient finished product to meet demand?”
“What is the impact of releasing blocked product ZZZ in meeting US demand over the next quarter?”
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Holistic view of the supply chain, ability to quantify the exact effect of changes in $ terms or in terms of increased supply reliability
Reduce the need to carry very large inventories, start more batches than required, and other ‘unseen’ buffers
Planning system using similar methodology improved semiconductor on-time delivery from 75% to 95%
View seminar abstract on this topic
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Simulation model for Genentech Fermentation
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In Fall 2006, a model was developed for Discrete Event Simulation of Mammalian Cell Line Fermentation Process at Genentech’s SSF site. This project’s goal was to improve titer production rate for the fermentation process of a particular cell line at that facility. BioG developed a computer simulation model of the fermentation process, based in Microsoft Excel with a compiled simulation engine running the simulation in the background. This model allowed the user to change process parameters and then retrieve a numerical and graphical output of results.
Bio G developed a computer simulation model of the fermentation process, using Microsoft Excel as the graphical user interface with a compiled simulation engine written in Sigma running the simulation in the background.
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Integrated Process Economics / Network Model for Genentech
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Starting in 2007, UC Berkeley is partnering with Genentech to build an integrated process economics model of their supply chain network. This model will integrate operations, tactical and strategic data from multiple sites and will provide a series of tools to analyze processes, plant performance, the impact of new technologies, and risk across the entire supply chain network.
The series of tools developed will aim to directly address issues of inherent biological uncertainty and risk throughout the network.
www.bio-g.com |
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