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Activity Number:
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615
- Statistical Process Control
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Type:
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Contributed
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Date/Time:
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Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Quality and Productivity Section
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Abstract #304742
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Title:
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Bayesian Based Acceptance Criteria for SPC Applications
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Author(s):
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Hesham Fahmy* and Yanbing Zheng and Yuanyuan Duan
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Companies:
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AbbVie and AbbVie and AbbVie
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Keywords:
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SPC;
Bayesian;
Control Limits
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Abstract:
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Statistical process control (SPC) is important in pharmaceutical manufacturing not only for process stability and efficiency assessment but also for compliance with all appropriate pharmaceutical practices such as good manufacturing practice and good laboratory practice. Setting acceptance criteria is a key part in SPC and its main difficulty depends on the availability and quality of existing data. For example, control limits are usually set when a product is in the approval stage with limited data from full scale production runs. With such limited data, conventional statistical approaches will often yield wide acceptance ranges since there is uncertainty about the distribution of the data from the production process. In this presentation, we present a Bayesian approach to establish acceptance criteria for SPC applications. Historical data can be incorporated as prior information under the Bayesian framework. Strengths and shortcomings for Frequentist and Bayesian statistical approaches are discussed.
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Authors who are presenting talks have a * after their name.
