Three Practical Examples of Pharma 4.0 Technologies in Manufacturing Operations

Three Practical Examples of Pharma 4.0 Technologies in Manufacturing Operations

07/15/2020, 1:15 PM - 2:00 PM

Stage 4, Booth 2276, Exhibit Hall
Language:
English
Too often an incomplete understanding of operational realities is preventing pharmaceutical manufacturers from understanding the real reasons for product waste, repeat non-conformances, and other issues holding them back in delivering predictable product supply. This presentation will present three practical examples of how Pharma 4.0 technologies improved manufacturing operations from real Pharma 4.0 case studies with our client projects while improving compliance throughout the entire process. The session will include direct experiences from several client projects where artificial intelligence and machine learning techniques have been deployed to create compelling new actionable insights. This presentation will also offer a perspective on digital maturity and how to prepare organizations to be ready to accept changes related to the adoption of these fast-emerging new technologies.

Contributors

  • Pep Gubau

    Speaker

    Co-founder and CEO

    Bigfinite, Inc.

    Pep Gubau is an entrepreneur and has been working in Information Technology (IT) for the last 25 years with companies pioneering Linux adoption in...

Objective

  1. Objective 1. Advanced analytics can help you both with root cause analysis and predictive process performance. Analytics play an important role in creating value from typically siloed data and modernizing manufacturing operations. It finds opportunities for improvements in manufacturing operations that were previously invisible and collects and consumes manufacturing production data so it can project and quickly correct deviations before they occur. 2. The application of artificial intelligence and machine learning can transform data from manufacturing operations into knowledge that can support key operational issues. Use artificial intelligence to identify patterns and relationships between huge amounts of data from manufacturing operations - often yielding surprising new insights from unexpected relationships. Machine learning models can then applied to predict the impact of making a change to production. This allows companies to improve their manufacturing operations by gaining insights from model results setting the stage for more cost-efficient operations. 3. Explain what it takes to ensure end-to-end data integrity to achieve GxP compliance and increase inspection readiness. Data integrity and compliance are some of the most (if not the most) important requirements when adopting new technologies in the biopharmaceutical industry. Algorithms must be designed with data integrity guidelines and best practices must be applied to the system design like data capture, data consumption, and so forth.

Type of Session

  1. Type of Session
    Session

Categories

  1. Track
    Data/Information Management

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