Shreyas Becker from Sanofi discusses using AI to tackle supply chain challenges in life sciences. Topics include real-world evidence delivery, sharing sensorial data with big tech, and how data drives problem-solving. The podcast sheds light on the complexities of bio manufacturing, standardization challenges, key quality metrics, and AI's impact on efficiency in life sciences.
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Quick takeaways
Bio manufacturing poses challenges in predictability due to live organism variability, emphasizing the need for data-driven solutions.
Quality and yield are crucial in life sciences manufacturing, focusing on product quality, toxicity, and resource efficiency.
Deep dives
Challenges in Bio Manufacturing: Managing Variations in Production Output
Bio manufacturing presents challenges in predicting and controlling production output, unlike traditional manufacturing processes. For example, making drugs through bio manufacturing involves live organisms that introduce variability in output quantities, requiring a focus on predictability and reliability. Analogies like baking bread highlight the complexities of variables such as ingredient quality, environmental conditions, and process consistency affecting final output, underscoring the need for extensive monitoring and data-driven solutions.
Key Metrics in Life Sciences Manufacturing: Quality and Yield as Critical Measures
Quality and yield are vital metrics in life sciences manufacturing processes. Ensuring high product quality is paramount, with metrics like toxicity and active ingredient potency influencing overall quality assessments. Yield, calculated as output relative to input, is another essential measure, indicating production efficiency and profitability. While specific process metrics vary, emphasizing quality control and efficient resource utilization are common priorities in manufacturing operations.
Role of AI in Driving Efficiencies: Empowering Operational Decisions and Process Automation
Artificial Intelligence (AI) plays a significant role in enhancing operational processes and decision-making in manufacturing. AI empowers frontline workers by providing real-time insights and operational guidance for optimizing processes. Automation of administrative tasks through AI-driven systems improves productivity and reduces manual burdens. Advanced applications include AI integrating into machinery for real-time decision-making, improving operational efficiency and driving value creation in manufacturing environments.
Today’s guest is Shreyas Becker, Head of AI & Data Products, Manufacturing & Supply at Sanofi. Shreyas joins Emerj Senior Editor Matthew DeMello on today’s podcast to talk about alleviating pain points for supply chain leaders in life sciences spaces. From building systems that deliver “real world evidence” to the subject matter experts and managers who need it to the pros and cons of sharing sensorial data with big tech data storage platforms like Amazon Web Services – Shreyas helps the executive podcast audience understand how problems are both viewed and solved through the lens of data, from no matter where they arise. If you’ve enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
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