Lecture
From Data Foundation to Machine Learning and LLMs - Eliminating Silos Between R&D and QC
- at -
- B2.137
- Type: Lecture
Lecture description
Siloed R&D, manufacturing, and QC systems lead to slower investigations, and gaps in quality visibility. This session outlines a practical approach to consolidating operations within a single, structured data environment. We will examine how combining a best in class R&D software system with QC tooling enables both teams to operate more effectively. Capabilities include formulation & parameter optimization, tied to approval processes for final products. Final products can then be analyzed with a full suite of QC tools, including SPC charting, lab testing, and environmental data capture. This can be tied to both individual batch approval and certificate of analysis generation, as well as rework done at the R&D level, allowing a more efficient, closed loop process. Users from both teams can visualize deviations and violations over time, allowing them a better understanding of what will go wrong before new products and processes are put in place. Attendees will learn how unified data, consistent metadata, and standardized digital workflows strengthen AI readiness and audit readiness, reduce variability, support root-cause analysis, and increase the reliability and efficiency of quality operations.
