How Can We Build Trust In Model-Driven Manufacturing?

By Martin Ball and Ashley Howard, Cytiva

Biopharma manufacturers hold more process, equipment, and operational data than ever, yet improvement still tends to start after something has gone wrong. Prediction is within reach; acting on it with confidence is the harder part. Model-driven manufacturing needs more than good data. It needs process understanding, models that are maintained as equipment and modalities change, and governance that smaller organizations may struggle to sustain. Trust in AI-supported recommendations is a bigger obstacle than the technology. Aviation shows that trust in automation is earned incrementally, and the same will apply to scientists and engineers as their roles shift. When most companies collect similar data from similar systems, the edge may lie elsewhere.

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