Manage the complexity of end-to-end continuous processes by creating automated feedback loops. Integrating real-time analytics, like an online HPLC, with mechanistic models allows for the dynamic, on-the-fly adjustment of downstream unit operations based on live upstream performance, optimizing the entire system.

Modeling in process development can drastically reduce experiments, which is valuable for speed. However, even a small, single-digit percentage yield improvement in manufacturing provides a far greater long-term financial return. The gain is realized on every single batch produced throughout the product's entire commercial lifecycle, making it the most impactful area for modeling.

Analytical leaders often try to create one all-encompassing model for every scenario, resulting in a complex monstrosity. A better approach is a simple model for most cases, handling exceptions as one-offs. This avoids wasting months on a framework to solve a six-minute problem.

For startups adopting AI, the most effective starting point is not a massive overhaul. Instead, focus on a single, high-value process unit like a bioreactor. Use its clean, organized data to apply simple predictive models, demonstrate measurable ROI, and build organizational confidence before expanding.

Before building a complex digital twin, Takeda analyzed historical manufacturing data. A simple end-to-end model identified that changing set points for six parameters—within existing validated ranges—could boost yield by 35%, demonstrating massive value in data that companies already possess.

When modeling a complex issue like malaria bed nets, don't start with every variable. Begin with a simple model of the 5-6 core drivers. This makes the model easier to understand, hold in your head, and debug. Add complexity later, once the basic dynamics are established and validated.

Many assume vast amounts of data are necessary for a digital twin. In reality, process validation data combined with a handful of manufacturing trends is often sufficient. The focus should be on data quality and its relevance to a specific business decision, not sheer quantity. This approach makes powerful modeling accessible much earlier.

Instead of immediately scaling up the manufacturing process between clinical Phase 1 and 2, it is strategically better to produce more batches using the established Phase 1 process. This approach builds critical knowledge about process parameters and CQAs through repetition and increased clinical exposure.

It's tempting to think you can intuit the few factors a decision hinges on. This is often wrong. Complex systems have non-obvious leverage points. The process of building an explicit model reveals which variables have the most impact—a discovery you can't reliably make with intuition alone.