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.
The term 'digital twin' is often misused. It represents the final stage of a three-step evolution: 1) a Digital Model (offline simulation), 2) a Digital Shadow (receives real-time data), and 3) a Digital Twin. The critical distinction of a true twin is its ability to feed recommendations back to influence the physical process, creating a closed loop.
Instead of aiming for a massive, all-encompassing digital twin, identify a critical business bottleneck first. Build a focused, end-to-end offline model to prove its value. Only after demonstrating a clear return on investment should you scale it into a real-time, fully integrated system. This 'moonshot before Mars' approach minimizes risk and builds momentum.
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.
Large companies are often burdened by legacy systems and data silos. A small company, starting fresh, can implement a unified digital infrastructure from day one. This 'right first time' approach is a significant competitive advantage, allowing them to avoid the technical debt and organizational friction that slows down established players, even with less historical data.
While traditional hybrid models still require solving differential equations during use, Physics-Informed Neural Networks (PINNs) learn the final outcome directly. They are trained to obey physical laws but don't need the equations for inference. This provides a significant speed advantage for computationally expensive systems like chromatography, making them more suitable for real-time applications.