Impact of DataHowLab on Manufacturing Operations
Explaining and preventing process deviations remains a core challenge in biomanufacturing. DataHowLab enables MSAT and Manufacturing teams to gain deep insight into process behaviour, supporting proactive decision-making and ensuring a more robust operation.
The Challenge of Root-cause in Manufacturing
Identifying root cause in biomanufacturing is inherently difficult due to the complexity of biological systems, process interdependencies, and fragmented data sources.
Even under controlled conditions, minor variability can drive unexpected outcomes. With limited variability in manufacturing to learn from, conventional analysis methods often lack the depth or sensitivity to uncover underlying drivers.
AI-enabled hybrid modeling offers new opportunities for operators to generate high-value process intelligence and consistently improve outcomes.
Case 1: Using DataHowLab for a root cause analysis on a microbial process, to determine titer variability
Case 2: Using DataHowLab for a root cause analysis on a gene therapy process, to identify the underlying drivers of out-of-specification critical quality attributes (CQAs)
Case 3: Using DataHowLab to mitigate process failure when an error occurs
Key Results
DataHowLab provides operators with powerful process intelligence to ensure early detection of deviations and tighter control during manufacturing runs. For the full results with supporting plots, download the full case study below:
Case 1: DataHowLab was able to determine that factors outside of the main fermenter were driving titer variability
Case 2: DataHowLab was used to identify a complex combination of factors which caused 2 CQAs to regularly fall out of specification.
Case 3: DataHowLab was used to generate a mitigation strategy for a feed failure in the middle of a process, successfully returning titer values to target.
Should you wish to further explore the case with the DataHow team, please contact us directly.
