Manufacturing capacity and raw material constraints to meet production demands require optimization of legacy processes and equipment.
Growth of large molecule, bioprocessing, cell & gene therapies is increasing complexity of processes, equipment and systems. Advanced Analytics and machine learning are required to address these needs.
Limited visibility into real-time product quality is no longer acceptable. Predictive quality monitoring with machine learning, automated PAT for in-line monitoring (ILM), and deviation monitoring to enable intelligent QMS are required. This also sets the foundation for continued process verification (CPV) and Quality By Design (QbD).
Lack of structure and visibility in legacy siloed data systems creates the need to connect and contextualize product, process, and equipment data for easy access and to enable product and operational analytics.
Unexpected downtime and asset health degradation requires implementing a robust predictive maintenance approach to monitor asset health, risk, and anomalous behavior caused by operational failures.
Create and capture process development knowledge to increase PD performance, accelerate tech. transfer and scale up. Manage design space in real-time with process and quality data.
QbD-based Process Development
Capture PD knowledge to model and establish the CMC control strategy to create a foundation for CPV.
Batch Analytics / MSPC
Continuously improve batch performance and outcomes using a multi-variate golden batch profile.
Predictive Product Performance
Visibility into real-time batch performance, variables influencing poor batch performance, and data trends of key variables causing batch and equipment performance degradation.
Yield & Step-Loss Optimization
Objective and constraint-based optimization within the operation space to increase average yield, reduce loss between units, and improve consistency.
Batch and Process Optimization
Objective and constraint-based optimization within the operation space to optimize any KPI or CQAs.
Automated PAT analysis to implement in-line monitoring, minimize transition times between unit operations, support real-time release and enable closed-loop control.
Abnormal Event Prediction
Turn alarm and failure history into proactive insights and predictions to make downstream equipment more responsive to process deviations.
Identify deviation trends for intelligent QMS by discovering underlying patterns using ML. Support and implement real-time evidence based CAPA management.
Prediction of intermediate and final CQAs, enabling corrective actions and expedited, evidence-based batch reviews.
intelligent Asset Performance Monitoring
Baseline health for multi-product/recipe assets and unit operations equipment. Continuous risk monitoring of critical failure modes and early detection of process induced faults. Real-time root cause detection with severity rating.
Reliability Centered Maintenance
End-to-end digital workflow for RCM using machine learning for risk-based asset health management and predictive maintenance.