Manufacturing Capacity
Manufacturing capacity and raw material constraints to meet production demands require optimization of legacy processes and equipment.
Manufacturing Complexity
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.
Product
Quality
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).
Manufacturing Data Standardization
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.
Asset Reliability
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.
Ready to Deploy Applications
Process Development
Digitalized PD
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 MVDA
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.
Process Optimization
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.
PAT Automation
Automated PAT analysis to implement in-line monitoring, minimize transition times between unit operations, support real-time release and enable closed-loop control.
Quality Monitoring & Prediction (CPV)
Abnormal Event Prediction
Turn alarm and failure history into proactive insights and predictions to make downstream equipment more responsive to process deviations.
Non-conformance Monitoring
Identify deviation trends for intelligent QMS by discovering underlying patterns using ML. Support and implement real-time evidence based CAPA management.
Predictive Quality
Prediction of intermediate and final CQAs, enabling corrective actions and expedited, evidence-based batch reviews.
Reliability
intelligent Asset Performance Monitoring
(iAPM)
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
(RCM)
End-to-end digital workflow for RCM using machine learning for risk-based asset health management and predictive maintenance.
Capabilities for an Agile and Resilient Supply Chain
Process Design & Characterization
- Integrated digital environment for knowledge capture, design, and experimentation
- Combine PAT and process data streams for real-time DOE projects
- Accelerate molecule development by leveraging prior experiment results
- Augment wet experiments with in-silico models – both mechanistic and empirical models
Knowledge Management
- Link experiment results to product and quality submissions
- Leverage knowledge and characterization of past projects to aid in development and scale up of new, similar projects.
- Capture clinical run data up to and including Regulatory Approval Submissions.
- Use development and clinical data as part of the robustness of the product and process design history to support CMC submissions (Collecting data, building models foundation of QbD)
Tech Transfer
- Create and feed the “Digital Twin” through the design, build, qualify stages, then model and optimize using it as part of manufacturing process.
- Coordinate and share experiences to and from DOEs and to PHAs, Tox, ADME, and MSAT Teams.
- Capture any pre-qualification state failures or boundary conditions learnings to translate into the PQ or PV and commercial operations
Process Qualification
- Capture product profile and performance during engineering and consistency runs
- Limit testing CPPs and their impact on CQAs with process equipment
- Optimizing control strategy for CPPs
- Using PAT to design and analyze inline measurements
Knowledge Management
- Define and capture control strategy changes through the product’s lifecycle
- Integrated environment for QC/Lab/Process data
- Operation and Product context for QC/Process/IoT data
- No-dev data pipeline and preparation for ML and informatics
- Accelerated Tech Transfers
- Uniform and globalized PLM
Quality by Design
- Foundation CPV - Predictive monitoring of CPPs and CQAs
- Automated DoE for batch/continuous processes
- Faster DoE experiments and results with ML
- Real-time batch and product analysis results in less FTE hours
- Robustness Product Portfolio
- Reduction in rejects and waste materials/batches
Process Optimization and ILM
- Transfer data/models from previous development stages
- Operation and Product context for PAT data modelling
- Real-time soft sensors from PAT data
- Reliability Centered Maintenance
- Faster scale up and expansion
- Consistent yield and quality
- Increase equipment uptime and safety
Review by Exception/Real-Time Release
- Faster batch disposition – product time to market
- Reduced investigation hours of spec limit violation – Quality function
- Reduced cost of compliance – automated evidence-based compliance reviews.
- Closed-loop control with PAT sources
- Lower cost of Quality
Introducing MDS (Manufacturing Design Studio) from ZS powered by Quartic
A Complete Life Sciences Supply Chain Solution for R&D, Planning, Development, Engineering, Manufacturing, and Quality
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