To achieve Agility in manufacturing – embrace Variability – Part 1
Posted 1944 days ago
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Turning Variability into Agility – Why it Starts with MOM (Manufacturing Operations Management) – Part 2
By- Rajiv Anand
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Customer Stories
Life Sciences
10% Capacity Uplift and 20% Less Variability in Chromatography Purification
Consumer Packaged Goods
Predictive Quality Optimization Cuts Lab Costs by 80% in CPG Manufacturing
The Business Value of Industrial Success Stories
See how Quartic helps manufacturers cut costs, improve quality, and boost throughput across Life sciences, Chemicals, CPG, and more.
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Pharma Value Drivers
Driving Pharma Efficiency with Digital Intelligence
Deliver rapid scale-up, predictive quality analytics, and 21 CFR Part 11 data integrity
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Connect real-time product, process, and equipment data for pharma manufacturing analytics
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Implement digital manufacturing production process development to scale new molecules faster and smarter
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Maintain 21 CFR Part 11 data integrity and compliance, with real-time release
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Use anomaly detection and predictive monitoring to improve asset reliability management
Pharma Operations Challenges
Critical Challenges in Pharma Operations
Siloed process and product data systems
Legacy tools limit visibility into batch context and slow decision-making with analytics in pharma manufacturing
Lack of predictive insights into batch variability
Inability to detect early shifts with CPV in pharmaceutical manufacturing slows down product release.
Under-utilization of PAT investments for quality and process yield improvement.
Manual spectral data analysis without PAT automation increases cycle time and prevents real-time release readiness.
Tech transfer and scale-up delays with manual processes
Inefficient scale-up of manufacturing production process development slows down time-to-market and reduces profitability.
Reactive maintenance and unplanned downtime
Route-based inspections and legacy condition-based maintenance fail to monitor hidden risk of failure and require too much manual analysis effort.
Struggling with inconsistent quality and yield?
Explore how AI in pharma manufacturing unlocks improvement in process intelligence, yield, and quality.
Applications
Targeted Pharma Applications
Product Performance Improvement
QualityBy Design
Batch Optimization
Predictive Maintenance
Improved Vaccine Purity
Used in-silico model optimization to increase vaccine purity & enhance product recovery
+11% purity
Improved Yield in Chromatography
Used golden batch modeling to increase batch yield and minimize degraded output.
+10% capacity
The business value of Industrial Customer Success Stories
AI-Driven Process Development
Accelerated batch design using ML-based DoE for faster bioprocess analytical monitoring.
-75% DoE time
AI-Optimized Fermentation Boosts Protein Yield
Enhanced product yield through AI -driven models in real-time
+10% yield improvement
The business value of Industrial Customer Success Stories
Higher Yield in Biopharma Batches
Enabled batch process optimization using AI-based pattern detection.
+20% batch yield
Optimized Freeze Drying Batches
Shortened drying cycle time while maintaining product quality, improving batch throughput.
-15% cycle time
The business value of Industrial Customer Success Stories
Downtime Reduction with AI
Enabled predictive alerts for asset reliability management and maintenance optimization.
-50% downtime
Reliability Insights for Fermentation Asset
Early warnings reduced unplanned shutdowns with batch prediction ML models.
-60% unplanned stops
The business value of Industrial Customer Success Stories
With Quartic’s AI-based CPV and PAT automation, we reduced release review time by 40% while increasing yield.
Core Capabilities
Advanced Capabilities for Pharma & Biotech Efficiency
Process Design & Characterization
- Integrated digital workspace for managing process design and experiment
- DoE in real time using a combination of process and PAT data streams
- Utilize previous experiments data to expedite the molecule development
- Use empirical and mechanistic models to improve wet experiments
Knowledge Management for Development
- Easily connect experiment results to product quality submissions
- Utilize past characterizations to support future scale-up projects
- Gather clinical and regulatory data for QbD documentation
- Use development history to create models that support CMC submissions
Tech Transfer with Digital Twin
- Develop and refine digital twins during the phases of design and qualification
- Encourage tech transfer in pharma industry by maintaining an ongoing flow of knowledge
- Capture pre-qualification learnings for commercial operations
- Enable faster validation cycles and scale-up
Unified Knowledge Management
- Centralize product control strategy and compliance workflows
- Integrate QC, lab, and process data using digital twin software
- Enable no-code pipelines for ML and AI use cases
- Support real-time release and exception-based batch review
Optimization, ILM & Real-Time Release
- Use AI-driven CPV and PAT automation to forecast CPPs and CQAs
- Improve process yield and reduce batch rejections
- Implement real-time release to accelerate batch disposition
- Lower cost of quality with closed-loop intelligence
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