- 01 Real-time visibility into yield trends during production
- 02 AI-identified root causes with human-explainable reasoning
- 03 Closed-loop tracking of recommendation effectiveness
- 04 Multi-site OEE benchmarking and comparison
What FDA Inspectors Cite
Yield tracking failures and incomplete production records are frequent 483 targets. FDA expects manufacturers to calculate yields at every phase, investigate discrepancies, and maintain equipment in a state that does not compromise product quality.
Batch yield calculations exclude visual inspection defects without scientific justification
Pine Pharmaceuticals, LLC
September 30, 2024
The firm inappropriately calculated batch yields by leaving out all visual inspection defects, which potentially accounted for significant loss of the batch. There was no scientifically sound justification for excluding inherent particulate rejects from percent defect or yield calculations (actual versus theoretical yield).
Why Yield Losses Go Undetected and Unresolved
Pharmaceutical manufacturers generate enormous volumes of production data, yet most lack the systems to turn that data into actionable yield intelligence. These operational barriers keep yield optimisation stuck in reactive mode.
Challenge 1/7
Unstructured Data from Logbooks and Manual Records
- 01Critical yield-impacting events recorded as handwritten notes or unstructured text entries
- 02Logbook data never enters the analytical pipeline—it stays in binders or siloed digital logs
- 03Pattern recognition across logbook entries is impossible without manual review of thousands of records
Agentic Architecture
Six specialised AI agents orchestrated by Celestara, each with domain-specific skills, working across your connected manufacturing data sources to deliver continuous yield intelligence.
Data Layer
Celestara
Orchestration Layer
Orchestrates agent workflows, manages context windows, enforces 21 CFR Part 11 compliance, and maintains the closed-loop feedback cycle across all agents
Quality Outputs
Yield Optimisation in Celestara
1.Unified Data Collection Across All Sources
Celestara continuously ingests data from MES batch records, digital logbooks, LIMS results, ERP material data, equipment sensors, and environmental monitoring systems. Unstructured logbook entries are parsed and structured by the Data Collection Agent, creating a unified production context that connects yield figures to the conditions that produced them.
2.Real-Time Yield Tracking and Anomaly Detection
The Yield Analytics Agent monitors yield at every production phase in real time—not just at batch close. Statistical process control charts flag when yield trends deviate from historical norms. Supervisors see live dashboards that show yield trajectories during production, enabling intervention before a batch is lost.
3.Multi-Variable Pattern Recognition
When a yield anomaly is detected, the Pattern Recognition Agent correlates the yield data with equipment performance, environmental conditions, material lot attributes, and operator logbook entries. It searches historical batches for similar patterns and identifies which variables most strongly predict yield outcomes—surfacing the systemic factors that manual investigation misses.
4.Human-Explainable Recommendations
The Recommendation Engine translates pattern analysis into specific, actionable suggestions that engineers and operators can understand and evaluate. Each recommendation includes the data evidence behind it, the expected yield impact, and a confidence score. Recommendations are prioritised by potential yield recovery and feasibility of implementation.
5.Closed-Loop Feedback and Efficacy Tracking
Every recommendation is tracked from issuance through implementation to outcome. The Feedback Loop Monitor Agent measures whether the recommendation was adopted, whether yield improved on subsequent batches, and by how much. Recommendations that prove effective are reinforced; those that do not are flagged for review. This completes the loop that manual processes never close—turning yield optimisation from a one-time investigation into a continuously improving system.
Inputs
Data Collection Agent
LeucineOS Agent
Outputs
Inputs
Yield Analytics Agent
LeucineOS Agent
Outputs
Inputs
Pattern Recognition Agent
LeucineOS Agent
Outputs
Inputs
Recommendation Engine Agent
LeucineOS Agent
Outputs
Inputs
Feedback Loop Monitor Agent
LeucineOS Agent
Outputs
How Leucine Solves This
Purpose-built tools that address both the FDA compliance gaps and the operational barriers to effective yield management and OEE optimisation.
Unified Yield Visibility Across All Data Sources
Celestara ingests and correlates data from MES, logbooks, LIMS, equipment sensors, and environmental monitoring to provide a single view of yield performance. AI agents parse unstructured logbook entries, track yield at every production phase in real time, and detect anomalies before batches are lost—replacing post-hoc spreadsheet analysis with continuous, contextual yield intelligence.
Capabilities
- Real-time yield tracking at every production phase with SPC charting
- AI-powered parsing of unstructured logbook and manual record data
- Multi-variable correlation linking yield to equipment, environment, and material factors
- 21 CFR Part 11 compliant audit trail for all yield calculations and investigations
Measurable Impact
Projected results from deploying Celestara for yield and OEE optimisation across pharmaceutical manufacturing operations
Yield Improvement
Average absolute yield increase across production lines through AI-identified process optimisations
OEE Increase
Improvement in Overall Equipment Effectiveness through visibility into availability, performance, and quality rate drivers
Investigation Time
Reduction in time spent investigating yield deviations, from days of manual data gathering to hours with AI-assembled context
Recommendation Effectiveness
AI recommendations that measurably improved yield on subsequent batches, verified through closed-loop tracking
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Celestara
The AI-native data intelligence platform — orchestrating specialized agents to transform raw enterprise data into structured ontologies, automated pipelines, and actionable analytics.
Explore CelestaraYield losses are not random—they follow patterns buried across your MES, logbooks, and equipment data. Celestara AI agents surface those patterns, recommend specific actions your teams can understand, and track whether those actions actually work. Stop investigating yield problems one batch at a time. Start seeing the system-wide picture.
