Yield losses in pharmaceutical manufacturing are rarely caused by a single variable. They emerge from the interaction of equipment performance, environmental conditions, material variability, and operator decisions across thousands of batch records. Yet most manufacturers still investigate yield gaps manually, batch by batch, with no systematic way to connect the signals. Celestara deploys specialised AI agents that sit on top of your MES and logbook data to identify patterns behind yield losses, recommend corrective actions that operators and engineers can understand and act on, and continuously track whether those recommendations actually improve outcomes—completing the loop that manual processes never close.
Key Highlights
Regulatory Context
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).
The firm failed to adequately calculate the theoretical and actual batch yield steps at each phase of the production process for all sterile drug products. Calculated yield steps were not inclusive of all products produced during the manufacturing run. Production proceeded to the next step when actual yield failed to meet the validated range.
Batch production records documented output quantities that did not match actual material found in warehouses. Extra drums of API were discovered with no traceability to manufacturing history, raw materials, or process conditions. The firm could not reconcile documented yields against physical inventory, making traceability of disposition impossible.
Review of BMS data for 2022–2024 revealed too numerous to count excursions of differential pressure, temperature, and humidity throughout production areas. At least 40 work orders for aseptic equipment repairs extended over 100 days. Recurring issues including leak test failures caused process time extensions and batch rejections. Preventive maintenance records failed to identify instruments used for verification.
There was no identification of critical equipment throughout batch production records. Batch records for drug product and finished drug product batches failed to include equipment identification, making it impossible to correlate equipment performance with yield outcomes or conduct meaningful OEE analysis.
Critical calculations required during manufacturing were not documented on the batch records for all lots manufactured across multiple production lines from May 2020 to July 2024. Without documentation of these calculations, there is no audit trail for yield determination or variance investigation at any production phase.
The Problem
Yield Optimisation in Celestara
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.
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.
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.
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.
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.
The Solution
Purpose-built tools that address both the FDA compliance gaps and the operational barriers to effective yield management and OEE optimisation.
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.
The recommendation engine translates yield pattern analysis into specific, human-explainable actions prioritised by expected impact. Each recommendation includes supporting evidence, confidence scores, and feasibility assessment. Every recommendation is tracked from issuance through implementation to measured outcome—creating a closed feedback loop that continuously improves yield performance.
Deploy standardised yield calculation methodologies, equipment performance tracking, and OEE metrics across all manufacturing sites. Every facility captures yield data at the same granularity and in the same format, making cross-site comparison meaningful and enabling best-practice transfer from high-performing lines to those with yield gaps.
Track how FDA cites yield calculation failures, production record deficiencies, and equipment performance gaps across the industry. Understand which yield-related deficiencies draw 483 observations and warning letters so your team can proactively align practices with current enforcement expectations.
Results
Projected results from deploying Celestara for yield and OEE optimisation across pharmaceutical manufacturing operations
Next Step
Yield 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.
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