INSUS predicts batch yield from process, raw-material, quality and historical batch data while production is still underway, giving pharmaceutical teams earlier visibility into deviations. Process and quality teams keep the decision.
01
Lab results can arrive hours after a batch is complete.
02
Yield losses and deviations may already be locked in.
03
Predict the batch outcome while there is still time to act.
The problem
Pharmaceutical chemical synthesis involves multi-step processes where raw-material quality, reaction conditions, temperature, pressure, concentration, agitation and catalyst state can affect the final result. Sampling and laboratory analysis often provide critical quality information only after a batch has progressed or completed. By then, low-yield or off-spec outcomes can already mean rework, scrap, extended cycles and delayed release.
The shift
Process and quality teams stay responsible for every intervention. What changes is whether the likely yield and deviation from successful batch behavior are visible while the batch is still running.
Today
With INSUS
How it works
This fits OptiStudio as process and quality decision support beside existing MES, historian and LIMS systems. Access is read-only. It does not automatically change pharmaceutical process parameters.
Connect
Connect read-only to DCS/SCADA, process historians, LIMS, MES, raw-material information, batch records and other available production and quality data.
Understand
Analyze historical batch behavior, process variables, raw-material characteristics and quality outcomes to identify relationships associated with high-yield and successful production.
Predict
Estimate the likely batch yield as production progresses and detect multivariable deviations that may indicate a potential yield or quality problem.
Advise
Provide actionable recommendations based on predicted outcomes and process deviations, helping process teams evaluate adjustments before implementing them under existing quality procedures.
Verify
Compare predicted yield with actual batch and laboratory results so the model can be validated and refined for the specific product and synthesis line.
Getting started
For pharmaceutical manufacturers that already collect batch, process, raw-material and quality data. Delivered through OptiStudio as batch-yield decision support. Expert Services can help with data assessment and validation. Advisory and read-only. Implementation must fit applicable pharmaceutical quality and validation requirements.
Related for pharma
Batch yield prediction sits beside process-parameter optimization and contamination risk prediction on the same pharmaceutical operation.
Yield and quality insight often arrive after a batch has progressed. This use case predicts likely batch yield and flags deviations from successful batch behavior while production is still underway.
Historical batch records, DCS/SCADA and historian data, LIMS quality results, MES batch information and relevant raw-material characteristics. No new sensors when those systems already exist.
No. It is decision support. Qualified operators and engineers review every recommendation, especially where regulatory and process-safety considerations apply.
A golden batch is a successful batch behavior used as a reference profile. The current batch can be compared against that profile to identify meaningful deviations for investigation.
Implementation should be designed around the applicable pharmaceutical quality, change-control and validation requirements of the manufacturing environment. The system does not replace those controls.
Through OptiStudio as process and quality decision support. Expert Services can support data assessment, integration and validation with your process and quality teams.
Performance depends on reliable batch and process history. Recommendations are product- and process-specific. The system does not guarantee yield improvement or replace laboratory release decisions.
Pharma · Batch yield prediction
Use your own process and batch history to predict yield while production is underway, identify deviations from successful batch behavior and give process teams earlier insight before the outcome is locked in.
