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Golden Batch Analysis

Problem Statement

During batch manufacturing of drugs, several challenges arises . Data collection presents hurdles with paper-based systems lacking a centralized historical data repository, hindering comprehensive analysis., inconsistency in recording values, and susceptibility to human errors.

Inconsistency in recording values and susceptibility to human errors pose significant challenges throughout the manufacturing process, which involves multiple intricate steps such as blending, sieving, granulation, drying, compression, and coating. These complex operations require meticulous attention to detail and precise data recording at each stage.

Yield and quality face challenges as manufacturers often adhere solely to Original Equipment Manufacturer (OEM) criteria, leading to batch variability and overlooking the need for a risk-based quality monitoring approach. Addressing these challenges is crucial for optimizing batch manufacturing efficiency and ensuring consistent product quality.

Challenges faced by most Pharma Manufacturing organizations are how and why FDA is conducting risk-based audits & how the traditional risk-averse mindset is the biggest bottleneck in pharma. 

Value proposition

We understand that the challenges faced by pharma companies are unique and require custom-tailored solutions., a Custom-tailored solution called “Golden Batch Analysis” is a new paradigm in drug manufacturing process.

Solution Strategy

Parameter Digitalization

  • AI/ML driven algorithm for scanning of old, historical, Paper based BMR
  • AI-driven text parsing precision facilitates the extraction of information, BMR transitioning into e-BMR

Golden Batch Identification

  • Golden batch identification/prediction powered by AI/ML models.
  • Exploring Parameter Influence on Golden Batch: What If Analysis

Relationship Between Critical Process & Quality Parameters

  • Identify the relationship b/w CPP& CQA
  • Identification of Variable importance