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Path to Zero Failures in Pharma Manufacturing using AI and Automation

CASE INTRODUCTION

In the pharmaceutical manufacturing sector, batch failures and deviations in Critical Quality Attributes (CQAs) pose substantial risks, leading to significant financial, operational, and reputational damages. These disruptions can affect drug supply, elevate drug costs, and, most critically, endanger human lives. Hence, both pharmaceutical companies and regulatory agencies such as the FDA and EMA are dedicated to eradicating production failures to ensure public health and uphold the industry's integrity.

Root cause analysis for batch deviations typically spans 2 to 6 months, scrutinizing 1,000 to 10,000 parameters per batch. This extensive analysis involves Critical Process Parameters (CPP) and Critical Material Attributes (CMA) across numerous batches, relying on a non-predictive, manual, and partially electronic data extraction process. This method often fails to leverage past knowledge and depends heavily on individual expertise, leading to low confidence and inefficacy in preventing failures.

Nuron offers a transformative solution that automates deviation and root cause analysis, reducing the timeframe from months to mere hours or days. By employing an AI-driven, step-by-step, and data-centric approach, Nuron swiftly identifies the root causes of deviations and failures, moving away from intuition-based methods. Moreover, Nuron's machine learning capabilities provide real-time predictions of CQAs, failures, and deviations. This is achieved by integrating live production batch data with historical data from numerous previous batches. The automation of the ETSAD (Extraction, Transformation, Search, Analysis, and Decision-Making) workflow significantly enhances efficiency and accuracy, paving the path towards zero failures in pharmaceutical manufacturing.

PROBLEM

Batch failures and deviations in critical quality attributes (CQAs) cause significant financial, operational, and reputational losses for pharma companies.

These issues result in significant disruptions to drug supply, increased drug prices, and, most importantly, potential threats to human lives. Consequently, pharmaceutical companies and regulatory agencies, such as the FDA and EMA, are deeply committed to eliminating production failures to safeguard public health and maintain the integrity of the pharmaceutical industry

healthcare_services

5-10% batches have quality issues

62% Drug shortage caused by quality failures

CAUSE

Root cause analysis for batch deviations and failures takes 2 to 6 months, involving 1000-10,000 parameters per batch.

Critical process parameters (CPP) and material attributes (CMA) impacting quality attributes require analysis of data from 10s to 100s of batches.

The current manual and partially electronic data extraction process is non-predictive, post-partum, does not use past knowledge, and relies on individual experience, resulting in low confidence and an inability to prevent failures promptly.

Current Manual Process for CSR

Paper Scans, Digital Data
PDFs, Other Files
Manual Data Extraction
Manual Analysis
Intuitive Root Cause Analysis

NURON DATA-TO-DECISION MAKING SOLUTION

Nuron automates the deviation and root cause analysis, completing the process in a matter of hours to days, a significant improvement over the traditional timeframe of months

Nuron's solution utilizes an Al-driven, step-by-step, and data-driven approach rather than relying in individuals' intuition, to swiftly identify the root causes of deviations and failures

Additionally, our machine learning capabilities enable real-time predictions of Critical Quality Attributes (CQAs), failures, and deviations

This is achieved by seamlessly integrating live production batches' data and documents with information extracted from tens to hundreds of previous batches

Automation of ETSAD Workflow

(Extraction, Transformation, Search, Analysis, and Decision-Making)

ETSAD Workflow
Scanned PDFs, Other Files (BMR, QA, SCADA Files)
Al Powered Data Extraction
Text, Data, Images, Tables, Handwriting, SCADA Data
Multi-Batch Analysis
Auto-Organize Auto-Analyze
Automated Root Cause
Live Failure Predictions

Step-by step Data to Decision Workflow

Compare
Interactive Visualization
Correlations
Gen AI
Critical Factors
Reports
Predictive ML
ETSAD Workflow
Scanned PDFs, Other Files (BMR, QA, SCADA Files)
Al Powered Data Extraction
Text, Data, Images, Tables, Handwriting, SCADA Data
Multi-Batch Analysis
Auto-Organize Auto-Analyze
Automated Root Cause
Live Failure Predictions

Step-by step Data to Decision Workflow

Compare
Interactive Visualization
Correlations
Gen AI
Critical Factors
Reports
Predictive ML

GENAI POWERED REPORT ANALYSIS & WRITING

Nuron harnesses the power of GenAl to efficiently search and extract insights from various sources, including previous investigation reports, CAPA reports, equipment reports, and other relevant documents. Additionally, we deploy custom GenAl solutions to automate the process of report writing.

Paper Docs PDF, Word etc. Structured Data Images

Custom GenAl

Full Text Search Q/A Search Similarity Search Data Analysis

Full Text Search Q/A Search Similarity Search Data Analysis

VIDEO

Achieving Zero Failures in Pharma Manufacturing with AI: A Deep Dive into Nuron's Data-to-Decision Solution

VIEW MORE

Learn more about how Nuron's AI-driven solutions can transform pharmaceutical manufacturing and ensure zero failures. Get the full case study by clicking the link below to delve into the detailed insights and methodologies.

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CONTACT FOR A DEMO

Contact us to better understand your business needs

Nitin Kumar - Ph.D. - CEO and Founder

nitin@nuron.io • 1 627 820 4631

Dr. Himanshu Sharma - Head, Business Development

himanshu.sharma@nuron.io • +91 8451941616

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To know more about Nuron's expertise in pharma, click below

https://www.nuron.io/pharmaceuticals.html
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