Quality Innovation Tour – North America events in October and November 2022

Integration of Quality Management and Manufacturing Automation Systems: The Key to Enabling Quality by Design

 

The increasing complexity in the Life Sciences industry continues to drive digital transformation. Life Sciences manufacturers are faced with quality anomalies that can delay product release for hundreds of hours. Often, quality management systems rely on manual processes for data transfer. This proves to be inefficient, hinders visibility, creates data integrity concerns and causes lengthy delays.  

Additionally, despite the growing application of continuous process verification, many such programs don’t use real-time automation data for verification which limits quality by design (QbD) value, increases compliance risk and threatens supply chain continuity.  

The benefits of enabling QbD with AI-driven quality operations include connecting AI-enabled quality management and batch automation to provide real-time visibility, using real-time critical process parameter alerts to provide early detection, and more.  

QbD is enormously valuable and generates a lot of data and knowledge. Unfortunately, it tends to be underutilized, siloed and not operationalized. It is too often an exercise, executed at the early stages of product development but was always intended to be dynamic, increasing its value across the lifecycle of a product.  

The combination of Sparta Systems and Honeywell functionality is changing that. 

Download our latest white paper to learn how the integration of Honeywell Experion and TrackWise Digital are increasing the value of QbD through: 

  • Integrated quality and batch operations that give organizations real-time, end-to-end visibility, enable faster and better decision making and proactive response to quality events.  
  • Real-time CPP alerts and corrective action guidance to provide early detection of manufacturing anomalies, prevent quality violations and ensure higher product quality.  
  • Process context for quality investigations to reduce the time needed to investigate quality issues, increase operational efficiencies and provide AI-enabled auto-categorization and correlation.  

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