Accelerate Quality Deviation Reviews with Advanced Analytics and AI

[Ahmet Ö.]

Advanced analytics and AI-powered platforms, combined with expert knowledge, enable faster investigation of quality deviations in production processes, intelligent root cause analyses, easier reporting, and increased accuracy. They also provide significant insights into operational efficiency.

### Common Challenges in Quality Deviation Investigations

While quality control is critical in production processes, issues such as resource constraints, manual operations, lack of real-time data, and excessive documentation slow down investigation processes. Especially in the pharmaceutical, food, and healthcare sectors, strict legal regulations further complicate matters.

Manual data collection and pulling information from different systems are both time-consuming and divert teams from value-creating activities. Dispersed and disconnected records make it difficult and time-consuming to determine the root causes of deviations.

### Streamlining Workflows with Digital Solutions

AI-powered advanced analytics platforms provide speed and accuracy by directly pulling data from their sources without copying it. These systems automatically detect deviations and enable rapid intervention by creating real-time dashboards and monitoring critical process parameters.

Experts can perform trend analysis on these platforms without requiring coding knowledge, and clearly identify potential problems by comparing with historical data. All investigation information is gathered in a central location, improving collaboration and reporting processes.

### Bioreactor Quality Deviation Management with Seeq Platform

A leading pharmaceutical manufacturer accelerated deviation investigations using a four-stage process with an advanced analytics platform called Seeq:

• Real-Time Monitoring: Operators can quickly identify deviations using dashboards showing critical process parameters.
• Triage and Contextualization: With the AI-powered system, the details and potential causes of the deviation are quickly analyzed, and relevant engineers are informed.
• Root Cause Analysis: Engineers determine the main causes of the deviation by comparing historical and current data.
• Reporting and Information Sharing: AI-powered reports are automatically generated and presented to quality and production managers.

### Conclusion

Advanced analytics and artificial intelligence solutions significantly reduce the workload of experts and enable faster resolution of quality deviations compared to manual processes. This allows industrial enterprises to improve their processes and increase their competitiveness. AI helps prevent the recurrence of deviations by learning from past data, while enhancing regulatory compliance and operational efficiency.