Scenario: A Biotech Company Analyzing 500+ Deviations to Uncover Trends and Gain Cross-Departmental Clarity
A mid-sized biotech company had over 500 deviations logged at a single manufacturing site, spread across multiple departments including Quality, Manufacturing, Engineering, and Supply Chain. The data resided in their existing systems (QMS, Excel exports, reports), and they saw an opportunity to gain deeper insights—without overburdening internal teams or resources.
Their goals:
• Identify root causes and systemic issues
• Understand patterns and emerging risks
• Create actionable insights to drive continuous improvement
• Highlight potential areas of focus across departments
However, they did not want to integrate another system. They needed insightful analysis that could work seamlessly with their existing workflows.
Our Approach:
| Category | What We Did |
| 1. Clustering & Categorization | – Auto-categorized deviations using NLP from free-text entries – Grouped similar deviation types, root causes, and impacted products – Detected untagged or misclassified issues |
| 2. Trend Analysis | – Conducted temporal trend analysis (monthly, quarterly patterns) – Created departmental heatmaps of deviation volume and types – Tracked root cause frequency and evolution (recurring vs. emerging issues) |
| 3. Correlation & Impact Assessment | – Linked deviation types to outcomes (e.g. batch failures, delays, CAPAs) – Highlighted high-risk areas based on recurrence and severity |
The Results
Identified 3 procedural issues responsible for 30% of all deviations, enabling targeted SOP and training updates across departments.
1. SOP Adherence Issues
Deviations resulting from inconsistent or incorrect execution of procedures—often documented as training gaps, operator errors, or misinterpretations.
2. Documentation and Batch Record Errors
Incomplete or mismatched entries, missing signatures, or other record-keeping issues—appearing across both Quality and Manufacturing teams.
3. Material Handling or Labeling Mistakes
Errors such as incorrect labeling, wrong material usage, or mix-ups—frequently seen in Supply Chain, QA, and Manufacturing, but described in varying ways.
Identified an increasing trend of material-related deviations linked to a vendor change that occurred 6 months prior. This led to supplier review and corrective actions that eliminated future related events.
Several deviations classified as ‘minor’ showed strong language similarities to past ‘critical’ events in other departments—suggesting possible risk normalization and highlighting the need for consistent cross-functional risk evaluation.
Most importantly, the client gained a centralized, data-driven view of deviation risks across teams—turning reactive compliance into proactive process improvement
In regulated industries like biotech, deviation data often holds untapped operational insights. Our AI-driven approach uncovers that value without requiring system integration—making it scalable, fast to deploy, and completely non-disruptive to your existing operations.
If you’re dealing with a growing number of deviations and want to turn data into decisions, reach out to us for a pilot.