Predictive Performance Monitoring in Bio-Manufacturing Powered by Unified Data Model

This global leader in bio-pharmaceutical innovation is dedicated to advancing cell and gene therapies that transform patient lives. It operates in a highly regulated landscape under stringent GMP standards and relies on data to ensure the highest levels of quality and safety throughout its manufacturing processes. The organization generates data from diverse data sources—from precise sensor-derived metrics to comprehensive unstructured records such as batch logs, deviation reports, and maintenance documents. The company expects its vast array of data sources to empower teams and professionals with the insights required to continually elevate standards. With an unwavering commitment to excellence and innovation, the organization isn’t just using data to meet today’s challenges, but pioneering tomorrow’s breakthroughs in transformative healthcare solutions.

Locked in silos: The uphill battle for production insights

The production facility struggled to connect critical production insights from across a siloed data landscape. As a result, the business faced profound challenges that had a direct impact on business results including quality and regulatory risks, operational inefficiencies, reactive decision-making, financial losses, and stifled innovation. The fragmented data landscape didn’t just hamper immediate production performance—it posed significant risks to quality, regulatory compliance, and overall financial health. In particular, the bio-pharmaceutical company was hindered by:

Fragmented data: Core production data was spread across LIMS, MES, QMS, and SCADA systems, as well as standalone lab instruments, maintenance reports, and paper based documentation.

Reactive decision-making: Deviations and yield losses were typically detected after they occurred, delaying root cause analysis and corrective actions.

Limited data contextualization: Analysts and operations teams spent weeks preparing data from multiple sources to correlate root causes across quality, equipment, and environmental parameters.

No predictive insight: Without real-time contextual data, the plant was unable to proactively detect early signals that could prevent process drift or batch loss.

From data silos to predictive insights: Enabling proactive operational excellence

To overcome these challenges, the manufacturer partnered with On Time Edge to leverage ZONTAL, to create a real-time performance monitoring framework that bridges structured and unstructured data across the plant.

Unified data model with ZONTAL

ZONTAL created a digital backbone that ingested structured sensor data (e.g., temperature, pH, and flow rates), unstructured documents (e.g., batch deviations, audit trail logs), and semi-structured reports (e.g., LIMS outputs, maintenance records) into a unified, contextualized data model.

Real-time data streaming and integration by On Time Edge

On Time Edge’s expert team is designing and implementing the integration of upstream and downstream systems, aligning each data source with the ISA-95 model. Using custom adapters and lightweight edge services, On Time Edge is enabling secure, real-time streaming of relevant signals and events directly into the ZONTAL platform.

Monitoring four key disparate parameters

The scope is focused on four critical indicators of production health:

• Bioreactor temperature drift (structured – SCADA)
• Operator deviation reports (unstructured – QMS)
• HEPA filter maintenance logs (semi-structured – CMMS)
• Batch yield loss patterns (structured – LIMS/MES)

ZONTAL is harmonizing these signals into a time synchronized digital record. On Time Edge deployed predictive analytics workflows that continuously analyzed correlations between these disparate data types to identify leading indicators of batch failure or productivity loss.

AI-driven alerts and dashboarding

On Time Edge is leveraging AI models and KPI dashboards tailored to user personas (QA, maintenance, manufacturing engineering, plant management), providing predictive alerts when combinations of anomalies indicated a high risk of impact to batch performance or compliance.

Data-driven excellence: Empowering proactive maintenance, rapid diagnosis, and consistent yields

Reduced unplanned downtime — Early detection of HEPA degradation correlated with yield deviations allow the team to proactively schedule maintenance before critical failures occurred.

Faster root cause analysis — Automated context linking between deviation reports, batch performance, and sensor anomalies enabled QA and process engineers to resolve issues in hours instead of days.

Improved batch yield consistency — Predictive alerts on bioreactor drift and equipment wear helped operators adjust process parameters in real-time, avoiding costly deviations and reducing batch-to-batch variability.

Audit-ready data lineage — By preserving the full digital thread of every parameter, ZONTAL will enable faster, more confident responses to regulatory audits, with complete traceability across structured and unstructured data sources.

Through a strategic collaboration between On Time Edge and ZONTAL, this bio-manufacturing plant is bridging the gap between fragmented data and real time production insight. The combined power of domain expertise, smart data streaming, and unified data modeling transforms a reactive environment into a proactive, AI enabled production floor delivering scalable and measurable improvements in compliance, yield, and overall performance.