Today’s pharmaceutical environment, speed is not just a competitive advantage — it is a patient requirement. Every hour spent waiting for batch disposition delays lifesaving therapies reaching patients who depend on them.
-Larry Fiegland, Discoverant Product Manager
Yet, despite major investments in Industry 4.0, many manufacturers still wait days or weeks for data consolidation, manual review cycles, or investigation resolution before releasing their products.
The disconnect is rarely technology hardware. Plants generate enormous volumes of data. The challenge is turning that data into timely, contextual decisions.
Real-time analytics is changing that. Solutions such as Dassault Systèmes’ Discoverant platform are changing that dynamic. Instead of waiting for end-of-batch analysis, organizations are using continuous monitoring, multivariate insight, and role-specific visualization to accelerate release by exception, reduce investigations, and increase confidence in product quality.
The Problem: Data Exists — Insight Doesn’t
Every pharmaceutical batch produces:
- instrument data
- in-process quality attributes
- environmental measurements
- offline testing results
- operator and equipment event logs
But too often, these are siloed in disparate systems — LIMS, historian platforms, MES, spreadsheets, or even paper records. Quality teams spend significant time pulling, restoring, reconciling, and validating the data before they ever analyze it.
That latency matters. Slow insight means:
- Deviations are discovered too late
- Investigation cycles lengthen
- Batches wait for approval
- Risk decisions are made without complete visibility
The result is both operational delay and regulatory frustration.
Real-Time Analytics Changes the Equation
Real-time is not about “fast charts.” It is about enabling decisions while the process is occurring.
For example, Discoverant continuously ingests and contextualizes process, quality, and historian data into a structured process record. With that foundation, teams can:
1. Detect process drift before it becomes a deviation
Multivariate control models capture relationships across process parameters, not just single thresholds. When the interaction between variables becomes unstable, alerts allow intervention while the batch is still recoverable.
2. Compress investigation cycle time
Root causes are not hidden across systems — data context is preserved and available instantly. Process engineers can review trends, batch comparison, and historical signatures rapidly, avoiding days of manual data wrangling.
3. Empower exception-based release
Instead of reviewing every parameter for every batch, quality and release teams can focus only where risk exists — accelerating disposition without reducing scrutiny.
4. Drive learning across products and sites
Discoverant aggregates process intelligence over time, enabling pattern recognition, recurring deviation detection, and capability tracking across products and facilities.
Case Example: Release Without Waiting
Imagine a fermentation process where performance hinges on precise control of temperature, pH, nutrient feed rate, and a specific dissolved oxygen trajectory.
Traditional monitoring watches these individually. But acceptable values alone do not guarantee an acceptable product — the relationship between them matters.
A multivariate monitoring model built on historical successful runs learns the expected interaction among these variables. Now, on each batch:
- The system identifies emerging deviation fingerprints hours before sampling shows impact
- Engineers can adjust parameters proactively
- Data and model evidence go directly into the release dossier
The effect? Release happens faster because confidence arrives earlier.
Instead of waiting for offline assay results to identify a bad batch, the batch is guided to success — or flagged early if risk increases.
Why the Shift Matters Now
Three forces are converging:
1. Regulatory Encouragement for Data-Driven Control
Agencies increasingly endorse continued process verification, process knowledge, and real-time review (ICH Q10/Q12, FDA emerging technology focus). Solutions like Discoverant help demonstrate process understanding, traceability, and knowledge management maturity.
2. Margin Pressure Requires Leaner Decision Cycles
With pipeline uncertainty, loss of exclusivity, and global complexity, wasted time directly affects operating margin.
3. Workforce Knowledge Gap
Newer staff have less tacit process knowledge. Good analytics compensate by guiding interpretation and elevating risk signals.
Real-time analytics is therefore both a productivity tool and a risk-management tool.
The Path to Real-Time Release Capability
Organizations often think this transformation requires new sensors or new systems. In reality, the critical steps are:
1. Connecting existing data sources
Integrate control systems, lab data, quality records, and event logs into a common analytical backbone — with lineage and integrity preserved.
2. Establishing contextualized models of process behavior
This includes process fingerprinting, multivariate control models, adaptive limits, and golden batch monitoring.
3. Delivering role-specific visibility
- Operators get actionable status and alarms
- Engineers receive diagnostics and capability analytics
- QA receives automated batch assessments and exception flags
4. Culture change from reporting to learning
Discoverant’s analytics shift the organization away from passive documentation toward active insight and intervention.
The Impact: Faster, Safer, More Confident Release Decisions
Companies that achieve real-time analytical maturity report:
- Reduced investigation cycle time
- Fewer unplanned excursions
- Shorter release timelines
- Higher transparency during regulatory inspection
- Stronger cross-site knowledge sharing
Most importantly, they deliver the product more reliably to patients.
Conclusion: Speed with Confidence is the New Mandate
The pharmaceutical industry does not suffer from lack of data — it suffers from lack of timely understanding. Real-time analytics bridges this gap by converting raw information into actionable decisions during the process, not weeks after it.
Faster release is not about cutting corners — it is about knowing earlier, responding earlier, and proving control earlier.
Companies adopting capabilities like Discoverant aren’t just accelerating batch disposition; they are transforming how process knowledge is used — from hindsight to foresight — and that may be the most powerful shift happening in pharmaceutical operations today.
Learn how BIOVIA Discoverant can help you unlock the full value of your laboratory and manufacturing data.
As the Discoverant Product Manager, Larry is part of the Research and Development organization within BIOVIA and is responsible for sales activities and product development for the BIOVIA Discoverant solution. Larry obtained his Ph.D. from Virginia Tech under the direction of Professor John R. Morris, working on ultrahigh vacuum studies of the reaction mechanisms of ozone with saturated and unsaturated self-assembled monolayers. Prior to graduate school, Larry’s industrial experience includes four years as a New Process and Development Chemist working on scale up of chemical processes from pilot plant to full-scale manufacturing.