Review by Exception: How to Accelerate Product Release With Digital Tools

For life sciences manufacturers and those operating in regulated industries, balancing quality and efficiency is often like walking a tightrope.

On the one hand, businesses must comply with Good Manufacturing Practices, ensuring quality standards are met and SOPs are followed. On the other hand, compliance efforts often slow the production process, preventing product batches from being released and causing inventory to sit in storage.

Faced with rising costs and shorter product lifecycles, life sciences manufacturers continue to look for ways to reduce inventory and accelerate time to market.

One way to achieve this is to reduce the time required to identify, review, and resolve process exceptions.

In this post, we’ll discuss review by exception and how manufacturers are leveraging digital tools to more effectively identify quality issues and deviations, and streamline the review process.

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Review by exception is a modern way of executing quality assurance and control measures to ensure that the final product contains no flaws prior to being released. Instead of reviewing every detail outlined in the associated quality report, many manufacturers leverage review by exception to only highlight deviations, speeding up the process significantly.

For pharmaceutical products, deviations can include quality defects as well as aberration from the established standard operating procedures.

In more traditional manufacturing environments, operators, supervisors, and quality control personnel utilize paper-based methods to record production processes and quality events. These paper reports are forwarded to the quality reviewer to scrutinize the various exceptions.

However, reviewing these production and control record documents can be considerably time-consuming and demanding.

The quality assurance team combs through these multi-page documents to identify and manually clear each exception to the set quality parameters, limits, and tolerances. As a result, this review process holds up product batches, delaying their transition to the release and delivery stage.

Given the rise of digitization across the pharmaceutical manufacturing industry in recent years, manufacturers are increasingly digitizing the review process. This digital approach points out the exceptions automatically, saving the QA team from manually combing through paper documents.

In regulated manufacturing, quality assurance often turns into a choke point and not because people are careless, but because the process itself hasn’t evolved fast enough.

Most QA teams still spend their days combing through batch records line by line. Every field, every timestamp, every process parameter gets checked, even when nearly all of it is perfectly fine. It’s slow, repetitive work that eats up time without really improving quality.

EY estimates that more than 70% of QA effort goes into reviewing documentation. Not investigations. Not process improvement. Just record-checking. Anyone who’s been part of a batch release knows what that feels like, long hours spent validating data that rarely reveals anything new.

And the cost isn’t only time, delayed batch release ranks among the top three causes of supply chain disruption in pharma. When QA is tied up verifying non-critical data, finished goods sit waiting in quarantine, even when they’re ready to ship.

It only gets worse as production volume grows. One small deviation hidden in a 200-page batch record can hold up release for days if there’s no way to flag it automatically. Without Review by Exception, every batch gets treated like a high-risk one, and the backlog builds fast.

Review by Exception flips that script. Instead of reviewing everything, QA focuses on the handful of records that actually need attention, the one or two percent that matter. In real operations, that can cut review time per batch in half, speed up release, and make scheduling a lot more predictable. More importantly, it lets QA spend their time where it counts: investigating real issues, not checking boxes.

Review by Exception is about cutting through noise so QA only looks at what actually needs attention.

Here’s what that looks like on the floor.

Data capture happens as usual.
Operators record process data through digital batch records i.e. weights, temps, sign-offs. They follow the same steps they always have, just without the paper.

System checks run quietly in the background.
Every entry gets compared to limits or set conditions. If something falls outside spec, the system notices right away. Nobody has to dig for it later.

Exceptions get logged automatically.
Each one is tagged based on rules the team defines, maybe it’s minor, maybe it’s critical. The goal is simple: make sure the right people see it fast.

Routing rules decide who gets it.
A minor issue might land with a supervisor to clean up. A critical one goes straight to QA with all the details attached and what happened, where, and when. No screenshots, no guessing.

QA only reviews the outliers.
Instead of scanning every line of a 200-page record, they open the exceptions, check the supporting data, and make a call. If nothing major turns up, the batch keeps moving.

That’s the whole shift. Same data, same people, just less wasted motion.

Types of Exceptions

Not every blip in the data is a crisis. Most aren’t.

Minor exceptions are things like late signatures, missing comments, or small admin gaps. They’re logged for traceability but don’t block release.

Critical exceptions are different. They point to possible quality or compliance risks out-of-range values, failed checks, missing required data. Those get full QA attention before anything moves forward.

Once those definitions are in place, the system applies them the same way every time. No gray area, no back-and-forth.

Roles in an RBE Setup

RBE doesn’t replace people; it just clears the clutter so everyone can focus on what matters.

Operators still run the process and record data. Their routine doesn’t change much.

Supervisors catch and resolve smaller exceptions before they grow into delays.

QA steps in only when something truly needs review—deviations, gaps that could affect product quality, compliance concerns.

Where It Fits

RBE works best when it’s woven into the systems already running production and quality.

• The MES handles production data and process timing.

• The QMS tracks deviations, CAPAs, and approvals.

• The eBR ties it all together.
  
  

When those systems talk to each other, through open connections or shared logic, exceptions get caught at the point of entry, not days later during review.

That’s how Review by Exception actually works in a real plant: fewer surprises, faster decisions, and QA teams spending time where they can make an impact.

Regulator bodies including the Food and Drug Administration require manufacturers to follow good manufacturing practices to ensure the products being developed are safe for human consumption. As such, manufacturers must outline specific production processes and develop strict quality parameters for their products and ensure that each production batch aligns with those specifications.

But as discussed earlier, the traditional paper-based method of identifying quality exceptions leads to a significantly increased time-to-delivery. Not only does this disappoint customers, but it also increases inventory costs due to the lengthy review process.

Because of this, modern manufacturers leverage digitization to implement quality reviews during the production process instead of at the end. Consequently, workers can focus on analyzing and handling issues in real-time, as soon as the system flags them.

For effective and efficient review by exception, manufacturers benefit from digitizing the entire production process. By implementing a digital solution across every step of production, manufacturers are able to collect comprehensive production data, enabling seamless integration between operations and quality control processes.

Additionally, digital review by exception tools log and record all necessary quality event details, often furnishing the record with severity levels and associated resolution processes. The system makes this easier with interactive work instructions and automated reports.

Taking a digital approach to quality assurance provides pharmaceutical manufacturers with a number of significant benefits, including:

Eliminating human errors in production: Manual, paper-based document management presents several opportunities for human error. These errors interfere with the quality assurance and control process because teams are then forced to work with faulty, inaccurate data. As a result, it can be challenging to pinpoint the origin of the errors, making review and resolution more time-consuming and difficult to drill down.

Using an integrated, digital platform enables businesses to leverage data collected from digital work instructions, machines, and IoT devices to eliminate the need for manual data entry and prevent human error from clouding the quality report.

Quicker and easier review of exceptions: Using a digital solution for review by exception helps to identify non-conforming parameters and devations in real-time. This allows quality teams to respond to issues more quickly, ensuring that the production run isn’t affected by these quality events down the line.

Additionally, the system can help supervisors understand each exception’s severity, allowing timely intervention during production.

Faster time to market: Reviewing exceptions using paper-based methods creates a bottleneck at the end of the production run, increasing time-to-market. As a result, order fulfillment is delayed, negatively impacting the business’s revenue.

Digitizing review by exception allows manufacturers to catch issues before they progress. Timely intervention ensures that operators can focus on production processes, ensuring quicker time-to-market, and more reliable product fulfillment.

Review by Exception isn’t something you turn on and walk away from. It’s a change in how quality is managed, and in a regulated environment, that change carries weight.

Here’s what tends to cause problems and how experienced teams stay ahead of them.

1. Exception logic that’s incomplete or too rigid
If the logic isn’t right, the system either flags everything or nothing.
Too tight, and it misses real issues.
Too loose, and QA ends up buried in low-risk noise.
Once that happens, people stop trusting the data, and RBE loses its value.

How teams handle it: Bring QA, ops, and IT together early to define what actually counts as an exception. Use past batch data to test the logic before rollout. Then treat it like a living system, review and tune it as production evolves.

2. False positives that wear people down
Early setups often catch “exceptions” that aren’t problems at all like time sync glitches, late clicks, formatting quirks. When that keeps happening, people start ignoring alerts.

How teams handle it: Most start with a “shadow mode.” The system flags exceptions, but QA still does a full review. After a few runs, you can compare what the system caught versus what QA found and adjust from there.

3. Uneven training and unclear ownership
Operators, supervisors, and QA all touch the process differently. If everyone’s guessing about what triggers an exception or who should act, you’ll get delays and inconsistent handling.

How teams handle it: Train for understanding, not just compliance. Walk through real examples with each group. Make sure escalation paths are documented and visible.

4. Compliance and validation hurdles
Digital RBE still lives under the same regulations as manual review i.e. traceability, audit trails, signatures, validation. The setup itself can be a project if you’re working with a system not built for GxP.

How teams handle it: Use platforms designed for regulated work. Tulip’s validated cloud, e-signatures, audit logging, and documentation aligned with GAMP 5 make compliance less of a side project and more of a built-in function.

5. Cultural resistance inside quality teams
The hardest part isn’t the tech, it’s trust. QA people are trained to catch everything. Handing some of that control to automation feels risky.

How teams handle it: Start small. Run pilots. Compare manual and RBE results side by side. Let QA help shape the logic and thresholds so they own the process, not just audit it later.

RBE works when it’s built on sound logic, validated systems, and trust between people and technology. Skip any of those, and you’re back where you started—slower reviews and uneasy teams.

Review by Exception isn’t the end goal. It’s a step toward faster, more autonomous quality systems that can handle complexity without losing control.

Here’s where things are moving.

AI will assist review, not replace it
Right now, RBE logic is rule-based. It spots what breaks a limit and flags it. Useful, but narrow.

The next wave adds pattern recognition like machine learning trained on years of batch data that can catch drift or odd combinations before they turn into exceptions. You might not see a single out-of-spec value, but the model knows something looks off.

Picture a system that doesn’t just say, “Temperature out of range,” but “This batch behaves like one that led to a CAPA six months ago.”

That’s already starting to happen inside Tulip apps, where AI looks across multiple data points at once, not one field at a time. It’s early, but the direction is clear: humans stay in charge, AI helps them see sooner.

Real-time release is getting closer
As confidence in digital records grows, regulators are warming up to real-time release, the idea that a product can be cleared automatically when all quality conditions are met.

The EMA and FDA have both indicated they’re open to it, as long as systems are validated, data integrity holds, and process controls can be proven.

Review by Exception is a proving ground for that model. It shows that digital review can be trusted, that system logic can be validated, and that automation can support and not replace human oversight.

RBE as a sign of digital maturity
Some see RBE as a feature. It’s really a maturity marker.

Early-stage operations can’t run exception logic because their data lives on paper or in unstructured forms. Once that data becomes digital i.e.captured in guided workflows, linked to machines, tied to e-signatures exception logic just becomes possible.

And once it’s possible, the path opens up fast:

Structured data →
Exception logic →
AI-supported insight →
Autonomous release decisions

Tulip’s architecture supports that evolution. Teams can start small by basic rule-based detection and build toward predictive models without swapping out systems or revalidating everything from scratch.

The takeaway: Review by Exception isn’t the future of QA. It’s the bridge to it.

Currently, 7 of the top 10 life science companies are leveraging Tulip’s platform in some capacity to help digitize their production processes.

Using Tulip, pharmaceutical manufacturers are able to digitize their standard operating procedures, guiding workers through every step of the production process. As a result, businesses can ensure that the batch was produced in accordance with the established SOPs.

Additionally, Tulip enables operators to conduct in-line quality checks, ensuring that products meet the necessary specifications and are produced under the right conditions.

Should any deviation or quality events occur, they are immediately logged and can be responded to in real-time.

If you’re interested in seeing how Tulip’s operations platform can help you streamline review by exception processes, reach out to a member of our team today!

Review by Exception helps manufacturers move faster by putting QA attention where it counts. Instead of checking every line of every record, teams focus on the few exceptions that actually need review. The payoff shows up in shorter release cycles, fewer delays, and QA capacity that goes back into real quality work.

Tulip makes that shift easier to pull off. Its validated, no-code platform connects with your existing systems and lets you build exception logic without heavy IT effort. You can start small, prove the value, and expand as your processes mature, all within the same environment.