Why Pharma 4.0 Hasn’t Solved Batch Release — And Why Release Readiness Is the Missing Layer.

The Persistent Frustration Nobody Names

Production in the pharmaceutical industry is becoming increasingly automated, data collection has become more comprehensive, deviations are identified earlier, and electronic batch records have replaced most paper documents. In certain stages, the amount of verification work has been significantly reduced—sometimes by 40–60%—thanks to automated checks and EBR. Yet the overall release time for many products is still measured in weeks, and as industry shows, often extending into multiple weeks for sterile injectable products.
This is the paradox we live with: manufacturing moves faster due to automation and digitisation, but release does not
 
This scenario is familiar to many: the batch is technically ready, production operations have been completed, the systems show that the execution was successful, but the batch cannot be released. It is in an intermediate state: produced, tested, partially documented, but still not ready for release – for legal reasons.

Another familiar situation is when everything looks fine until it is time for verification.
There are no critical alarms during execution. Process control indicators are within normal limits, deviations are insignificant or understandable, and documentation appears to be almost complete.
And only during the official batch inspection does the invisible become apparent: missing signatures, unresolved links between deviations, belated explanations or evidence that exists but not in the form required for release. What seemed stable during execution becomes fragile in retrospective analysis.
At one sterile injectable manufacturing facility, the implementation of electronic batch recording reduced compliance verification time from several days to less than 24 hours.
However, the quality control department still spent most of that time resolving exceptions and discrepancies between the EBR, LIMS, and QMS systems before product release could be considered. The process chain works as intended; the bottleneck has simply shifted.

—- The bottleneck is the result of pressure —

And that pressure is now concentrated on the quality control department and skilled professionals. Decisions that carry personal legal responsibility must now be made quickly, as batch production times have accelerated. Only the ability to influence conditions in the early stages of the manufacturing process remains limited.
The closer we get to the decision to release a product, the less room there is for uncertainty. This concentration of risk is not theoretical; it defines the daily work of CDMOs and complex manufacturing organisations, where limited capacity and a shortage of specialists exacerbate delays.
This tension is not due to a lack of effort, knowledge or experience. Quality assurance teams and quality professionals are more experienced, better trained and receive more digital support than ever before. The discipline of verification has strengthened, not weakened.
It is not the result of outdated quality assurance methods. Modern quality systems are validated, integrated, and compliant with current regulatory requirements.
This tension is most acute in organisations that have invested heavily in developing Pharma 4.0 capabilities. Organisations that have invested deeply in Pharma 4.0 now see the data more holistically—both its value and its limitations. Multiple industry analyses indicate that data integrity remains one of the most frequently cited themes in FDA warning letters in recent years.

This problem is structural in nature. We have improved our tools and increased the speed at which tasks are performed, while the volume of data has increased significantly. The essence of batch release remains the same. Release is still retrospective; it is a legal act based on evidence and performed under personal responsibility. With the growth of automation, the burden of proof does not decrease, but becomes more fragmented.
We need a new way to continuously collect, maintain, and understand the evidence package as it evolves, rather than gathering and reconciling it at the very end. Many organisations intuitively sense this gap, even if they don’t have a precise term to describe it.
This article draws on that shared experience. The aim of the article is not to assign blame or question the fundamentals of GMP, but to identify a paradox that has quietly emerged against the backdrop of digital transformation and explore why addressing release readiness as a separate issue should be the next necessary step.

What Pharma 4.0 Actually Solved—and What It Didn’t

Pharma 4.0 has changed the entire approach to batch execution and monitoring.

What Pharma 4.0 genuinely improved:

The 2025 ISPE survey showed that companies that implemented Pharma 4.0 achieved an average 25% increase in OEE and a 40% reduction in cycle time from setup to final packaging.
However, all these achievements stop at the batch release stage. Pharma 4.0 has accelerated execution, but it has not revised — and could not — revise and redefine batch release.
 

What it did not change:

Let’s consider a typical batch of sterile injectables. MES completion is 24 hours with full parametric data. EBR verification is now 18 hours. The deviation management system signals a minor IPC deviation during filling.
But what about release? Still more than 20 days away. Why?

The QP is waiting for consolidated lab data (3 days), closure of the deviation with root cause (5 days), cross-system reconciliation (4 days), and customer approval (7 days) — none of the execution tools can shorten these lead times.
Pharma 4.0 has optimised what happens during production. Batch release is an area of legal evidence. Speed of execution conflicts with the need to gather evidence. Large volumes of fragmented data face the challenge of interpretation and reconciliation. And that cannot be automated.
No control panel redefines QP responsibility. No AI predicts the entire deviation closure chain. No EBR automatically generates control logs compliant with Appendix 11 across LIMS-QMS-MES boundaries without validation risk.

Pharma 4.0 has achieved impressive success where possible: reducing execution time from weeks to days. At the same time, it has identified batch release as a separate issue that requires an explicit approach as a continuous state of evidence rather than a final checklist.

Batch release occupies a fundamentally different position in pharmaceutical manufacturing than any other activity affected by Pharma 4.0. Although it is the final stage, in regulatory and legal reality it is a certified legal act in accordance with Chapter 5 of EU GMP and Part 211 of 21 CFR.

It is this difference that explains why batch release has remained unchanged.

Release Is a Legal Act, Not a Process Step

So, batch release is a legal act performed under the personal responsibility of a qualified person (QP). It cannot be delegated to a system, distributed among functions, or partially automated without changing its regulatory nature.
It is defined by three conscious characteristics:
Personal responsibility: decisions are made by specific qualified specialists who confirm compliance with requirements. Systems provide evidence, but responsibility cannot be delegated.
Retrospective analysis: the release evaluates completed performance after the fact. Unlike real-time process control, it cannot influence the results but only evaluates them after the fact.
Irreversibility: Once certified, a release cannot be cancelled without revocation. In the presence of fragmentary evidence, this leads to slow and deliberate final decision-making.
These are the fundamentals of GMP, not shortcomings.

Release Is Evidence-Based, Not Event-Based

Manufacturing is event-driven: dispense materials, run processes, take samples, generate results. Release is evidence-based: it hinges on a complete, coherent body of proof demonstrating compliance across GMP domains.

Why Automation Increases the Proof Burden

So, what do we have as a result:

Key takeaway: the more automated the production, the more vulnerable the checks are at later stages. This points to a mismatch — the legal, retrospective logic of evidence is not satisfied by tools optimised for events. Release requires explicit, continuous governance, not just automation.

The Hidden Conflation: “Release” vs. “Readiness”

One reason why the bottleneck in the batch release process has been so resistant to improvement is the subtle but pervasive confusion between the concepts of ‘release’ and ‘readiness.’ Although these two concepts are related, they are not identical, and their interchangeable use makes it difficult to understand the essence of the problem.
In essence, the difference between them is simple.
Release is a legal decision. It is made at a specific point in time, under personal responsibility, and completes the production cycle of a batch.

Readiness is a state that develops over time. It reflects how complete, consistent, and well-founded the evidence package is at a given point in time.

Release happens once. Readiness exists continuously.
Most production and quality control systems were not designed with this difference in mind. Before production was automated, readiness for release was based on the following assumptions:
First, it is assumed that readiness can always be assessed at the end. Evidence is collected during execution, but its adequacy for release is only assessed after the batch is complete. Until that point, readiness is mostly assumed rather than explicitly assessed.
Second, it was assumed that missing elements were exceptions. Gaps in documentation, expected results, or unresolved links are viewed as deviations from the norm rather than a natural consequence of accumulating evidence over time.
Third, deviations are viewed as isolated events rather than conditions that change the state of readiness. A deviation is recorded, investigated, and closed, but its broader impact on the overall readiness of the batch is not apparent until late in the process.
These assumptions have determined how systems are designed and how teams operate.

Why This Breaks at Scale
As the manufacturing environment becomes more complex, these assumptions begin to lose their relevance.
In multi-client CDMO operations, readiness is influenced by specific client requirements, verification expectations, and release dates. What is ‘complete’ for one client may not be sufficient for another, even within the same facility.
In this type of manufacturing, batches are run one after another, often using shared equipment and overlapping operations. Problems discovered late in one batch can affect the planning and readiness of subsequent batches, amplifying the consequences of late detection.
The process structure may remain stable, but constraints, checks, and documentation requirements vary depending on the recipe or customer requirements. This increases the number of contingent obligations that must be fulfilled, although their fulfilment occurs without explicit tracking.
Hybrid documentation systems, in which paper, scanned records and electronic systems coexist, make it difficult to form a single, coherent picture of readiness at any given point in time. Evidence may exist, but not always where it is expected to be found during an audit.
In such circumstances, readiness can no longer be determined with certainty at the end. This is because readiness (of a particular batch) is a dynamic state of that batch, which can change as evidence emerges, is verified and resolved.

A Missing Layer
The result of this confusion of concepts (release and readiness for release) is that organisations continue to view readiness as a by-product of execution rather than as a state that develops in parallel with the production process and therefore requires separate attention. Release remains a moment; readiness remains implicit.
What is lacking is not another execution system or another data store, but a way to explicitly track and maintain readiness (as a separate process) as it evolves, so that the final decision to release is made based on a known state rather than a discovered state.
This distinction—between release as a decision and readiness as an evolving state—is the basis for rethinking how batch release can be supported without revising its legal or regulatory nature.

Release Readiness as a Continuous State

Recognising readiness for release as a separate condition or state does not mean revising the definition of batch release or weakening GMP safety measures. Rather, it is a way to make existing safety measures more visible and consistent without changing authority or decision-making rights.
We suggest viewing readiness for release as a continuous process. However, this does not imply any of the following:

No real-time release
Continuous readiness does not mean automatic release of batches immediately after data becomes available. The legal act of release remains discrete, retrospective, and performed by a human.

No autonomous decisions
The system does not make any decisions. Readiness status does not mean approval, rejection or withholding of a batch.

No artificial intelligence
Continuous readiness is not based on probabilistic models, predictions or machine judgements. It is based on explicit, predefined commitments and recorded evidence.

No replacement of QA or qualified person
The role, responsibility, and legal accountability of QA and QP remain unchanged. Continuous readiness exists to support human judgement, not to replace it.

These exceptions are fundamental. Without them, the concept would be incompatible with current regulatory practice.

What “Continuous” Does Mean

Let us determine how understanding continuity will help assess readiness rather than release.

1. Explicit obligations
Readiness for release is determined in terms of explicit obligations that must be fulfilled: completion of documentation, in-process control, handling of deviations, and quality evidence. These obligations already exist in GMP; the difference is that they are explicit rather than implied.

2. Parallel assessment
Commitments are assessed in parallel rather than sequentially. Documentation, IPC, deviations, and quality evidence move independently and converge only where necessary. This reflects how evidence accumulates in practice.

3. Early identification of obstacles
When a obligation becomes unfulfilled or blocked, for example due to a missing record, a result outside acceptable limits, or an unresolved deviation, its impact on readiness becomes visible immediately, rather than several weeks later during batch release.

4. Deterministic, explainable status
At any given moment, readiness can be described using a small set of clear states (e.g., not ready, conditionally ready, blocked, ready). Each state can be explained using specific evidence and commitments, without interpretation or inference.
This approach does not create new requirements but organises existing ones to reflect their consequences in real time.

Why This Matters in Practice

When readiness is treated as implicit, teams discover problems late, under pressure, and with limited options to influence outcomes. When readiness is treated as a continuous state, problems surface while there is still time to respond.
Importantly, this does not eliminate uncertainty. It changes when uncertainty is encountered—earlier in the lifecycle, when it is easier to address, rather than at the moment of release, when it is hardest.

A Different Role for Systems

In this framing, systems are not asked to make decisions. They are asked to reveal the current state of readiness, based on what is already known. This supports better planning, clearer prioritisation, and more defensible final decisions—without accelerating or automating release itself.

Readiness does not decide. It reveals.

By making readiness explicit, continuous, and explainable, organisations can reduce the fragility of late-stage review while preserving the legal and regulatory integrity of batch release.

Why This Matters Specifically for CDMOs

For contract development and manufacturing organisations (CDMOs), the difference between release and readiness is not abstract — it is an operational fault line. The structural realities of CDMOs amplify the weaknesses of late-stage validation, making explicit readiness too costly to ignore against the backdrop of a market growing to €258 billion in 2025.

Multi-client release pressure is the first and most obvious factor. CDMOs operate under multiple quality agreements, release expectations, and documentation standards simultaneously. A batch that appears complete from an internal perspective may still be considered incomplete by a client’s QA or QP. When readiness is implicit rather than explicit, these differences surface late, at the point of release, when there is little room to adapt.
At the same time, QA and QP resources are shared across products, clients, and campaigns. Unlike vertically integrated manufacturers, CDMOs cannot dedicate release resources to a single value stream. Release delays in one batch cascade into others, concentrating pressure on a small number of individuals whose decisions carry legal responsibility across the portfolio.

Client-driven timelines further intensify this pressure. Release dates are often commercially committed before manufacturing begins. When readiness issues emerge late, the organisation faces a trade-off between schedule pressure and review thoroughness—precisely the situation GMP is designed to avoid. Without early visibility into readiness, this trade-off is discovered too late.
CDMOs are also exposed to heightened inspection risk. Inspectors do not evaluate CDMOs batch by batch; they assess patterns. Repeated late deviations, extended batch holds, or inconsistent release timelines raise questions about system adequacy, even when individual batches are ultimately compliant. The issue is not non-compliance, but fragility under scrutiny.
Finally, there is reputation risk. For CDMOs, release reliability is not just a quality attribute; it is a commercial differentiator. Clients remember delays, unclear explanations, and last-minute surprises long after individual investigations are closed.

The Economic Consequences of Implicit Readiness

These structural pressures translate directly into operational cost.

Release latency
When readiness is assessed only at the end, batches spend extended time in a “manufactured but unreleased” state. Even small delays accumulate across portfolios and campaigns.

QA workload concentration
When readiness is implicit, review effort collapses into narrow release windows. Peaks of review activity increase error risk, prolong decision cycles, and contribute to staff fatigue in roles that are already difficult to backfill.

Working capital lock-up
Unreleased finished goods represent immobilised capital. For CDMOs, this includes not only inventory value, but delayed invoicing and reduced manufacturing flexibility for subsequent campaigns.

Investigation expansion
Late discovery of readiness-relevant issues broadens investigations. What could have been a narrow correction becomes a multi-week review exercise, consuming QA capacity and extending batch holds.

From Concept to Pain

The cumulative effect is not inefficiency in execution, but uncertainty at the moment that matters most. CDMOs do not struggle because they lack data or systems; they struggle because readiness remains invisible until it is tested under legal and commercial pressure.
Making release readiness explicit does not remove these pressures, but it changes how they are managed. It allows organisations to encounter issues earlier, distribute effort more evenly, and approach release as a confirmation of a known state rather than a discovery exercise.