Reducing Time for Batch Release

The time it takes to make and release a batch of product factors in raw material dispensing, processing time, packaging, testing, and transportation, which can take anywhere from 5 to 20 days depending on the complexity of a product. However, a significant portion of that span is dwell time, not time spent for work; the finished batch often sits in quarantine waiting for record review or while a deviation is being investigated. This time spent waiting ties up inventory and pushes back the date product reaches patients. An electronic batch record can be used to shorten waiting time and optimize the batch review process.

What’s in the Review

Under 21 CFR 211.192, the quality control unit must review and approve every batch production record before distribution, confirming that the batch followed approved procedures and met specifications. That review includes checks for documentation completeness, material traceability, step-by-step conformance to the master batch record (MBR), laboratory results against acceptance criteria, and yield reconciliation.

It helps to be precise about what a batch record is: the MBR is the pre-approved template that defines the process while the Batch Manufacturing Record or the Batch Production Record document are the actual execution against that template. For medical devices and combination products, the equivalent is the Medical Device File under ISO 13485 and 21 CFR 820/QMSR. Each of these is subject to the same underlying expectation: contemporaneous, attributable, and complete documentation consistent with ALCOA+ data integrity principles.

Where the Days Pile Up

Where delays often occur is during how most companies satisfy the expectation of batch review; a single batch record for a complex biologic or sterile injectable can run past hundreds of pages, meaning that the review can hold off product release if it takes too long. Often times, this is a problem of document flow and visibility, rather than issues with manufacturing, and using paper-based resources can significantly extend this delay.

Manual documentation: If operators hand-record every measurement and signature during production on paper, a single missing entry can return the record to the floor for additions, pausing production and having people run around in the meantime.

Timing: In paper or hybrid document workflows, deviations tend to be found late during final review, which means that they often add days or weeks to the release window due to the amount of time needed to investigate or resolve them.

Review sequencing: In the same vein that the batch release timing may be delayed from downstream deviations, issues are more likely to be found and corrected quicker when review runs step by step with production instead of in sequence after production.

What Review-by-Exception Changes

Review-by-exception moves quality review from exhaustive physical verification of every entry field to focused oversight. This is done by screening manufacturing and quality data so that only critical process exceptions are sent out for review. QA reviewers examine data from predefined parameters instead of giving equal weight to each entry, so that priority is given to data that is representative of product quality or other critical data types. This is a system of review that relies on a digital foundation to capture data as the batch executes and identifies data limits in the moment of action, rather than a static record examined weeks after production.

How ACE EBR Addresses Bottlenecks

Errors caught at entry, not at review. Conditional flagging can be used to enforce data limits in real time so when a critical parameter falls outside tolerance, ACE EBR triggers a batch stop, step stop, or warning flag and alerts the operator at the point of entry. Conditional batch steps also gate execution, allowing the batch to advance only once specifications and required sub-steps are met.

Execution paths that match the process. ACE EBR supports both sequential and parallel execution, along with control execution order within a batch plan. Sections can be organized into parallel or sequential phases, and batch plans also support table-based data entry for structured capture of more complex data.

Focused, exception-based review. Reviewers work by starting with records with flagged activities rather than reading every page of the batch record, and the entire document is backed by a complete audit trail. During review, if batch is put on hold or placed in quarantine while additional information is being gathered, the batch record can still be accessed so one open question doesn’t freeze the whole record. Reviewers can also create child records during the QA review phase to run follow-up investigations that are visible under the parent record.

“The steps that need the most careful attention find the reviewer automatically in ACE EBR. That’s the whole idea behind review by exception, and it’s one of the fastest ways we’ve seen teams cut down on batch record review time versus paper-based batch executions.”

-Ryan Dellinger, Product Manager II

Deviations connected to the quality system. Because ACE EBR operates on the same platform as the ACE eQMS, a deviation can be logged and resolved in place and linked to the associated CAPA and downstream records. When an OOS investigation requires batch record data, exception logs, and other batch information, these are available directly within the investigation record, so reviewers can quickly find production evidence without leaving the platform.

Control and accountability at the step level. Permission-defined roles shape who can view, edit, or approve batch record steps, and custom signing order sets approvals by role or by batch step. Every record carry 21 CFR Part 11–compliant electronic signatures, complete audit trails, and role-based permissions. ACE is also available across Windows, iOS, and Android, so operators can capture data on the production floor instead of transcribing it later.

The Bottom Line

Batch release runs long often due to extended batch review times. Paper and hybrid workflows make it worse by surfacing errors late, forcing page-by-page verification, and scattering deviation evidence across disconnected systems. An electronic batch record built for review-by-exception addresses this issue: data limits are enforced as the batch runs, review narrows to genuine exceptions, execution and review can happen in parallel, and investigation evidence stays connected, not only to the record but to the eQMS.