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Case Study

From 14 Days to 5: Rebuilding Batch Release with eBMR

Discover how a pharmaceutical manufacturer transformed its batch release process using Nexgensis eBMR, reducing release timelines through digital workflows, automated approvals, and real-time production data.

Nexgensis Technologies 26 February 2026 8 min read

Nexgensis Knowledge Center

From 14 Days to 5: Rebuilding Batch Release with eBMR

A top-10 formulation manufacturer operating four sites was releasing finished batches in an average of fourteen calendar days. Manufacturing was not the constraint. The constraint was a paper batch record that travelled physically between production, laboratory and quality assurance, accumulating queries at every desk.

14 → 5

days average batch release

-82%

documentation errors at first review

-64%

QA review hours per batch

4

sites live within nine months

The starting position

Each batch generated roughly 180 pages of executed record. Quality assurance reviewed all of it, line by line, because there was no reliable way to know which entries deserved attention. Around one in three batches was returned to production for a correction, and each return cost between one and three days.

  • Paper batch records physically routed across three departments.
  • Laboratory results transcribed manually into the release package.
  • No visibility of where a batch record sat at any given moment.
  • Deviations raised days after the event that triggered them.

Designing for review by exception

The programme did not begin with software configuration. It began with a line-by-line assessment of the batch record to classify every entry as critical, supporting or informational. Only critical entries would require review; everything else would be reviewed only when the system flagged a deviation from its specification.

Redesigned release flow

1

Execute

Operators record steps on the line with in-context guidance and forced sequencing.

2

Validate

Every entry is checked against specification at the moment of capture.

3

Flag

Out-of-specification and out-of-sequence events raise an exception automatically.

4

Review

QA reviews exceptions and critical steps rather than the full record.

5

Release

Electronic signature completes release with a full, immutable audit trail.

Implementation approach

  1. 1Digitised two representative products first, covering the widest range of process steps.
  2. 2Ran paper and electronic records in parallel for two full campaigns.
  3. 3Integrated LIMS results directly into the release package, removing transcription.
  4. 4Trained operators on the line rather than in a classroom, using their own products.
  5. 5Rolled out site by site with a shared master template and local variants controlled centrally.

Results after twelve months

MeasureBeforeAfter
Average release time14 days5 days
Batches returned for correction31%4%
QA review hours per batch11.54.1
Deviations raised within shift22%91%
Right-first-time documentation69%98%
We stopped reading batch records and started reading exceptions. That single change gave us back nine days.
Head of Quality Assurance

What made it work

  • Executive sponsorship that protected the parallel-run period from schedule pressure.
  • Operator involvement in template design, which removed resistance at go-live.
  • A single master template preventing site-level divergence from returning.
  • Integration with LIMS and equipment data so the record assembled itself.

Key takeaways

  • Batch release delay is usually a review problem, not a manufacturing problem.
  • Review by exception only works when exceptions are generated by the system, not found by people.
  • Digitising the batch record first exposes process variation that paper had been hiding.
  • Parallel running for two campaigns built inspector and operator confidence before cutover.
  • Cycle time reduction released working capital without adding QA headcount.

Want to see how this works in your organization?

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Whether you are evaluating eQMS, LIMS, ELN, eBMR, DMS, APQR, Asset Management, eLogs or AI Analytics, our team can provide a personalized demonstration tailored to your business needs.

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