Q&A Case studyDigital Batch Records
Retiring Paper Batch Records Across a Commercial Biologics Campus
Takeda Osaka replaced paper batch records with an eBR platform for three commercial biologics products — and paid down years of review-by-exception debt.
Reduction in batch review lead time
85%
The practitioner
Kenji Takahashi
Director, MES & Digital Operations, Takeda
Interview by Mrudula Kulkarni
Managing Editor - Pharma Now
14 Jul 2026 · 10 min read
The challenge
Paper batch records at the Osaka campus were averaging 340 pages per lot and driving a QA review backlog of nearly two weeks at peak.
The approach
The team defined a lean eBR data model per product, ran a structured user acceptance testing programme with shop-floor operators, and…
The result
Review lead time dropped from thirteen days to under two, right-first-time climbed above 96 percent, and the last two regulatory…
The challenge
Paper batch records at the Osaka campus were averaging 340 pages per lot and driving a QA review backlog of nearly two weeks at peak. Right-first-time on batch records sat around 71 percent, and every audit surfaced observations tied to legibility, contemporaneity, or transcription errors.
The approach
The team defined a lean eBR data model per product, ran a structured user acceptance testing programme with shop-floor operators, and staged the rollout product-by-product. A change management workstream ran in parallel, retraining QA reviewers on review-by-exception and rewiring downstream release workflows.
The result
Review lead time dropped from thirteen days to under two, right-first-time climbed above 96 percent, and the last two regulatory inspections closed with no data integrity observations. The eBR platform is now being extended to two additional commercial products.
Q
How did you get operator buy-in on eBR?
We ran user acceptance testing with actual shop-floor operators — not managers or IT — and iterated the UI three times based on their feedback.
The unlock was making the eBR faster than paper for the common case. Operators stopped seeing it as a compliance burden once they realised it saved them 40 minutes per lot on data entry alone.
Q
What was the hardest technical challenge?
Modelling the review-by-exception logic. Paper batch records are reviewed page-by-page; eBR only surfaces the exceptions, which means the system has to know what "normal" looks like for every parameter.
We spent three months building the exception library with QA reviewers before the first product went live. Every subsequent product plugged into the same library.
Q
What changed for QA reviewers?
The job shifted from checking every entry to investigating exceptions. That required a real retraining programme — QA reviewers had built decades of muscle memory around page-by-page verification.
We paired every reviewer with a senior colleague for their first month on eBR. By month two, review times had collapsed and everyone was confident in the new workflow.
A 340-page paper record is not a document. It's a two-week bottleneck disguised as compliance.
Kenji Takahashi, Director MES & Digital Operations
Measured impact
85%
Reduction in batch review lead time
96%
Right-first-time on batch records
0
Data integrity observations at last two inspections
The method, in brief
1
Define a lean eBR data model per product — Started with the leanest possible data model. Every field had to earn its place — if a paper section wasn't adding review value, it didn't make the eBR.
2
Iterate the UI with shop-floor operators — Three rounds of user acceptance testing with actual operators before the first product went live. Every round produced concrete UI changes.
3
Build the review-by-exception library first — Three months building the exception library with QA reviewers before flipping the switch. Every product going forward reuses the same library.
4
Stage the rollout product-by-product — One commercial product per wave, three months apart. Each wave fed lessons into the next. First three products live in nine months.
5
Retrain QA reviewers on exception investigation — Paired every reviewer with a senior colleague for their first month on eBR. By month two, review times had dropped and confidence was uniform across the QA team.
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