The Hidden Cost of a Deviation
A simple missing signature. Benign at first glance. Catastrophic in consequence.
A batch is finished: hundreds of thousands of pounds’ worth of material, weeks of labour, late nights, and tight timelines poured into it. Documentation is in final QA review, ready for release to the patient. Then it’s spotted - a missing signature. And everything stops.
That missing signature can mean one of two things:
Someone simply forgot to sign, or
Someone forgot to perform a mission-critical step.
That step could be confirmation of sterility, or the addition of a critical reagent and in biomanufacturing, if it isn’t documented, it didn’t happen. Regulators don’t accept excuses; they require written evidence.
The potentially life-saving product, worth hundreds of thousands, is now quarantined while the deviation is investigated. Logs are reviewed, operators interviewed, and root cause analyses undertaken. If no definitive proof is found that the task was completed exactly as required, the batch can’t be released.
In biomanufacturing, especially in cell and gene therapy, that’s fatal. These are living, patient-specific therapies that can’t be reworked or remanufactured.
The Toyota Lesson: Build Quality In
Like biomanufacturing, small errors in car manufacturing can have catastrophic consequences.
In the mid-20th century, Toyota faced recurring assembly errors. The solution became the Toyota Production System (TPS), built on two principles: Jidoka (automation with a human touch) and Just-in-Time (minimising waste).
TPS guaranteed quality not by correcting mistakes after they occurred, but by designing out the possibility of error (poka-yoke). If a fault appears, production stops immediately until the issue is resolved, preventing downstream failures. This approach was highly successful and is now the benchmark for manufacturing across all industries.
Biomanufacturing needs to embrace the same mindset, but for data. Instead of nuts and bolts, we rely on signatures, digital entries, and sensor readings to prove every step happened correctly. A missing input is the biological equivalent of a missing screw in an engine: it might look fine from the outside, but it’s unsafe to release.
The problem, however, is that today’s production lines are still largely manual and paper-based, with siloed datasets reviewed retrospectively by different teams. There’s no automatic alarm to catch a missed check, no pre-emptive warning when a parameter drifts, no digital trail to determine the root cause. Humans are expected to operate flawlessly, under pressure, in gowns and gloves, across hundreds of pages of records and multidisciplinary teams. It’s no wonder deviations remain alarmingly common.
The True Cost of Non-Digital Manufacturing
Deviations aren’t just common, they’re also expensive. Estimates suggest that documentation errors and deviations can account for 15–20% of total manufacturing costs in ATMP production.
They inflate cost of goods, delay batch release, and limit scalability. For smaller biotech companies, a single critical deviation can erase a program’s profit margin - or worse, delay patient treatment.
There are also second and third-order consequences such as
Regulatory costs: Every deviation must be investigated, documented, and approved, often consuming a huge amount of staff hours and delaying other production.
Reputational and opportunity costs: Lost patient, customer and regulator trust, delayed trials, and lower throughput. All of which compounds the hidden financial and human impact.
If the ATMP industry wants to industrialise, it must solve these challenges.
Digital Jidoka: The Future of Quality
Just as Toyota embedded sensors and poka-yoke into every production station, digital biomanufacturing can now embed intelligence into every step.
The first step is data unification. Once manufacturing data (historical and real-time) is integrated into a single, standardised repository, Digital Jidoka becomes possible:
Automated prompts that prevent progression until required checks are completed.
Real-time anomaly detection that flags deviations as they emerge.
Predictive QC that identifies when an operator might miss a step or a parameter might be about to drift.
Autonomous root cause, CAPA, deviation, and report generation. Saving teams hours of manual work.
This is Digital Jidoka: building quality in, rather than inspecting it at the end.
It’s how biomanufacturing can finally become right first time, every time, with fewer deviations and faster releases.
First published on the Native Labs newsletter. Subscribe for new articles.
