Caught a recurring out-of-spec pattern that lived in three notebooks
A specialty beverage manufacturer's in-house QC team · 5 employees · ~450 samples/mo
Published
When a customer audit demanded six months of moisture and titration trends, this five-person beverage QC team spent three days compiling them from paper notebooks. Digitizing that history in Aliquora surfaced a supplier OOS pattern they had suspected but never been able to prove.
Challenge
Lab kept paper notebooks and emailed COAs as Word documents. When a customer audit asked for trends across six months, it took three days to compile. They suspected a moisture issue with one supplier but couldn't quantify it.
What they set up
Set up materials per supplier with separate spec ranges. Used CSV import to backfill the last six months of titrator and moisture meter data. Used the dashboard's OOS view to filter by supplier and material.
Results
3 days → 20 min
Audit prep time
Identified
Supplier-attributed OOS pattern
12 → 35
COAs per week
"The audit story alone paid for the year. Now we can answer 'show me everything from supplier X for Q2' in under a minute."
— Lab Director (composite, based on early-access feedback)
Implementation timeline
How the transition unfolded week by week.
- 11
Paper-to-CSV transcription
Week 1–2The team transcribed six months of notebook entries into a staging spreadsheet — one row per sample. This was the most labor-intensive part of the project but also the most revealing: they found 11 results that had been recorded twice with slightly different values, a systematic transcription issue that had never surfaced before.
- 22
Supplier and material setup
Week 2Configured materials by supplier so that each supplier's lots carried its own spec range. The moisture thresholds for two suppliers differed by 0.4% — a gap that had been easy to overlook in a shared notebook but became explicit when modeled as separate spec limits.
- 33
Historical data import
Day 10Imported six months of titrator and moisture meter data via CSV. Aliquora flagged 23 historical results as OOS on import — 8 of them from the same supplier, clustered in two three-week windows that matched a raw material lot changeover.
- 44
OOS pattern analysis
Week 3Used the OOS filter view to isolate all out-of-spec moisture results by supplier. The pattern was immediately visible: one supplier's moisture values were consistently 0.3–0.6% above spec during lot transitions. The lab director shared the filtered export with the supplier's quality team — a conversation they'd been trying to have for months without data to back it up.
- 55
Ongoing workflow & COA scale-up
Week 4 onwardSwitched daily result entry to Aliquora and started issuing COAs directly from the system. The jump from 12 to 35 COAs per week came partly from eliminating Word-template overhead and partly from taking on two new client accounts the team hadn't had capacity to service before.
What they'd do the same — and differently
Practical takeaways from the rollout.
-
Transcribing historical paper records is tedious, but it's where the value compounds fastest. The OOS pattern they found during backfill was the single most actionable output of the whole project.
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Separate spec limits per supplier is worth setting up even when the differences seem small. The 0.4% moisture gap between two suppliers became a live quality signal once it was modeled explicitly.
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COA throughput scales with workflow simplification, not headcount. Going from 12 to 35 COAs per week with the same five-person team was a workflow change, not a hiring decision.
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Build the supplier filter habit early. The first thing the lab director does each Monday morning is open the OOS view filtered by supplier — a five-second check that would have taken a half-day of notebook cross-referencing before.
See how it works for Food & Beverage
Food & Beverage Safety — features & workflowRelated reading
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