Lot, batch, serial and genealogy records
Classified, extracted and linked back to source evidence for reviewer control.
Production quality traceability automation
AI production quality traceability automation for MTS/CTO manufacturers
AI Beaver helps production, quality, supplier quality and operations teams link lot, batch, serial, inspection, NCR, CAPA and supplier evidence into reviewable traceability packs.
The workflow is built for manufacturers that need faster containment, root-cause analysis, audit evidence and customer-response packs when quality issues affect stocked or configured products.
30-55%
Target reduction in quality evidence gathering and traceability reconstruction
Document inputs
Real documents this workflow is built around
These are the source files AI Beaver expects to map during an audit and prototype. The implementation can start with a narrow subset, then expand as extraction quality and review rules are proven.
Inspection plans, inspection results and SPC exports
Classified, extracted and linked back to source evidence for reviewer control.
NCRs, concessions, deviations, CAPAs and 8D records
Classified, extracted and linked back to source evidence for reviewer control.
Supplier certificates, inspection reports and SCAR evidence
Classified, extracted and linked back to source evidence for reviewer control.
Production orders, travellers and digital work instructions
Classified, extracted and linked back to source evidence for reviewer control.
Customer complaints, returns and containment records
Classified, extracted and linked back to source evidence for reviewer control.
Manual bottlenecks
Why this workflow is a strong automation candidate
Step 1
Quality teams manually reconstruct which lots used which material, revision, supplier certificate and inspection plan.
Capture production, inspection, supplier, NCR, CAPA, shipment and complaint evidence from connected systems.
Step 2
NCRs, concessions, CAPAs, supplier evidence and customer complaints are not always linked to production and shipment records.
Extract SKU, lot, batch, serial, production order, supplier, revision, inspection and defect fields.
Step 3
Containment and root-cause analysis require searching MES, ERP, QMS, WMS, inboxes and spreadsheets.
Link quality evidence to production runs, material receipts, supplier certificates and shipped inventory.
Step 4
Audit and customer-response packs take longer when source evidence is spread across several systems.
Flag missing inspection evidence, unlinked NCRs, open CAPAs, affected lots and containment gaps.
Extraction and checks
Fields extracted and validation checks performed
The automation should produce reviewable data, not a black-box answer. Every important field or exception needs a source link, confidence signal and review route.
| Extracted fields | Validation checks |
|---|---|
| SKU, lot, batch, serial, production order and shipment reference | Lot, batch and serial matched across ERP, MES, WMS and QMS |
| Supplier, material receipt, certificate, batch and inspection reference | Supplier certificate linked to received material and production run |
| Drawing, document revision, work instruction and inspection-plan version | Inspection plan and document revision consistency |
| NCR, defect type, concession, CAPA, SCAR, 8D and containment status | Missing, failed or out-of-tolerance inspection evidence |
| Customer complaint, return, affected inventory and reviewer outcome | Open NCR, concession, CAPA, SCAR or 8D action detection |
Workflow outputs
What the implementation should produce
AI Beaver normally starts with a controlled workflow output: summaries, exception queues, review files, dashboards or proposed system updates. Direct writes into operating systems should be added only after review rules are proven.
- Traceability evidence pack
- Quality containment summary
- NCR, CAPA and SCAR linkage report
- Affected inventory and shipment list
- Audit or customer-response pack
FAQ
Common questions
Can traceability automation make quality disposition decisions?
No. The automation prepares linked evidence, exceptions and affected-stock views. Quality, operations or authorised managers retain disposition and customer-response decisions.
Can this work when evidence is split between ERP, MES and QMS?
Yes. That is the main use case. AI Beaver maps identifiers across systems, then builds source-linked review packs and exception queues where records do not align.
Assess this workflow using your real documents
Start with a focused audit of document types, source systems, manual checks, exception rules and review requirements.