Capture
Collect product files, customer POs, CPQ outputs, schedules, ASNs, certificates, inspection records and shipment evidence from connected repositories.
MTS/CTO manufacturing case model
Product data, configured order and traceability control across manufacturing systems
A practical model for a mid-size make-to-stock or configure-to-order manufacturer working across ERP, PIM, CPQ, QMS, MES, WMS, supplier portals, customer portals and Microsoft 365.
The case is anonymized and modelled. It shows the workflow shape, review gates and measurement targets AI Beaver would validate during an audit and prototype.
30-75%
Target reduction across selected document-control workflows
Before state
Document and data control breaks across system boundaries
The pain is not one bad system. It is the repeated manual reconciliation between systems of record, working folders, supplier documents and customer commitments.
| Area | Manual problem |
|---|---|
| Product data | SKU attributes, pack sizes, labels and substitutions are maintained across ERP, PIM, spreadsheets and supplier files. |
| Order checks | Customer POs and configured orders are compared manually against quotes, CPQ outputs, stock rules and customer-specific document requirements. |
| Supplier evidence | ASNs, delivery notes, certificates and shortage messages are chased through email and portals before goods-in or production use. |
| Traceability | Quality teams reconstruct lots, serials, inspection results, NCRs, CAPAs and shipment evidence manually when a problem appears. |
Controlled AI workflow model
Match
Match documents to SKU, customer part number, supplier part, lot, batch, serial, PO, sales order, production order and shipment references.
Validate
Check configuration rules, product master fields, obsolete revisions, missing supplier evidence, inspection gaps and affected inventory.
Review
Route exceptions to customer service, planning, procurement, quality, engineering or dispatch with source links and proposed updates.
Update
Prepare reviewed update files for ERP, PIM, CPQ, QMS, WMS or dashboard workflows once ownership and approval rules are proven.
Review gates
Control matters more than full automation
This case model assumes AI prepares evidence, comparisons, exception queues and update files. It does not approve production release, quality disposition, customer commitments or stock movements.
- No direct release of product master, configuration, quality or shipment records without agreed review gates.
- Every extracted field that affects stock, customer commitment, quality or dispatch keeps a source reference.
- Low-confidence, conflicting, missing, obsolete or high-value evidence is routed to a named owner.
- Approved updates are staged before ERP, PIM, CPQ, QMS, WMS or customer-facing records change.
- Correction patterns are monitored so prompts, extraction rules and validation thresholds can be improved safely.
Measurement targets
What the prototype should prove before rollout
Targets are workflow assumptions, not guarantees. The audit should validate baseline volume, handling time, exception rates, reviewer effort and integration limits before production build.
| Workflow area | Target range |
|---|---|
| Product-data cleanup | 40-75% less manual re-keying on suitable product-data updates |
| Configured order review | 35-65% faster preparation for PO and configuration checks |
| Supplier document chasing | 30-60% reduction in acknowledgement, ASN and certificate admin |
| Traceability evidence packs | 30-55% faster quality evidence gathering and reconstruction |
| Document exception search | 35-60% less time spent locating status and source evidence |
Model this against your manufacturing documents
Start with one controlled workflow, measure the baseline, then test extraction, validation and review gates on real files.