AI can identify likely scrap drivers, but it should not be the approval authority for process changes.
In practice, process changes should be approved through your existing change control process by the functions already accountable for product quality, process capability, and controlled execution. That usually means process or manufacturing engineering, quality, and the operational owner of the process. Depending on the change, additional review may be required from validation, maintenance, metrology, IT or OT, training, document control, supply chain, or program leadership.
Process or manufacturing engineering: owns the technical rationale, proposed parameter changes, tooling changes, sequence changes, or work instruction updates.
Quality: reviews effect on product characteristics, inspection strategy, control plans, nonconformance risk, and evidence requirements.
Operations or production leadership: confirms the change is executable on the floor with available staffing, cycle time, equipment constraints, and downtime windows.
Validation or compliance stakeholders: required when the change affects validated systems, qualified processes, electronic records, or traceability expectations.
Other approvers as needed: maintenance for equipment settings, metrology for measurement implications, document control for revision release, training for operator readiness, and IT or OT for system changes and data flows.
The exact approval matrix depends on your procedures, product risk, customer requirements, and whether the affected process is special, qualified, automated, or tightly linked to as-built traceability.
AI can support prioritization and root cause investigation. It can suggest that scrap correlates with a machine state, operator sequence, supplier lot, environmental condition, routing branch, or inspection pattern. That is not the same as proving causation or authorizing a process adjustment.
Before approval, the organization still needs to verify that the signal is real, that data quality is sufficient, that the recommendation is technically plausible, and that the proposed change will not create a larger quality, throughput, or traceability problem elsewhere.
Common failure modes include incomplete genealogy, bad timestamp alignment across systems, inconsistent reason codes, unmodeled operator workarounds, small sample bias, and models that perform well historically but degrade after process drift or supplier changes.
Whether the AI finding is correlation, causal evidence, or only a lead for investigation.
Whether the scrap signal is based on trustworthy and reconciled data from MES, ERP, QMS, historians, inspection systems, or manual logs.
Whether the proposed change affects controlled documents, routings, recipes, limits, inspection steps, training records, or supplier instructions.
Whether the change requires testing, pilot runs, revalidation, or formal risk review.
Whether expected scrap reduction is worth the operational disruption, qualification burden, and implementation risk.
In brownfield plants, approval is often slower because the change touches multiple systems and owners. A scrap driver discovered in analytics may map back to recipe parameters in a PLC or SCADA layer, routing logic in MES, master data in ERP, inspection plans in QMS, and operator instructions in a separate document system. If those systems are loosely integrated, the review has to confirm consistency across all of them.
This is also why full replacement is rarely the practical answer. Replacing MES, ERP, QMS, or machine integrations just to operationalize AI recommendations usually fails in regulated, long-lifecycle environments because of qualification burden, validation cost, downtime risk, integration complexity, and the need to preserve traceability and controlled change histories.
If the AI-discovered scrap driver would change how the product is made, inspected, recorded, or released, it belongs in formal change control with accountable human approval. If it only changes investigative priority, dashboarding, or monitoring thresholds, approval may be lighter, but it still should follow your site’s governance for analytics and production decision support.
Whether you're managing 1 site or 100, C-981 adapts to your environment and scales with your needs—without the complexity of traditional systems.
