Engineering Change Order

Engineering changes happen informally. An engineer walks to the shop floor and tells the operator 'use the new drawing, we changed the hole pattern.' There is no change documentation. When something goes wrong three months later, nobody can reconstruct what changed, when, or why. The 'change process' is a conversation.

AI cannot track, analyze, or predict engineering change impacts because no change documentation exists in any form.

Change requests exist as emails or informal forms. Some engineers write detailed change descriptions; others write 'updated drawing per customer request.' Impact assessments are verbal — someone asks 'will this affect anything?' in a meeting and accepts the answer. Change history is scattered across email threads and personal files. Reconstructing 'what changed in Q3' requires interviewing multiple people.

AI could mine email archives for change-related communications but cannot reliably extract change scope, impact, or approval status because the information is unstructured and incomplete.

A standard ECO form is used consistently. Every change has a form with affected parts, change description, reason, impact assessment, and approval signatures. ECOs are stored in a shared location (file server or basic PLM). Engineers can find the ECO for any past change. But impact assessments are narrative — 'minor cost impact expected' — rather than quantified. Implementation tracking ('has this change been applied in production?') is manual.

AI can search and classify ECOs, identify change patterns (which products change most, what types of changes dominate), and generate change activity reports. Cannot perform automated impact analysis because impact assessments are narrative rather than structured.

ECOs are managed in PLM with complete, structured data. Each ECO has quantified impact assessments — cost delta, schedule impact, tooling requirements, inventory disposition. Affected parts link to the BOM. Approval routing follows defined rules based on change type and impact magnitude. Implementation status is tracked and linked to production order effectivity. An engineer can query 'show me all ECOs in the last year that impacted tooling cost by more than $10K' and get a reliable answer.

AI can predict change cost and schedule impacts based on historical patterns, identify conflicting changes, recommend approval routing based on change characteristics, and flag ECOs that are stalled in the process. Can correlate changes with downstream quality outcomes.

ECOs are schema-driven entities with formal relationships to every affected aspect of the product lifecycle. An AI agent can query 'what is the total cost, schedule, inventory, and quality impact of implementing ECO-2847 across all affected products, suppliers, and production lines?' and get a comprehensive, structured answer. Change impact models include probabilistic risk assessments based on historical change outcome data.

AI can perform fully autonomous change impact analysis, auto-route low-risk changes for expedited approval, and orchestrate multi-system change implementation. Predictive change management — forecasting which designs will need changes based on pattern analysis — is reliable.

Engineering changes are a real-time stream of structured change intelligence. Every change event — proposal, analysis, review, approval, implementation, verification — is captured and published as it happens. The system detects when changes are needed before engineers request them — component obsolescence, supplier failures, and field performance trends trigger proactive change proposals. The change record documents itself continuously.

Fully autonomous engineering change management. AI proposes, analyzes, routes, and monitors changes across the complete product lifecycle. The change process is a continuous intelligence stream, not a batch workflow.

Ceiling of the CMC framework for this dimension.