Quality KPIs

Estimate AI scrap reduction potential with a bounded business case, not a promise. Start from actual scrap and rework history, isolate the loss modes AI could influence, then apply scenario ranges based on data quality, process stability, intervention timing, and adoption. In regulated plants, integration, validation, and change control often limit realized gains more than model accuracy.

Rigor in root cause analysis is a primary control against recurring scrap on flight‑critical components, but only when it is systematic, evidence-based, and tied to controlled implementation and verification of corrective actions. In regulated, mixed-system environments, weak problem definition, poor data, and bypassing change control are common failure modes that allow the same defects to return.

In aerospace manufacturing, “scrap,” “rework,” “repair,” and “concession” describe very different quality dispositions with different technical, regulatory, and traceability implications. Misusing the terms can distort cost of poor quality, confuse airworthiness records, and create audit risk, especially in mixed legacy/MES environments.

An MES project team focused on waste needs cross‑functional roles from operations, engineering, quality, IT/OT, and finance, plus frontline representation. The exact mix depends on plant maturity and system landscape, but you need people who own the process, the data, and the systems, as well as those accountable for cost, compliance, and change control.