Glossary

Association rule mining

A data mining method that finds items, events, or conditions that frequently occur together in data.

Scrap, Rework and Cost of Poor Quality Reduction Quality, NCR, and Continuous Improvement

Association rule mining is a data analysis method used to discover patterns of co-occurrence in large datasets. It commonly identifies which items, events, attributes, or process conditions tend to appear together, and may also estimate how strongly one observed condition is associated with another.

In industrial and manufacturing settings, association rule mining is often applied to production, quality, maintenance, and supply chain data. Examples include finding defect types that commonly occur with certain machine states, material combinations that are often linked with scrap events, or process parameters that frequently appear alongside specific quality outcomes.

The output is usually expressed as rules in a form such as “if A occurs, B also tends to occur.” These rules describe statistical associations in historical data. They do not by themselves prove causation, physical mechanism, or process control limits.

What it includes and excludes

Includes pattern discovery across transactions, event logs, batch records, sensor summaries, or other structured datasets.
Includes measures such as support, confidence, and lift to describe how common or meaningful a rule may be.
Does not by itself explain why a pattern exists.
Does not replace root cause analysis, process validation, or formal statistical experimentation.

Operational meaning

Operationally, association rule mining may appear inside analytics platforms, MES-adjacent reporting, data lakes, or quality investigation workflows. Teams may use it to screen for recurring combinations of signals before deeper review, such as linking nonconformances to work centers, shifts, tooling, or incoming material attributes.

Common confusion

Association rule mining is often confused with correlation analysis, classification, or root cause analysis. Correlation usually measures a statistical relationship between variables, while association rules focus on recurring combinations or implication-style patterns. Classification predicts a target label. Root cause analysis investigates causal explanations. Association rule mining is primarily a pattern discovery technique.

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There is no single correct cadence. In aerospace, executives typically need a layered review rhythm: daily for critical escapes and major disruptions, weekly for plant-level trends and containment status, and monthly to quarterly for structural COPQ drivers and investment decisions. The right cadence depends on data quality, process maturity, and how quickly corrective actions can be validated.

What is the difference between rework and repair?

In regulated manufacturing, rework means bringing a nonconforming product back into specification using the original, approved process (or a pre-validated variant). Repair means making a nonconforming product usable by adding, patching, or modifying it in a way that does not fully restore it to the original design intent or specifications, and typically reduces or limits its allowable use. The distinction has implications for risk, documentation, validation, and customer and regulatory approvals.

Why are scrap decisions in MRO often harder than in OEM production?

Scrap decisions in MRO are usually harder because the part history, actual condition, repairability, and operational urgency are less predictable than in OEM production. The decision often depends on maintenance lineage, approved repair data, inspection evidence, parts availability, turnaround impact, and whether traceability is complete enough to support a defensible disposition.

What role does root cause rigor play in preventing recurring scrap on flight-critical components?

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.

Related Glossary

Sensitivity analysis

Sensitivity analysis examines how changes in input assumptions affect an output, model result, or operational outcome.

Rework impact

Rework impact is the effect that rework has on cost, schedule, capacity, quality, and traceability.