Glossary

Multivariate analysis

A set of statistical methods used to analyze multiple variables at the same time and their relationships.

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

Multivariate analysis commonly refers to a group of statistical methods used to examine more than one variable at the same time. In manufacturing and regulated operations, it is used to understand how process inputs, material attributes, equipment conditions, and quality results vary together rather than looking at each factor in isolation.

The term includes techniques that model relationships, patterns, correlation structures, and sources of variation across many measurements. Depending on the method, the goal may be to explain variation, detect abnormal conditions, classify outcomes, reduce data dimensionality, or predict a result. It does not simply mean making several separate single-variable analyses in parallel.

How it is used in operations

In industrial settings, multivariate analysis often appears in process monitoring, quality investigations, yield analysis, and continuous improvement work. Examples include evaluating how temperature, pressure, mix time, and raw material properties relate to assay, defect rate, or cycle time, or using many sensor signals together to identify a developing process drift.

It may be applied within MES, historian, LIMS, QMS, or analytics platforms, or performed offline using statistical software. The output can support investigation and decision-making, but the term itself refers to the analytical approach, not to a specific software product, dashboard, or compliance record.

What it includes and excludes

Includes analysis of multiple variables simultaneously.
Includes methods such as principal component analysis, cluster analysis, discriminant analysis, factor analysis, and multivariate regression, depending on context.
May use historical batch, process, laboratory, or equipment data.
Does not automatically imply machine learning, though some machine learning methods are multivariate.
Does not mean any dataset with many columns unless a method is actually evaluating relationships among those variables.

Common confusion

Multivariate analysis is often confused with multiple regression and multivariable analysis. Multiple regression is one specific statistical technique. Multivariable analysis often means one outcome modeled using several predictors, while multivariate analysis more strictly means analyzing multiple variables or outcomes together. In practice, different disciplines sometimes use these terms less precisely, so the intended method should be checked.

Useful scrap analytics do not require perfect operator data, but they do need error levels that are understood, stable over time, and low enough not to swamp the signals you care about. In most regulated plants, you should target accuracy better than ±5–10% at the shift or line level and focus on consistency, traceability, and data validation rather than unattainable precision.

What is a realistic COPQ percentage for aerospace manufacturing programs?

There is no single aerospace COPQ benchmark that holds across all programs. A realistic range is often in the low single digits for mature, stable production and materially higher for new product introduction, complex assemblies, or weak supplier performance. The result depends heavily on what costs are counted, data quality, and how much hidden rework, disruption, and concession activity is actually traced.

How should aerospace leaders measure and report cost of poor quality at the executive level?

Aerospace leaders should measure cost of poor quality (COPQ) using a small, clearly defined set of categories that tie directly to P&L and program risk, with traceable data sources and stable definitions. Executive reporting should highlight trends, main drivers, and structural constraints, not just a single headline number. COPQ must align with finance, program, and quality data and be governed through change control.

Which scrap and nonconformance metrics belong on the CEO’s dashboard?

A CEO dashboard should not show every quality KPI. It should show a small set of enterprise-level scrap and nonconformance measures tied to financial exposure, delivery risk, recurrence, containment speed, and trend by program, site, and supplier. The exact mix depends on costing discipline, NCR workflow maturity, and how well ERP, MES, QMS, and supplier data are connected.

Related Glossary

Material Usage Variance

Material usage variance is the difference between the standard quantity of material allowed for actual output and the actual quantity used, valued at standard cost.