Qqapp

SPC: statistical process control

Track process variation over time, not just the result of an inspection.

What problem it solves

Measurements must be kept in chronological order together with sampling information. Control charts help the team investigate unusual changes; control limits are not specification limits.

What we can configure in QAPP

The module is part of QAPP, not a separate product. Included features, rules, reports and integrations are confirmed in the project proposal and validated at acceptance. This presentation does not certify automatic compliance with any standard.

How to start

Send us a form without confidential data and describe who completes it, how often, and which decisions are based on it. Together, we will define a relevant example for the demonstration.

Methodological references: AIAG & VDA SPC Manual, 1st edition (July 2026) · NIST/SEMATECH — control charts. For measurement-system studies: AIAG MSA, 4th edition. QAPP uses these sources as methodological references and does not reproduce or replace licensed AIAG manuals or customer-specific requirements.

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QAPP STATISTICAL LAB

All analyses.
From data to decisions.

Statistical tools available in QAPP use the same application data set and provide charts, indicators and interpretations. Examples are inspired by process-control, data-collection, inspection, scrap and production scenarios.

APPLICATION EXAMPLE · PROCESS CONTROL CAPABILITY

Can the process maintain coating thickness within the limits?

Illustrative example: 50 coating-thickness measurements, LSL 60 μm and USL 100 μm. The visual shows the relationship between the distribution and specifications. The interactive tool calculates overall Pp/Ppk, Cp/Cpk when rational subgrouping is declared, and an optional Box–Cox model for positive non-normal data.

LSL60 μmTARGET80 μmUSL100 μmANALYSISPp / Ppk
i

Interpret in contextStability, sampling strategy and the distribution model should be checked before drawing a capability conclusion.

Test capability with up to 200 observations →
Normal curve (Gaussian)
LSL 60
TARGET 80
USL 100
5060708090100110 μm
SPC CHART SELECTION

Start from data type and sampling structure.

Variable · n = 1

One measurement at each time point, with no rational subgroup.

I-MR →

Variable · subgroup 2–10

Measurements close in time that represent short-term variation.

X̄–R →

Attribute · conform / nonconforming

Proportion nonconforming by lot, including different lot sizes.

P / Laney P′ →

Capability

After assessing stability and choosing an appropriate variation model.

Cp/Cpk & Pp/Ppk →

AIAG & VDA SPC 2026 expands control-chart selection guidance around process characteristics, data type and monitoring purpose. This selector is guidance only and does not replace the licensed manual or customer requirements.

Open a tool to load up to 200 observations and calculate results. Each page states the version implemented in the demo and its data requirements. The catalogue brings together SPC, hypothesis tests, MSA and DOE. Curves shown for 1-Sample and 2-Sample illustrate normal distributions; they do not represent the t-statistic distribution and do not determine test significance by themselves.

18 statistical toolsDemos with up to 200 observations
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03CapabilityQUALITY
Pp / PpkDISTRIBUTION + SPECIFICATIONS

Overall Pp/Ppk, Cp/Cpk from rational subgroups, X̄–R screening and a Box–Cox path for positive data. Stability and distribution choice remain interpretation conditions.

QAPP EXAMPLECharacteristic measured between 60–100 μm Load data and calculate →
07RegressionMODELLING
R² 91.4%

Simple linear regression with ANOVA, confidence intervals, prediction, leverage, Cook D and full residual diagnostics.

QAPP EXAMPLETemperature vs measured result Load data and calculate →
11I-MRSPC

Track individual observations and variation between consecutive measurements.

QAPP EXAMPLEProcess temperature over time Load data and calculate →
141-SampleHYPOTHESIS TEST
Target μ₀Illustrative distribution

Compare a sample mean with a target and calculate the confidence interval.

QAPP EXAMPLEIs the mean thickness equal to 80 μm? Load data and calculate →
152-SampleCOMPARISON
ABIllustrative distributions

Compare two means, variations and the confidence interval for the population difference.

QAPP EXAMPLESupplier A vs Supplier B Load data and calculate →
16MSA — complete suiteMEASUREMENT
8.7%
  • Equipment 4.1%
  • Operator 3.2%
  • Interaction 1.4%

Gage R&R, Bias, Linearity, Stability and Attribute Agreement, with charts, sources and methodological interpretation.

QAPP EXAMPLEThickness measurement system Open MSA suite →
17DOEEXPERIMENTS
A · TemperatureB · SpeedAB · Interaction

2² factorial DOE with effects, interaction, ANOVA, half-normal screening, residuals and centre points for curvature screening.

QAPP EXAMPLETemperature and speed optimisation Load data and calculate →
ANALYSES CONNECTED DIRECTLY TO THE PROCESS

The same methods, applied in context.

QAPP Analytics brings statistical methods together, while collection modules provide measurement context. Required analyses and how they connect to data are defined for your implementation.