SPC Automation & Quality Engineering Hub

Production-ready Python and pipeline patterns for automating Statistical Process Control — control charts, out-of-control rule detection, MES/SCADA alignment, and compliance reporting.

SPC Fundamentals & Control Chart Taxonomy Choose the right chart, derive AIAG-compliant limits, validate stability, and gate capability indices. X-bar/R, X-bar/S, I-MR, p/np/c/u, and Cp/Cpk/Pp/Ppk in production Python. Automated Control Chart Generation & Calculation Vectorized calculation engines, dynamic Plotly rendering, rolling-window recalibration, and orchestration patterns that keep limits deterministic, auditable, and fast. Manufacturing Data Ingestion & Preprocessing MES/SCADA connectors, time-series alignment, missing-value handling, outlier filtering, and batch validation — the deterministic foundation under every reliable SPC pipeline. SPC Alerting & OCAP Response Turn out-of-control signals into action: route alerts to ticketing and MES, codify Out-of-Control Action Plans, and drive Slack/Teams notifications without alarm storms.

What this site covers

The SPC Automation & Quality Engineering Hub is built for quality engineers, manufacturing operations teams, Six Sigma practitioners, and Python data analysts who need to move beyond spreadsheets. Every article ships with production-ready code, AIAG- and NIST-aligned formulas, and the edge-case handling that breaks real pipelines: variable subgroup sizes, sensor dropouts, time-series misalignment, and false-alarm storms.

Articles cover the full pipeline lifecycle: ingestion from MES and SCADA systems, preprocessing (alignment, imputation, outlier filtering, batch validation), chart generation for X-bar/R, X-bar/S, I-MR, p/np/c/u, EWMA/CUSUM, and rolling-window recalibration, plus interactive Plotly rendering, Nelson and Western Electric rule detection, and the alerting and Out-of-Control Action Plan layer that turns a signal into a documented response. Every reference is reproducible and grounded in standards — ASTM E2587, AIAG SPC, ISO 9001:2015, and IATF 16949.

Start here

Four concrete, worked walkthroughs that anchor the rest of the library.

Calculate X-bar/R control limits in Python The canonical worked example: unbiasing constants, subgroup statistics, and AIAG-compliant limits from raw measurements. Cpk vs. Ppk for short production runs When within-subgroup and overall sigma diverge — and how to report capability honestly on limited data. Implement Nelson rules in Python Vectorized detection of all eight Nelson rules against frozen control limits — the special-cause layer every automated chart needs. Build an OCAP decision table in Python Map each out-of-control rule to a documented reaction, owner, and escalation tier so a signal becomes a repeatable action.

Browse by section

Four sections covering the full SPC automation pipeline, from raw telemetry to audit-ready charts and out-of-control response.

Latest & recently updated

Freshly revised deep-dives from across every section.