AI Pattern Detection
How AI augments standard SPC rules to detect emerging trends, shifts, and cyclical patterns before they trigger out-of-control signals.
What AI Adds Beyond Standard Rules
Western Electric and Nelson rules are binary — a pattern either triggers a rule or it doesn't. AI pattern detection works on a continuous scale, identifying emerging patterns before they cross rule thresholds. A gradual upward drift might take 20 subgroups to trigger Nelson Rule 5 (6 consecutive trending points) — AI can flag the drift at subgroup 10 with a confidence score, giving you a head start on investigation.
Detection Types
SigmaResolve detects five pattern types: Trends (gradual drift using rolling linear regression), Level shifts (sudden mean changes using change-point analysis), Cyclical patterns (periodic oscillation via autocorrelation), Mixture (bimodal distributions suggesting two process streams), and Stratification (unnaturally low variation suggesting data issues). Each detection includes the statistical method used, confidence score, and probable process causes.
Confidence Scores and Transparency
Every AI finding includes a High/Medium/Low confidence rating. High-confidence findings appear as primary alerts alongside rule violations. Low-confidence findings go to a separate 'Observations' section — visible but not alarming. The system displays the statistical method for each detection: 'Trend detected using linear regression on 20-subgroup rolling window, slope significance p < 0.002.' Quality engineers don't trust black boxes.
Manufacturing-Specific Cause Hypotheses
Unlike generic anomaly detection, SigmaResolve's AI provides manufacturing-relevant probable causes. A detected upward trend suggests: tool wear, fixture loosening, thermal expansion, material degradation. A sudden shift suggests: new material lot, operator change, machine adjustment, fixture replacement. Causes are drawn from a manufacturing knowledge base — not generic ML vocabulary.
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