Statistical Quality Analysis Tools with AI Interpretation

Process capability index (Cpk) measures how well a manufacturing process stays within specification limits. A Cpk of 1.33 means the process is centered with adequate margin; 1.67 or higher meets IATF 16949 and AS9100 requirements for production. QualityEngineer.ai calculates both short-term Cpk and long-term Ppk using bias-corrected sigma, and the AI explains whether your process is acceptable, conditional, or needs improvement - in plain language.

Gauge R&R (Gauge Repeatability and Reproducibility) measures whether your measurement system is capable of detecting the variation it needs to measure. It separates variation into repeatability (same operator, same part) and reproducibility (different operators). You need a Gauge R&R study for any measurement system used in capability studies or SPC - it is required by IATF 16949 and AIAG MSA 4th Edition. The platform performs ANOVA-based Gauge R&R and reports %GRR, number of distinct categories, and adequacy assessment.

The platform supports X-bar/R charts with automated Western Electric rule detection. It identifies out-of-control conditions including: points beyond 3-sigma control limits, runs of 9+ points on the same side of center, trends of 6+ consecutive increasing or decreasing points, and alternating patterns. AI interprets each violation and explains its practical significance - whether it indicates a process shift, trend, or mixture.

After each calculation, the AI analyzes results and provides: a plain-language explanation of what the numbers mean, an assessment against industry thresholds (like IATF 16949 Cpk requirements), specific recommendations for improvement if needed, and comparisons to your historical data for the same characteristic. This turns statistical output into actionable quality decisions - even for engineers who are not statistics experts.

Beyond Cpk/Ppk, Gauge R&R, and SPC, the platform includes: ANOVA (one-way and multi-factor analysis of variance), DOE (full and fractional factorial designs with interaction analysis), hypothesis testing (t-tests, chi-square with confidence intervals), regression analysis (linear and multiple regression with p-values), Pareto analysis (80/20 rule for defect categorization), acceptance sampling (AQL-based plans per ANSI/ASQ Z1.4), and Six Sigma metrics (DPMO, sigma level, yield calculations).